The human brain is among the most complicated organs and one of nature’s most amazing creations. The brain’s capacity is considered limitless; there isn’t a thing it can’t remember. Although many often don’t think about it, the processes that happen in the mind are fascinating.

As technology evolved over the years, scientists figured out a way to make machines think like humans, and this process is called machine learning. Like cars need fuel to operate, machines need data and algorithms. With the application of adequate techniques, machines can learn from this data and even improve their accuracy as time passes.

Two basic machine learning approaches are supervised and unsupervised learning. You can already assume the biggest difference between them based on their names. With supervised learning, you have a “teacher” who shows the machine how to analyze specific data. Unsupervised learning is completely independent, meaning there are no teachers or guides.

This article will talk more about supervised and unsupervised learning, outline their differences, and introduce examples.

Supervised Learning

Imagine a teacher trying to teach their young students to write the letter “A.” The teacher will first set an example by writing the letter on the board, and the students will follow. After some time, the students will be able to write the letter without assistance.

Supervised machine learning is very similar to this situation. In this case, you (the teacher) train the machine using labeled data. Such data already contains the right answer to a particular situation. The machine then uses this training data to learn a pattern and applies it to all new datasets.

Note that the role of a teacher is essential. The provided labeled datasets are the foundation of the machine’s learning process. If you withhold these datasets or don’t label them correctly, you won’t get any (relevant) results.

Supervised learning is complex, but we can understand it through a simple real-life example.

Suppose you have a basket filled with red apples, strawberries, and pears and want to train a machine to identify these fruits. You’ll teach the machine the basic characteristics of each fruit found in the basket, focusing on the color, size, shape, and other relevant features. If you introduce a “new” strawberry to the basket, the machine will analyze its appearance and label it as “strawberry” based on the knowledge it acquired during training.

Types of Supervised Learning

You can divide supervised learning into two types:

• Classification – You can train machines to classify data into categories based on different characteristics. The fruit basket example is the perfect representation of this scenario.
• Regression – You can train machines to use specific data to make future predictions and identify trends.

Supervised Learning Algorithms

Supervised learning uses different algorithms to function:

• Linear regression – It identifies a linear relationship between an independent and a dependent variable.
• Logistic regression – It typically predicts binary outcomes (yes/no, true/false) and is important for classification purposes.
• Support vector machines – They use high-dimensional features to map data that can’t be separated by a linear line.
• Decision trees – They predict outcomes and classify data using tree-like structures.
• Random forests – They analyze several decision trees to come up with a unique prediction/result.
• Neural networks – They process data in a unique way, very similar to the human brain.

Supervised Learning: Examples and Applications

There’s no better way to understand supervised learning than through examples. Let’s dive into the real estate world.

Suppose you’re a real estate agent and need to predict the prices of different properties in your city. The first thing you’ll need to do is feed your machine existing data about available houses in the area. Factors like square footage, amenities, a backyard/garden, the number of rooms, and available furniture, are all relevant factors. Then, you need to “teach” the machine the prices of different properties. The more, the better.

A large dataset will help your machine pick up on seemingly minor but significant trends affecting the price. Once your machine processes this data and you introduce a new property to it, it will be able to cross-reference its features with the existing database and come up with an accurate price prediction.

The applications of supervised learning are vast. Here are the most popular ones:

• Sales – Predicting customers’ purchasing behavior and trends
• Finance – Predicting stock market fluctuations, price changes, expenses, etc.
• Healthcare – Predicting risk of diseases and infections, surgery outcomes, necessary medications, etc.
• Weather forecasts – Predicting temperature, humidity, atmospheric pressure, wind speed, etc.
• Face recognition – Identifying people in photos

Unsupervised Learning

Imagine a family with a baby and a dog. The dog lives inside the house, so the baby is used to it and expresses positive emotions toward it. A month later, a friend comes to visit, and they bring their dog. The baby hasn’t seen the dog before, but she starts smiling as soon as she sees it.

Why?

Because the baby was able to draw her own conclusions based on the new dog’s appearance: two ears, tail, nose, tongue sticking out, and maybe even a specific noise (barking). Since the baby has positive emotions toward the house dog, she also reacts positively to a new, unknown dog.

This is a real-life example of unsupervised learning. Nobody taught the baby about dogs, but she still managed to make accurate conclusions.

With supervised machine learning, you have a teacher who trains the machine. This isn’t the case with unsupervised learning. Here, it’s necessary to give the machine freedom to explore and discover information. Therefore, this machine learning approach deals with unlabeled data.

Types of Unsupervised Learning

There are two types of unsupervised learning:

• Clustering – Grouping uncategorized data based on their common features.
• Dimensionality reduction – Reducing the number of variables, features, or columns to capture the essence of the available information.

Unsupervised Learning Algorithms

Unsupervised learning relies on these algorithms:

• K-means clustering – It identifies similar features and groups them into clusters.
• Hierarchical clustering – It identifies similarities and differences between data and groups them hierarchically.
• Principal component analysis (PCA) – It reduces data dimensionality while boosting interpretability.
• Independent component analysis (ICA) – It separates independent sources from mixed signals.
• T-distributed stochastic neighbor embedding (t-SNE) – It explores and visualizes high-dimensional data.

Unsupervised Learning: Examples and Applications

Let’s see how unsupervised learning is used in customer segmentation.

Suppose you work for a company that wants to learn more about its customers to build more effective marketing campaigns and sell more products. You can use unsupervised machine learning to analyze characteristics like gender, age, education, location, and income. This approach is able to discover who purchases your products more often. After getting the results, you can come up with strategies to push the product more.

Unsupervised learning is often used in the same industries as supervised learning but with different purposes. For example, both approaches are used in sales. Supervised learning can accurately predict prices relying on past data. On the other hand, unsupervised learning analyzes the customers’ behaviors. The combination of the two approaches results in a quality marketing strategy that can attract more buyers and boost sales.

Another example is traffic. Supervised learning can provide an ETA to a destination, while unsupervised learning digs a bit deeper and often looks at the bigger picture. It can analyze a specific area to pinpoint accident-prone locations.

Differences Between Supervised and Unsupervised Learning

These are the crucial differences between the two machine learning approaches:

• Data labeling – Supervised learning uses labeled datasets, while unsupervised learning uses unlabeled, “raw” data. In other words, the former requires training, while the latter works independently to discover information.
• Algorithm complexity – Unsupervised learning requires more complex algorithms and powerful tools that can handle vast amounts of data. This is both a drawback and an advantage. Since it operates on complex algorithms, it’s capable of handling larger, more complicated datasets, which isn’t a characteristic of supervised learning.
• Use cases and applications – The two approaches can be used in the same industries but with different purposes. For example, supervised learning is used in predicting prices, while unsupervised learning is used in detecting customers’ behavior or anomalies.
• Evaluation metrics – Supervised learning tends to be more accurate (at least for now). Machines still require a bit of our input to display accurate results.

Choose Wisely

Do you need to teach your machine different data, or can you trust it to handle the analysis on its own? Think about what you want to analyze. Unsupervised and supervised learning may sound similar, but they have different uses. Choosing an inadequate approach leads to unreliable, irrelevant results.

Supervised learning is still more popular than unsupervised learning because it offers more accurate results. However, this approach can’t handle larger, complex datasets and requires human intervention, which isn’t the case with unsupervised learning. Therefore, we may see a rise in the popularity of the unsupervised approach, especially as the technology evolves and enables more accuracy.

When you first get into modern computing, one of the terms that comes up most frequently is relational databases. These are clusters that are organized in such a way that they effortlessly find links between connected data points.

Relational databases are convenient, but what happens when you deal with vast amounts of information? You need something to act as your North Star, guiding you through the network and allowing you to stay on top of the data.

That something is an RDBMS. According to Google, RDBMS stands for a relational database management system – software that sets up and manages relational databases. In its full form, it’s been the light at the end of the tunnel for thousands of companies due to its accuracy, security, and ease of use.

The definition and importance of RDBMSs are the tip of the iceberg when it comes to these systems. This introduction to RDBMS will delve a bit deeper by taking a closer look at the concept of RDBMS, the history of this technology, use cases, and the most common examples.

History of RDBMS

The concept of RDBMS might be shrouded in mystery for some. Thus, several questions may come up when discussing the notion, including one as basic as “What is RDBMS?”

Knowing the RDBMS definition is a great starting point on your journey to understanding this concept. But let’s take a few steps back and delve into the history of this system.

Origins of the Relational Model

What if we told you that the RDBMS concepts are older than the internet? It may sound surprising, but it’s true.

The concept of RDBMS was developed by Edgar F. Codd 43 years ago. He aimed to propose a more efficient way to store information, a method that would consume drastically less memory than anything at the time. His model was groundbreaking, to say the least.

E.F. Codd’s Paper on Relational Model

Codd laid down his proposal in a 1970s paper called “A Relational Model of Data for Large Shared Data Banks.” He advocated a database solution comprised of intertwined tables. These tables enabled the user to keep their information compact, lowering the amount of disk space necessary for storage (which was scarce at the time).

The rest is history. The public welcomed Codd’s model with open arms since it optimized storage requirements and allowed people to answer practically any question using his principle.

Development of SQL

Codd’s research paved the way for relational database management systems, the most famous of which is SQL. This programming language was also developed in the ‘70s and was originally named SEQUEL (Structured English Query Language). It was quickly implemented across the computing industry and grew more powerful as the years went by.

Evolution of RDBMS Software

The evolution of RDBMS software has been fascinating.

Early RDBMS Software

The original RDBMS software was powerful, but it wasn’t a cure-all. It was a match made in heaven for users dealing with structured data, allowing them to organize it with minimal effort. However, pictures, music, and other forms of unstructured information were largely incompatible with this model.

Modern RDBMS Software

Today’s RDBMS solutions have come a long way from their humble beginnings. A modern relational DBMS can process different forms of information with ease. Programs like MySQL are versatile, adaptable, and easy to set up, helping database professionals spearhead the development of practically any application.

Key Concepts in RDBMS

Here’s another request you may have for an expert in RDBMS – explain the most significant relational database concepts. If that’s your question, your request has been granted. Coming up is an overview of RDBMS concepts that explain RDBMS in simple terms.

Tables and Relations

Tables and relations are the bread and butter of all relational database management systems. They sound straightforward, but they’re much different from, say, elements you come across in Microsoft Excel.

Definition of Tables

Tables are where data is stored in an RDBMS. They’re comprised of rows and columns for easier organization.

Definition of Relations

Relations are the links between tables. There can be several types of relations, such as one-to-one connections. This form means a data point from one table only matches one data point from another table.

Primary and Foreign Keys

No discussion about RDBMS solutions is complete without primary and foreign keys.

Definition of Primary Keys

A primary key is the unique element of each table that defines the table’s rows. The number of primary keys in a table is limited to one.

Definition of Foreign Keys

Foreign keys are used to form an inextricable bond between tables. They always refer to the primary key of another table.

Normalization

Much of database management is akin to separating wheat from the chaff. One of the processes that allow you to do so is normalization.

Purpose of Normalization

Normalization is about restoring (or creating) order in a database. It’s the procedure of eradicating unnecessary data for the purpose of cleaner tables and smoother management.

Normal Forms

Every action has its reaction. For example, the reaction of normalization is normal forms. These are forms of data that are free from redundant or duplicate information, making them easily accessible.

Popular RDBMS Software

This article has dissected basic relational database concepts, the RDBMS meaning, and RDBMS full form. To further shed light on the technology, take a look at the crème de la crème of RDBMS platforms.

Oracle Database

If you want to make headway in the database management industry, Oracle Database can be one of your best friends.

Overview of Oracle Database

Oracle Database is the most famous RDBMS around. The very database of this network is called Oracle, and the software comes in five different versions. Each rendition has a specific set of features and benefits, but some perks hold true for each one.

Key Features and Benefits

• Highly secure – Oracle employs top-grade security measures.
• Scalable – The system supports company growth with adaptable features.
• Available – You can tap into the architecture whenever necessary for seamless adjustments.

Microsoft SQL Server

Let’s see what another powerhouse – Microsoft SQL Server – brings to the table.

Overview of Microsoft SQL Server

Microsoft SQL Server is a reliable RDBMS with admirable capabilities. Like Oracle, it’s available in a range of editions to target different groups, including personal and enterprise users.

Key Features and Benefits

• Fast – Few systems rival the speed of Microsoft SQL Server.
• Versatile – The network supports on-premise and cloud applications.
• Affordable – You won’t burn a hole in your pocket if you buy the standard version.

MySQL

You can take your business to new heights with MySQL. The following section will explore what makes this RDBMS a go-to pick for Uber, Slack, and many other companies.

Overview of MySQL

MySQL is another robust RDBMS that enables fast data retrieval. It’s an open-source solution, making it less complex than some other platforms.

Key Features and Benefits

• Quick – Efficient memory use speeds up the MySQL environment.
• Secure – Bulletproof password systems safeguard against hacks.
• Scalable – You can use MySQL both for small and large data sets.

PostgreSQL

Last but not least, PostgreSQL is a worthy contender for the best RDBMS on the market.

Overview of PostgreSQL

If you need a long-running RDBMS, you can’t go wrong with PostgreSQL. It’s an open-source solution that’s received more than two decades’ worth of refinement.

Key Features and Benefits

• Nested transactions – These elements deliver higher concurrency control.
• Anti-hack environment – Advanced locking features keep cybercriminals at bay.
• Table inheritance – This feature makes the network more consistent.

RDBMS Use Cases

Now we get to what might be the crux of the RDBMS discussion: Where can you implement these convenient solutions?

Data Storage and Retrieval

• Storing large amounts of structured data – Use an RDBMS to keep practically unlimited structured data.
• Efficient data retrieval – Retrieve data in a split second with an RDBMS.

Data Analysis and Reporting

• Analyzing data for trends and patterns – Discover customer behavior trends with a robust RDBMS.
• Generating reports for decision-making – Facilitate smart decision-making with RDBMS-generated reports.

Application Development

• Backend for web and mobile applications – Develop a steady web and mobile backend architecture with your RDBMS.
• Integration with other software and services – Combine an RDBMS with other programs to elevate its functionality.

RDBMS vs. NoSQL Database

Many alternatives to RDBMS have sprung up, including NoSQL databases. But what makes these two systems different?

Overview of NoSQL Databases

A NoSQL database is the stark opposite of RDBMS solutions. It takes a non-relational approach, which is deemed more efficient by many.

Key Differences Between RDBMS and NoSQL Databases

• Data model – RDBMSs store structured data, whereas NoSQL databases store unstructured information.
• Scalability – NoSQL is more scalable because it doesn’t require a fixed schema (relation-based model).
• Consistency – RDBMSs achieve consistency through rules, while NoSQL models feature eventual consistency.

Choosing the Right Database for Your Needs

Keep these guidelines in mind when selecting your database platform:

• Use an RDBMS for centralized apps and NoSQL for decentralized solutions.
• Use an RDBMS for structured data and NoSQL for unstructured data.
• Use an RDBMS for moderate data activity and NoSQL for high data activity.

Exploring the Vast Utility of RDBMS

If you’re looking for a descriptive answer to the “what is relational database management system question,” here it is – it is the cornerstone of database management for countless enterprises. It’s ideal for structured data projects and gives the user the reins of data management. Plus, it’s as secure as it gets.

The future looks even more promising. Database professionals are expected to rely more on blockchain technology and cloud storage to elevate the efficacy of RDBMS.

An ER diagram in DBMS (database management systems) is a lot like a storyboard for an animated TV show – it’s a collection of diagrams that show how everything fits together. Where a storyboard demonstrates the flow from one scene to the next, an ER diagram highlights the components of your databases and the relationships they share.

Understanding the ER model in DBMS is the first step to getting to grips with basic database software (like Microsoft Access) and more complex database-centric programming languages, such as SQL. This article explores ER diagrams in detail.

ER Model in DBMS

An ER diagram in DBMS is a tangible representation of the tables in a database, the relationships between each of those tables, and the attributes of each table. These diagrams feature three core components:

• Entities – Represented by rectangles in the diagram, entities are objects or concepts used throughout your database.
• Attributes – These are the properties that each entity possesses. ER diagrams use ellipses to represent attributes, with the attributes themselves tending to be the fields in a table. For example, an entity for students in a school’s internal database may have attributes for student names, birthdays, and unique identification numbers.
• Relationships – No entity in an ER diagram is an island, as each is linked to at least one other. These relationships can take multiple forms, with said relationships dictating the flow of information through the database.

Mapping out your proposed database using the ER model is essential because it gives you a visual representation of how the database works before you start coding or creating. Think of it like the blueprint you’d use to build a house, with that blueprint telling you where you need to lay every brick and fit every door.

Entities in DBMS

An Entity in DBMS tends to represent a real-life thing (like the students mentioned previously) that you can identify with certain types of data. Each entity is distinguishable from the others in your database, meaning you won’t have multiple entities listing student details.

Entities come in two flavors:

• Tangible Entities – These are physical things that exist in the real world, such as a person, vehicle, or building.
• Intangible Entities – If you can see and feel an entity, it’s intangible. Bank accounts are good examples. We know they exist (and have data attributed to them) but we can’t physically touch them.

There are also different entity strengths to consider:

• Strong Entities – A strong entity is represented using a rectangle and will have at least one key attribute attached to it that allows you to identify it uniquely. In the student example we’ve already shared, a student’s ID number could be a unique identifier, creating a key attribute that leads to the “Student” entity being strong.
• Weak Entities – Weak entities have no unique identifiers, meaning you can’t use them alone. Represented using double-outlined rectangles, these entities rely on the existence of strong entities to exist themselves. Think of it like the relationship between parent and child. A child can’t exist without a parent, in the same way that a weak entity can’t exist without a strong entity.

Once you’ve established what your entities are, you’ll gather each specific type of entity into an entity set. This set is like a table that contains the data for each entity in a uniform manner. Returning to the student example, any entity that has a student ID number, name, and birthdate, may be placed into an overarching “Student” entity set. They’re basically containers for specific entity types.

Attributes in DBMS

Every entity you establish has attributes attached to it, as you’ve already seen with the student example used previously. These attributes offer details about various aspects of the entity and come in four types:

• Simple Attributes – A simple attribute is any attribute that you can’t break down into further categories. A student ID number is a good example, as this isn’t something you can expand upon.
• Composite Attributes – Composite attributes are those that may have other attributes attached to them. If “Name” is one of your attributes, its composites could be “First Name,” “Surname,” “Maiden Name,” and “Nickname.”
• Derived Attributes – If you can derive an attribute from another attribute, it falls into this category. For instance, you can use a student’s date of birth to derive their age and grade level. These attributes have dotted ellipses surrounding them.
• Multi-valued Attributes – Represented by dual-ellipses, these attributes cover anything that can have multiple values. Phone numbers are good examples, as people can have several cell phone or landline numbers.

Attributes are important when creating an ER model in DBMS because they show you what types of data you’ll use to populate your entities.

Relationships in DBMS

As your database becomes more complex, you’ll create several entities and entity sets, with each having relationships with others. You represent these relationships using lines, creating a network of entities with line-based descriptions telling you how information flows between them.

There are three types of relationships for an ER diagram in DBMS:

• One-to-One Relationships – You’ll use this relationship when one entity can only have one of another entity. For example, if a school issues ID cards to its students, it’s likely that each student can only have one card. Thus, you have a one-to-one relationship between the student and ID card entities.
• One-to-Many Relationships – This relationship type is for when one entity can have several of another entity, but the relationship doesn’t work in reverse. Bank accounts are a good example, as a customer can have several bank accounts, but each account is only accessible to one customer.
• Many-to-Many Relationships – You use these relationships to denote when two entities can have several of each other. Returning to the student example, a student will have multiple classes, with each class containing several students, creating a many-to-many relationship.

These relationships are further broken down into “relationship sets,” which bring together all of the entities that participate in the same type of relationship. These sets have three varieties:

• Unary – Only one entity participates in the relationship.
• Binary – Two entities are in the relationship, such as the student and course example mentioned earlier.
• n-ary – Multiple entities participate in the relationship, with “n” being the number of entities.

Your ER diagram in DBMS needs relationships to show how each entity set relates to (and interacts with) the others in your diagram.

ER Diagram Notations

You’ll use various forms of notation to denote the entities, attributes, relationships, and the cardinality of those relationships in your ER diagram.

Entity Notations

Entities are denoted using rectangles around a word or phrase, with a solid rectangle meaning a strong entity and a double-outlined rectangle denoting a weak entity.

Attribute Notations

Ellipses are the shapes of choice for attributes, with the following uses for each attribute type:

• Simple and Composite Attribute – Solid line ellipses
• Derived Attribute – Dotted line ellipses
• Multi-Valued Attribute – Double-lined ellipses

Relationship Notations

Relationship notation uses diamonds, with a solid line diamond depicting a relationship between two attributes. You may also find double-lined diamonds, which signify the relationship between a weak entity and the strong entity that owns it.

Cardinality and Modality Notations

These lines show you the maximum times an instance in one entity set can relate to the instances of another set, making them crucial for denoting the relationships inside your database.

The endpoint of the line tells you everything you need to know about cardinality and ordinality. For example, a line that ends with three lines (two going diagonally) signifies a “many” cardinality, while a line that concludes with a small vertical line signifies a “one” cardinality. Modality comes into play if there’s a minimum number of instances for an entity type. For example, a person can have many phone numbers but must have at least one.

Steps to Create an ER Diagram in DBMS

With the various notations for an ER diagram in DBMS explained, you can follow these steps to draw your own diagram:

• Identify Entities – Every tangible and intangible object that relates to your database is an entity that you need to identify and define.
• Identify Attributes – Each entity has a set of attributes (students have names, ID numbers, birthdates, etc.) that you must define.
• Identify Relationships – Ask yourself how each entity set fits together to identify the relationships that exist between them.
• Assign Cardinality and Modality – If you have an instance from Entity A, how many instances does it relate to in Entity B? Is there a minimum to consider? Assign cardinalities and modalities to offer the answers.
• Finalize Your Diagram – Take a final pass over the diagram to ensure all required entities are present, they have the appropriate attributes, and that all relationships are defined.

Examples of ER Diagrams in DBMS

Once you understand the basics of the ER model in DBMS, you’ll see how they can apply to multiple scenarios:

• University Databases – A university database will have entities such as “Student,” “Teacher,” “Course,” and “Class.” Attributes depend on the entity, with the people-based entities having attributes including names, dates of birth, and ID numbers. Relationships vary (i.e., a student may only have one teacher but a single teacher may have several students).
• Hospital Management Databases – Entities for this type of database include people (“Patients,” “Doctors,” and “Nurses”), as well as other tangibles, such as different hospital buildings and inventory. These databases can get very complex, with multiple relationships linking the various people involved to different buildings, treatment areas, and inventory.
• E-Commerce Databases – People play an important role in the entities for e-commerce sites, too, because every site needs a list of customers. Those customers have payment details and order histories, which are potential entities or attributes. Product lists and available inventory are also factors.

Master the ER Model in DBMS

An ER diagram in DBMS can look like a complicated mass of shapes and lines at first, making them feel impenetrable to those new to databases. But once you get to grips with what each type of shape and line represents, they become crucial tools to help you outline your databases before you start developing them.

Application of what you’ve learned is the key to success with ER diagrams (and any other topic), so take what you’ve learned here and start experimenting. Consider real-world scenarios (such as those introduced above) and draw diagrams based on the entities you believe apply to those scenarios. Build up from there to figure out the attributes and relationships between entity sets and you’re well on your way to a good ER diagram.

The larger your database, the higher the possibility of data repetition and inaccuracies that compromise the results you pull from the database. Normalization in DBMS exists to counteract those problems by helping you to create more uniform databases in which redundancies are less likely to occur.

Mastering normalization is a key skill in DBMS for the simple fact that an error-strewn database is of no use to an organization. For example, a retailer that has to deal with a database that has multiple entries for phone numbers and email addresses is a retailer that can’t see as effectively as one that has a simple route to the customer. Let’s look at normalization in DBMS and how it helps you to create a more organized database.

The Concept of Normalization

Grab a pack of playing cards and throw them onto the floor. Now, pick up the “Jack of Hearts.” It’s a tough task because the cards are strewn all over the place. Some are facing down and there’s no rhyme, reason, or pattern to how the cards lie, meaning you’re going to have to check every card individually to find the one you want.

That little experiment shows you how critical organization is, even with a small set of “data.” It also highlights the importance of normalization in DBMS. Through normalization, you implement organizational controls using a set of principles designed to achieve the following:

• Eliminate redundancy – Lower (or eliminate) occurrences of data repeating across different tables, or inside individual tables, in your DBMS.
• Minimize data anomalies – Better organization makes it easier to spot datasets that don’t fit the “norm,” meaning fewer anomalies.
• Improve data integrity – More accurate data comes from normalization controls. Database users can feel more confident in their results because they know that the controls ensure integrity.

The Process of Normalization

If normalization in DBMS is all about organization, it stands to reason that they would be a set process to follow when normalizing your tables and database:

1. Decompose your tables – Break every table down into its various parts, which may lead to you creating several tables out of one. Through decomposition, you separate different datasets, eliminate inconsistencies, and set the stage for creating relationships and dependencies between tables.
2. Identify functional dependencies – An attribute in one table may be dependent on another to exist. For example, a “Customer ID” number in a retailer’s “Customer” table is functionally dependent on the “Customer Name” field because the ID can’t exist without the customer. Identifying these types of dependencies ensures you don’t end up with empty records (such as a record with a “Customer ID” and no customer attached to it).
3. Apply normalization rules – Once you’re broken down your table and identified the functional dependencies, you apply relevant normalization rules. You’ll use Normal Forms to do this, with the six highlighted below each having its own rules, structures, and use cases.

Normal Forms in DBMS

There isn’t a “single” way to achieve normalization in DBMS because every database (and the tables it contains) is different. Instead, there are six normal forms you may use, with each having its own rules that you need to understand to figure out which to apply.

First Normal Form (1NF)

If a relation can’t contain multiple values, it’s in 1NF. In other words, each attribute in the table can only contain a single (called “atomic”) value.

Example

If a retailer wants to store the details of its customers, it may have attributes in its table like “Customer Name,” “Phone Number,” and “Email Address.” By applying 1NF to this table, you ensure that the attributes that could contain multiple entries (“Phone Number” and “Email Address”) only contain one, making contacting that customer much simpler.

Second Normal Form (2NF)

A table that’s in 2NF is in 1NF, with the additional condition that none of its non-prime attributes depend on a subset of candidate keys within the table.

Example

Let’s say an employer wants to create a table that contains information about an employee, the skills they have, and their age. An employee may have multiple skills, leading to multiple records for the same employee in the table, with each denoting a skill while the ID number and age of the employee repeat for each record.

In this table, you’ve achieved 1NF because each attribute has an atomic value. However, the employee’s age is dependent on the employee ID number. To achieve 2NF, you’d break this table down into two tables. The first will contain the employee’s ID number and age, with that ID number linking to a second table that lists each of the skills associated with the employee.

Third Normal Form (3NF)

In 3NF, the table you have must already be in 2NF form, with the added rule of removing the transitive functional dependency of the non-prime attribute of any super key. Transitive functional dependency occurs if the dependency is the result of a pair of functional dependencies. For example, the relationship between A and C is a transitive dependency if A depends on B, B depends on C, but B doesn’t depend on A.

Example

Let’s say a school creates a “Students” table with the following attributes:

• Student ID
• Name
• Zip Code
• State
• City
• District

In this case, the “State,” “District,” and “City” attributes all depend on the “Zip Code” attribute. That “Zip” attribute depends on the “Student ID” attribute, making “State,” “District,” and “City” all transitively depending on “Student ID.”

To resolve this problem, you’d create a pair of tables – “Student” and “Student Zip.” The “Student” table contains the “Student ID,” “Name,” and “Zip Code” attributes, with that “Zip Code” attribute being the primary key of a “Student Zip” table that contains the rest of the attributes and links to the “Student” table.

Boyce-Codd Normal Form (BCNF)

Often referred to as 3.5NF, BCNF is a stricter version of 3NF. So, this normalization in DBMS rule occurs if your table is in 3NF, and for every functional dependence between two fields (i.e., A -> B), A is the super key of your table.

Example

Sticking with the school example, every student in a school has multiple classes. The school has a table with the following fields:

• Student ID
• Nationality
• Class
• Class Type
• Number of Students in Class

You have several functional dependencies here:

• Student ID -> Nationality
• Class -> Number of Students in Class, Class Type

As a result, both the “Student ID” and “Class” attributes are candidate keys but can’t serve as keys alone. To achieve BCNF normalization, you’d break the above table into three – “Student Nationality,” “Student Class,” and “Class Mapping,” allowing “Student ID” and “Class” to serve as primary keys in their own tables.

Fourth Normal Form (4NF)

In 4NF, the database must meet the requirements of BCNF, in addition to containing no more than a single multivalued dependency. It’s often used in academic circles, as there’s little use for 4NF elsewhere.

Example

Let’s say a college has a table containing the following fields:

• College Course
• Lecturer
• Recommended Book

Each of these attributes is independent of the others, meaning each can change without affecting the others. For example, the college could change the lecturer of a course without altering the recommended reading or the course’s name. As such, the existence of the course depends on both the “Lecturer” and “Recommended Book” attributes, creating a multivalued dependency. If a DBMS has more than one of these types of dependencies, it’s a candidate for 4NF normalization.

Fifth Normal Form (5NF)

If your table is in 4NF, has no join dependencies, and all joining is lossless, it’s in 5NF. Think of this as the final form when it comes to normalization in DBMS, as you’ve broken your table down so much that you’ve made redundancy impossible.

Example

A college may have a table that tells them which lecturers teach certain subjects during which semesters, creating the following attributes:

• Subject
• Lecturer Name
• Semester

Let’s say one of the lecturers teaches both “Physics” and “Math” for “Semester 1,” but doesn’t teach “Math” for Semester 2. That means you need to combine all of the fields in this table to get an accurate dataset, leading to redundancy. Add a third semester to the mix, especially if that semester has no defined courses or lecturers, and you have to join dependencies.

The 5NF solution is to break this table down into three tables:

• Table 1 – Contains the “Semester” and “Subject” attributes to show which subjects are taught in each semester.
• Table 2 – Contains the “Subject” and “Lecturer Name” attributes to show which lecturers teach a subject.
• Table 3 – Contains the “Semester” and “Lecturer Name” attributes so you can see which lecturers teach during which semesters.

Benefits of Normalization in DBMS

With normalization in DBMS being so much work, you need to know the following benefits to show that it’s worth your effort:

• Improved database efficiency
• Better data consistency
• Easier database maintenance
• Simpler query processing
• Better access controls, resulting in superior security

Limitations and Trade-Offs of Normalization

Normalization in DBMS does have some drawbacks, though these are trade-offs that you accept for the above benefits:

• The larger your database gets, the more demands it places on system performance.
• Breaking tables down leads to complexity.
• You have to find a balance between normalization and denormalization to ensure your tables make sense.

Practical Tips for Mastering Normalization Techniques

Getting normalization in DBMS is hard, especially when you start feeling like you’re dividing tables into so many small tables that you’re losing track of the database. These tips help you apply normalization correctly:

• Understand the database requirements – Your database exists for you to extract data from it, so knowing what you’ll need to extract indicates whether you need to normalize tables or not.
• Document all functional dependencies – Every functional dependence that exists in your database makes the table in which it exists a candidate for normalization. Identify each dependency and document it so you know whether you need to break the table down.
• Use software and tools – You’re not alone when poring through your database. There are plenty of tools available that help you to identify functional dependencies. Many make normalization suggestions, with some even being able to carry out those suggestions for you.
• Review and refine – Every database evolves alongside its users, so continued refining is needed to identify new functional dependencies (and opportunities for normalization).
• Collaborate with other professionals – A different set of eyes on a database may reveal dependencies and normalization opportunities that you don’t see.

Make Normalization Your New Norm

Normalization may seem needlessly complex, but it serves the crucial role of making the data you extract from your database more refined, accurate, and free of repetition. Mastering normalization in DBMS puts you in the perfect position to create the complex databases many organizations need in a Big Data world. Experiment with the different “normal forms” described in this article as each application of the techniques (even for simple tables) helps you get to grips with normalization.

Just like the snake it’s named after, Python has wrapped itself around the programming world, becoming a deeply entrenched teaching and practical tool since its 1991 introduction. It’s one of the world’s most used programming languages, with Statista claiming that 48.07% of programmers use it, making it as essential as SQL, C, and even HTML to computer scientists.

This article serves as an introduction to Python programming for beginners. You’ll learn Python basics, such as how to install it and the concepts that underpin the language. Plus, we’ll show you some basic Python code you can use to have a little play around with the language.

Python Basics

It stands to reason that you need to download and install Python onto your system before you can start using it. The latest version of Python is always available at Python.org. Different versions are available for Windows, Linux, macOS, iOS, and several other machines and operating systems.

Installing Python is a universal process across operating systems. Download the installer for your OS from Python.org and open its executable. Follow the instructions and you should have Python up and running, and ready for you to play around with some Python language basics, in no time.

Python IDEs and Text Editors

Before you can start coding in your newly-installed version of Python, you need to install an integrated development environment (IDE) to your system. These applications are like a bridge between the language you write in and the visual representation of that language on your screen. But beyond being solely source code editors, many IDEs serve as debuggers, compilers, and even feature automation that can complete code (or at least offer suggestions) on your behalf.

Some of the best Python IDEs include:

• Atom
• Visual Studio
• Eclipse
• PyCharm
• Komodo IDE

But there are plenty more besides. Before choosing an IDE, ask yourself the following questions to determine if the IDE you’re considering is right for your Python project:

• How much does it cost?
• Is it easy to use?
• What are its debugging and compiling features?
• How fast is the IDE?
• Does this IDE give me access to the libraries I’ll need for my programs?

Basic Python Concepts

Getting to grips with the Python basics for beginners starts with learning the concepts that underpin the language. Each of these concepts defines actions you can take in the language, meaning they’re essentially for writing even the simplest of programs.

Variables and Data Types

Variables in Python work much like they do for other programming languages – they’re containers in which you store a data value. The difference between Python and other languages is that Python doesn’t have a specific command used to declare a variable. Instead, you create a variable the moment you assign a value to a data type.

As for data types, they’re split into several categories, with most having multiple sub-types you can use to define different variables:

• String – “str”
• Numeric – “int,” “complex,” “float”
• Sequence – “list,” “range,” “tuple”
• Boolean – “bool”
• Binary – “memoryview,” “bytes,” “bytearray”

There are more, though the above should be enough for your Python basics notes. Each of these data types serves a different function. For example, on the numerical side, “int” allows you to store signed integers of no defined length, while “float” lets you assign decimals up to 15 points.

Operators

When you have your variables and values, you’ll use operators to perform actions using them. These actions range from the simple (adding and subtracting numbers) to the complex (comparing values to each other). Though there are many types of operators you’ll learn as you venture beyond the Python language basics, the following three are some of the most important for basic programs:

• Arithmetic operators – These operators allow you to handle most aspects of basic math, including addition, subtraction, division, and multiplication. There are also arithmetic operators for more complex operations, including floor division and exponentiation.
• Comparison operators – If you want to know which value is bigger, comparison operators are what you use. They take two values, compare them, and give you a result based on the operator’s function.
• Logical operators – “And,” “Or,” and “Not” are your logical operators and they combine to form conditional statements that give “True” or “False”

Control Structures

As soon as you start introducing different types of inputs into your code, you need control structures to keep everything organized. Think of them as the foundations of your code, directing variables to where they need to go while keeping everything, as the name implies, under control. Two of the most important control structures are:

• Conditional Statements – “If,” “Else,” and “elif” fall into this category. These statements basically allow you to determine what the code does “if” something is the case (such as a variable equaling a certain number) and what “else” to do if the condition isn’t met.
• Loops – “For” and “while” are your loop commands, with the former being used to create an iterative sequence, with the latter setting the condition for that sequence to occur.

Functions

You likely don’t want every scrap of code you write to run as soon as you start your program. Some chunks (called functions) should only run when they’re called by other parts of the code. Think of it like giving commands to a dog. A function will only sit, stay, or roll over when another part of the code tells it to do what it does.

You need to define and call functions.

Use the “def” keyword to define a function, as you see in the following example:

def first_function():

print (“This is my first function”)

When you need to call that function, you simply type the function’s name followed by the appropriate parenthesis:

first_function()

That “call” tells your program to print out the words “This is my first function” on the screen whenever you use it.

Interestingly, Python has a collection of built-in functions, which are functions included in the language that anybody can call without having to first define the function. Many relate to the data types discussed earlier, with functions like “str()” and “int()” allowing you to define strings and integers respectively.

Python – Basic Programs

Now that you’ve gotten to grips with some of the Python basics for beginners, let’s look at a few simple programs that almost anybody can run.

Hello, World! Program

The starting point for any new coder in almost any new language is to get the screen to print out the words “Hello, World!”. This one is as simple as you can get, as you’ll use the print command to get a piece of text to appear on screen:

print(‘Hello, World! ‘)

Click what “Run” button in your IDE of choice and you’ll see the words in your print command pop up on your monitor. Though this is all simple enough, make sure you make note of the use of the apostrophes/speech mark around the text. If you don’t have them, your message doesn’t print.

Basic Calculator Program

Let’s step things up with one of the Python basic programs for beginners that helps you to get to grips with functions. You can create a basic calculator using the language by defining functions for each of your arithmetic operators and using conditional statements to tell the calculator what to do when presented with different options.

The following example comes from Programiz.com:

# This function adds two numbers

def add(x, y):

return x + y

# This function subtracts two numbers

def subtract(x, y):

return x – y

# This function multiplies two numbers

def multiply(x, y):

return x * y

# This function divides two numbers

def divide(x, y):

return x / y

print(“Select operation.”)

print(“1.Add”)

print(“2.Subtract”)

print(“3.Multiply”)

print(“4.Divide”)

while True:

# Take input from the user

choice = input(“Enter choice(1/2/3/4): “)

# Check if choice is one of the four options

if choice in (‘1’, ‘2’, ‘3’, ‘4’):

try:

num1 = float(input(“Enter first number: “))

num2 = float(input(“Enter second number: “))

except ValueError:

print(“Invalid input. Please enter a number.”)

continue

if choice == ‘1’:

print(num1, “+”, num2, “=”, add(num1, num2))

elif choice == ‘2’:

print(num1, “-“, num2, “=”, subtract(num1, num2))

elif choice == ‘3’:

print(num1, “*”, num2, “=”, multiply(num1, num2))

elif choice == ‘4’:

print(num1, “/”, num2, “=”, divide(num1, num2))

# Check if user wants another calculation

# Break the while loop if answer is no

next_calculation = input(“Let’s do next calculation? (yes/no): “)

if next_calculation == “no”:

break

else:

print(“Invalid Input”)

When you run this code, your executable asks you to choose a number between 1 and 4, with your choice denoting which mathematical operator you wish to use. Then, you enter your values for “x” and “y”, with the program running a calculation between those two values based on the operation choice. There’s even a clever piece at the end that asks you if you want to run another calculation or cancel out of the program.

Simple Number Guessing Game

Next up is a simple guessing game that takes advantage of the “random” module built into Python. You use this module to generate a number between 1 and 99, with the program asking you to guess which number it’s chosen. But unlike when you play this game with your sibling, the number doesn’t keep changing whenever you guess the right answer.

This code comes from Python for Beginners:

import random

n = random.randint(1, 99)

guess = int(input(“Enter an integer from 1 to 99: “))

while True:

if guess < n:

print (“guess is low”)

guess = int(input(“Enter an integer from 1 to 99: “))

elif guess > n:

print (“guess is high”)

guess = int(input(“Enter an integer from 1 to 99: “))

else:

print (“you guessed it right! Bye!”)

break

Upon running the code, your program uses the imported “random” module to pick its number and then asks you to enter an integer (i.e., a whole number) between 1 and 99. You keep guessing until you get it right and the program delivers a “Bye” message.

Python Libraries and Modules

As you move beyond the basic Python language introduction and start to develop more complex code, you’ll find your program getting a bit on the heavy side. That’s where modules come in. You can save chunks of your code into a module, which is a file with the “.py” extension, allowing you to call that module into another piece of code.

Typically, these modules contain functions, variables, and classes that you want to use at multiple points in your main program. Retyping those things at every instance where they’re called takes too much time and leaves you with code that’s bogged down in repeated processes.

Libraries take things a step further by offering you a collection of modules that you can call from as needed, similar to how you can borrow any book from a physical library. Examples include the “Mayplotlib” library, which features a bunch of modules for data visualization, and “Beautiful Soup,” which allows you to extract data from XML and HTML files.

Best Practices and Tips for Basic Python Programs for Beginners

Though we’ve focused primarily on the code aspect of the language in these Python basic notes so far, there are a few tips that will help you create better programs that aren’t directly related to learning the language:

• Write clean code – Imagine that you’re trying to find something you need in a messy and cluttered room. It’s a nightmare to find what you’re looking for because you’re constantly tripping over stuff you don’t need. That’s what happens in a Python program if you create bloated code or repeat functions constantly. Keep it clean and your code is easier to use.
• Debugging and error handling – Buggy code is frustrating to users, especially if that code just dumps them out of a program when it hits an error. Beyond debugging (which everybody should do as standard) you must build error responses into your Python code to let users know what’s happening when something goes wrong.
• Use online communities and resources – Python is one of the most established programming languages in the world, and there’s a massive community built up around it. Take advantage of those resources. Try your hand at a program first, then take it to the community to see if they can point you in the right direction.

Get to Grips With the Basic Concepts of Python

With these Python introduction notes, you have everything you need to understand some of the more basic aspects of the language, as well as run a few programs. Experimentation is your friend, so try taking what you’ve learned here and writing a few other simple programs for yourself. Remember – the Python community (along with stacks of online resources) are available to help you when you’re struggling.

In April 1999, a $433 million Air Force rocket inexplicably malfunctioned almost immediately after liftoff, causing the permanent loss of an $800 million military communications satellite. This $1.2 billion disaster remains one of the costliest accidents in human history.

You might wonder if scientists ever found out what caused this misfiring. They sure did! And the answer is a software bug.

This accident alone is a testament to the importance of software testing.

Although you can probably deduce the software testing definition, let’s also review it together.

So, what is software testing?

Software testing refers to running a software program before putting it on the market to determine whether it behaves as expected and displays no defects.

While testing itself isn’t free, these expenses are cost-effective compared to potential money loss resulting from software failure. And this is just one of the benefits of this process. Others include improving performance, preventing human and equipment loss, and increasing stakeholder confidence.

Now that you understand why software testing is such a big deal, let’s inspect this process in more detail.

Software Testing Fundamentals

We’ll start with the basics – what are the fundamentals of testing in software engineering? In other words, what exactly is its end goal, and which principles underlie it?

Regarding the objectives of software testing, there are three distinct ones aiming to answer crucial questions about the software.

• Verification and validation. Does the software meet all the necessary requirements? And does it satisfy the end customer?
• Defects and errors identification. Does the software have any defects or errors? What is their scope and impact? And did they cause related issues?
• Software quality assurance. Is the software performing at optimal levels? Can the software engineering process be further optimized?

As for principles of software testing, there are seven of them, and they go as follows:

1. Testing shows the presence of defects. With everything we’ve written about software testing, this sounds like a given. But this principle emphasizes that testing can only confirm the presence of defects. It can’t confirm their absence. So, even if no flaws are found, it doesn’t mean the system has none.
2. Exhaustive testing is impossible. Given how vital software testing is, this process should ideally test all the possible scenarios to confirm the program is defect-free without a shadow of a doubt. Unfortunately, this is impossible to achieve in practice. There’s simply not enough time, money, or space to conduct such testing. Instead, test analysts can only base the testing amount on risk assessment. In other words, they’ll primarily test elements that are most likely to fail.
3. Testing should start as early as possible. Catching defects in the early stages of software development makes all the difference for the final product. It also saves lots of money in the process. For this reason, software testing should start from the moment its requirements are defined.
4. Most defects are within a small number of modules. This principle, known as defect clustering, follows the Pareto principle or the 80/20 rule. The rule states that approximately 80% of issues can be found in 20% of modules.
5. Repetitive software testing is useless. Known as the Pesticide Paradox, this principle warns that conducting the same tests to discover new defects is a losing endeavor. Like insects become resistant to a repeatedly used pesticide mix, the tested software will become “immune” to the same tests.
6. Testing is context-dependent. The same set of tests can rarely be used on two separate software programs. You’ll need to switch testing techniques, methodologies, and approaches based on the program’s application.
7. The software program isn’t necessarily usable, even without defects. This principle is known as the absence of errors fallacy. Just because a system is error-free doesn’t mean it meets the customer’s business needs. In software testing objectives, software validation is as important as verification.

Types of Software Testing

There are dozens (if not hundreds) types of testing in software engineering. Of course, not all of these tests apply to all systems. Choosing the suitable types of testing in software testing boils down to your project’s nature and scope.

All of these testing types can be broadly classified into three categories.

Functional Testing

Functional software testing types examine the system to ensure it performs in accordance with the pre-determined functional requirements. We’ll explain each of these types using e-commerce as an example.

• Unit Testing – Checking whether each software unit (the smallest system component that can be tested) performs as expected. (Does the “Add to Cart” button work?)
• Integration Testing – Ensuring that all software components interact correctly within the system. (Is the product catalog seamlessly integrated with the shopping cart?)
• System Testing – Verifying that a system produces the desired output. (Can you complete a purchase?)
• Acceptance Testing – Ensuring that the entire system meets the end users’ needs. (Is all the information accurate and easy to access?)

Non-Functional Testing

Non-functional types of testing in software engineering deal with the general characteristics of a system beyond its functionality. Let’s go through the most common non-functional tests, continuing the e-commerce analogy.

• Performance Testing – Evaluating how a system performs under a specific workload. (Can the e-commerce shop handle a massive spike in traffic without crashing?)
• Usability Testing – Checking the customer’s ability to use the system effectively. (How quickly can you check out?)
• Security Testing – Identifying the system’s security vulnerabilities. (Will sensitive credit card information be stored securely?)
• Compatibility Testing – Verifying if the system can run on different platforms and devices. (Can you complete a purchase using your mobile phone?)
• Localization Testing – Checking the system’s behavior in different locations and regions. (Will time-sensitive discounts take time zones into account?)

Maintenance Testing

Maintenance testing takes place after the system has been produced. It checks whether (or how) the changes made to fix issues or add new features have affected the system.

• Regression Testing – Checking whether the changes have affected the system’s functionality. (Does the e-commerce shop work seamlessly after integrating a new payment gateway?)
• Smoke Testing – Verifying the system’s basic functionality before conducting more extensive (and expensive!) tests. (Can the new product be added to the cart?)
• Sanity Testing – Determining whether the new functionality operates as expected. (Does the new search filter select products adequately?)

Levels of Software Testing

Software testing isn’t done all at once. There are levels to it. Four, to be exact. Each level contains different types of tests, grouped by their position in the software development process.

Read about the four levels of testing in software testing here.

Level 1: Unit Testing

Unit testing helps developers determine whether individual system components (or units) work properly. Since it takes place at the lowest level, this testing sets the tone for the rest of the software development process.

This testing plays a crucial role in test-driven development (TDD). In this methodology, developers perform test cases first and worry about writing the code for software development later.

Level 2: Integration Testing

Integration testing focuses on the software’s inner workings, checking how different units and components interact. After all, you can’t test the system as a whole if it isn’t coherent from the start.

During this phase, testers use two approaches to integration testing: top-down (starting with the highest-level units) and bottom-up (integrating the lowest-level units first).

Level 3: System Testing

After integration testing, the system can now be evaluated as a whole. And that’s exactly what system testing does.

System testing methods are usually classified as white-box or black-box testing. The primary difference is whether the testers are familiar with the system’s internal code structure. In white-box testing, they are.

Level 4: Acceptance Testing

Acceptance testing determines whether the system delivers on its promises. Two groups are usually tasked with acceptance testing: quality assessment experts (alpha testing before the software launches) and a limited number of users (beta testing in a real-time environment).

Software Testing Process

Although some variations might exist, the software testing process typically follows the same pattern.

Step 1: Planning the Test

This step entails developing the following:

• Test strategy for outlining testing approaches
• Test plan for detailing testing objectives, priorities, and processes
• Test estimation for calculating the time and resources needed to complete the testing process

Step 2: Designing the Test

In the design phase, testers create the following:

• Test scenarios (hypothetical situations used to test the system)
• Test cases (instructions on how the system should be tested)
• Test data (set of values used to test the system)

Step 3: Executing the Test

Text execution refers to performing (and monitoring) the planned and designed tests. This phase begins with setting up the test environment and ends with writing detailed reports on the findings.

Step 4: Closing the Test

After completing the testing, testers generate relevant metrics and create a summary report on their efforts. At this point, they have enough information to determine whether the tested software is ready to be released.

High-Quality Testing for High-Quality Software

Think of different types of software testing as individual pieces of a puzzle that come together to form a beautiful picture. Performing software testing hierarchically (from Level 1 to Level 4) ensures no stone is left unturned, and the tested software won’t let anyone down.

With this in mind, it’s easy to conclude that you should only attempt software development projects if you implement effective software testing practices first.

The term “big data” is self-explanatory: it’s a large collection of data. However, to be classified as “big,” data needs to meet specific criteria. Big data is huge in volume, gets even bigger over time, arrives with ever-higher velocity, and is so complex that no traditional tools can handle it.

Big data analytics is the (complex) process of analyzing these huge chunks of data to discover different information. The process is especially important for small companies that use the uncovered information to design marketing strategies, conduct market research, and follow the latest industry trends.

In this introduction to big data analytics, we’ll dig deep into big data and uncover ways to analyze it. We’ll also explore its (relatively short) history and evolution and present its advantages and drawbacks.

 

History and Evolution of Big Data

We’ll start this introduction to big data with a short history lesson. After all, we can’t fully answer the “what is big data?” question if we don’t know its origins.

Let’s turn on our time machine and go back to the 1960s. That’s when the first major change that marked the beginning of the big data era took place. The advanced development of data centers, databases, and innovative processing methods facilitated the rise of big data.

Relational databases (storing and offering access to interconnected data points) have become increasingly popular. While people had ways to store data much earlier, experts consider that this decade set the foundations for the development of big data.

The next major milestone was the emergence of the internet and the exponential growth of data. This incredible invention made handling and analyzing large chunks of information possible. As the internet developed, big data technologies and tools became more advanced.

This leads us to the final destination of short time travel: the development of big data analytics, i.e., processes that allow us to “digest” big data. Since we’re witnessing exceptional technological developments, the big data journey is yet to continue. We can only expect the industry to advance further and offer more options.

Big Data Technologies and Tools

What tools and technologies are used to decipher big data and offer value?

Data Storage and Management

Data storage and management tools are like virtual warehouses where you can pack up your big data safely and work with it as needed. These tools feature a powerful infrastructure that lets you access and fetch the desired information quickly and easily.

Data Processing and Analytics Framework

Processing and analyzing huge amounts of data are no walk in the park. But they can be, thanks to specific tools and technologies. These valuable allies can clean and transform large piles of information into data you can use to pursue your goals.

Machine Learning and Artificial Intelligence Platforms

Machine learning and artificial intelligence platforms “eat” big data and perform a wide array of functions based on the discoveries. These technologies can come in handy with testing hypotheses and making important decisions. Best of all, they require minimal human input; you can relax while AI works its magic.

Data Visualization Tools

Making sense of large amounts of data and presenting it to investors, stakeholders, and team members can feel like a nightmare. Fortunately, you can turn this nightmare into a dream come true with big data visualization tools. Thanks to the tools, creating stunning graphs, dashboards, charts, and tables and impressing your coworkers and superiors has never been easier.

Big Data Analytics Techniques and Methods

What techniques and methods are used in big data analytics? Let’s find the answer.

Descriptive Analytics

Descriptive analytics is like a magic wand that turns raw data into something people can read and understand. Whether you want to generate reports, present data on a company’s revenue, or analyze social media metrics, descriptive analytics is the way to go.

It’s mostly used for:

• Data summarization and aggregation
• Data visualization

Diagnostic Analytics

Have a problem and want to get detailed insight into it? Diagnostic analytics can help. It identifies the root of an issue, helping you figure out your next move.

Some methods used in diagnostic analytics are:

• Data mining
• Root cause analysis

Predictive Analytics

Predictive analytics is like a psychic that looks into the future to predict different trends.

Predictive analytics often uses:

• Regression analysis
• Time series analysis

Prescriptive Analytics

Prescriptive analytics is an almighty problem-solver. It usually joins forces with descriptive and predictive analytics to offer an ideal solution to a particular problem.

Some methods prescriptive analytics uses are:

• Optimization techniques
• Simulation and modeling

Applications of Big Data Analytics

Big data analytics has found its home in many industries. It’s like the not-so-secret ingredient that can make the most of any niche and lead to desired results.

Business and Finance

How do business and finance benefit from big data analytics? These industries can flourish through better decision-making, investment planning, fraud detection and prevention, and customer segmentation and targeting.

Healthcare

Healthcare is another industry that benefits from big data analytics. In healthcare, big data is used to create patient databases, personal treatment plans, and electronic health records. This data also serves as an excellent foundation for accurate statistics about treatments, diseases, patient backgrounds, risk factors, etc.

Government and Public Sector

Big data analytics has an important role in government and the public sector. Analyzing different data improves efficiency in terms of costs, innovation, crime prediction and prevention, and workforce. Multiple government parts often need to work together to get the best results.

As technology advances, big data analytics has found another major use in the government and public sector: smart cities and infrastructure. With precise and thorough analysis, it’s possible to bring innovation and progress and implement the latest features and digital solutions.

Sports and Entertainment

Sports and entertainment are all about analyzing the past to predict the future and improve performance. Whether it’s analyzing players to create winning strategies or attracting the audience and freshening up the content, big data analytics is like a valuable player everyone wants on their team.

Challenges and Ethical Considerations in Big Data Analytics

Big data analytics represent doors to new worlds of information. But opening these doors often comes with certain challenges and ethical considerations.

Data Privacy and Security

One of the major challenges (and the reason some people aren’t fans of big data analytics) is data privacy and security. The mere fact that personal information can be used in big data analytics can make individuals feel exploited. Since data breaches and identity thefts are, unfortunately, becoming more common, it’s no surprise some people feel this way.

Fortunately, laws like GDPR and CCPA give individuals more control over the information others can collect from them.

Data Quality and Accuracy

Big data analytics can sometimes be a dead end. If the material wasn’t handled correctly, or the data was incomplete to start with, the results themselves won’t be adequate.

Algorithmic Bias and Fairness

Big data analytics is based on algorithms, which are designed by humans. Hence, it’s not unusual to assume that these algorithms can be biased (or unfair) due to human prejudices.

Ethical Use of Big Data Analytics

The ethical use of big data analytics concerns the “right” and “wrong” in terms of data usage. Can big data’s potential be exploited to the fullest without affecting people’s right to privacy?

Future Trends and Opportunities in Big Data Analytics

Although it has proven useful in many industries, big data analytics is still relatively young and unexplored.

Integration of Big Data Analytics With Emerging Technologies

It seems that new technologies appear in the blink of an eye. Our reality today (in a technological sense) looks much different than just two or three years ago. Big data analytics is now intertwined with emerging technologies that give it extra power, accuracy, and quality.

Cloud computing, advanced databases, the Internet of Things (IoT), and blockchain are only some of the technologies that shape big data analytics and turn it into a powerful giant.

Advancements in Machine Learning and Artificial Intelligence

Machines may not replace us (at least not yet), but it’s impossible to deny their potential in many industries, including big data analytics. Machine learning and artificial intelligence allow for analyzing huge amounts of data in a short timeframe.

Machines can “learn” from their own experience and use this knowledge to make more accurate predictions. They can pinpoint unique patterns in piles of information and estimate what will happen next.

New Applications and Industries Adopting Big Data Analytics

One of the best characteristics of big data analytics is its versatility and flexibility. Accordingly, many industries use big data analytics to improve their processes and achieve goals using reliable information.

Every day, big data analytics finds “new homes” in different branches and niches. From entertainment and medicine to gambling and architecture, it’s impossible to ignore the importance of big data and the insights it can offer.

These days, we recognize the rise of big data analytics in education (personalized learning) and agriculture (environmental monitoring).

Workforce Development and Education in Big Data Analytics

Analyzing big data is impossible without the workforce capable of “translating” the results and adopting emerging technologies. As big data analytics continues to develop, it’s vital not to forget about the cog in the wheel that holds everything together: trained personnel. As technology evolves, specialists need to continue their education (through training and certification programs) to stay current and reap the many benefits of big data analytics.

Turn Data to Your Advantage

Whatever industry you’re in, you probably have goals you want to achieve. Naturally, you want to achieve them as soon as possible and enjoy the best results. Instead of spending hours and hours going through piles of information, you can use big data analytics as a shortcut. Different types of big data technologies can help you improve efficiency, analyze risks, create targeted promotions, attract an audience, and, ultimately, increase revenue.

While big data offers many benefits, it’s also important to be aware of the potential risks, including privacy concerns and data quality.

Since the industry is changing (faster than many anticipated), you should stay informed and engaged if you want to enjoy its advantages.

Books represent gateways to new worlds, allowing us to gain valuable knowledge on virtually any topic. Those interested in exploring computer science books face two challenges. First, just like you can’t build a good house without a proper foundation, you can’t expand your knowledge if you don’t understand basic concepts. Secondly, technology is always evolving, so besides understanding how things work, you need to stay current with the latest trends.

Finding books that help you build a good foundation and follow innovations isn’t easy. Fortunately, you don’t have to go through hundreds of titles to find the good ones. Here, we’ll introduce you to the best BSc Computer Science books that will set you up for success.

Top BSc Computer Science Books

These BSc Computer Science books can “program” your mind and help you absorb knowledge.

Introduction to Computer Science

Many people are eager to learn how to program and immerse themselves in the IT world. But the first step toward that is adopting fundamentals. Before jumping into the IT industry, you need to learn more about computer science and the basic concepts behind it.

Computer Science Illuminated by Nell Dale and John Lewis

This student-friendly book sheds light on computer science. It explores operating systems, hardware, software, and networks from “neutral ground” (without focusing on particular programming languages). Therefore, if you don’t “speak” programming languages just yet, this book will be your best friend.

Intro to Python for Computer Science and Data Science: Learning to Program With AI, Big Data, and the Cloud by Paul Deitel and Harvey Deitel

If you want to be a programming expert, you may need to speak Python, a universal language with a wide array of applications. This book teaches you how to use Python in computer science and offers the perfect balance between theoretical and practical knowledge. It transforms complex information into comprehensive and engaging data.

Data Structures and Algorithms

Finding the best BSc Computer Science book on data structures and algorithms can feel like trying to find a needle in a haystack. We found the needle for you and offer the best options.

Data Structures and Algorithms Made Easy by Narasimha Karumanchi

This book is a winner in the data structures and algorithms game. It’s the perfect option for beginners interested in learning the topic from scratch and building a solid foundation for more advanced levels. It covers basic concepts and moves on to more complex stuff without overwhelming the readers.

Data Structures and Algorithms in Java by Robert Lafore

If you’re familiar with Java and want to start with data structures and algorithms, this book is the gold standard. It will guide you on a journey from basic Arrays and Strings to advanced structures like Hash-Tables and Graphs.

Computer Networks

Computer networks are grids through which computing devices “talk to” each other and share data. Here are the books you can use to improve your knowledge and get ahead in your career.

Computer Networks by Andrew S. Tanenbaum

If you want to understand the nitty-gritty behind computer networks, this book is the way to go. Hop on a journey through email, the world wide web, video conferencing, and much more, to understand how the networks work and how to use them to your advantage.

Every chapter follows the same, easy-to-follow structure containing basic principles and real-life examples.

Computer Networking: A Top-Down Approach by James F. Kurose and Keith W. Ross

This beginner-friendly book takes a somewhat unusual approach. It first introduces students to applications and uses them to explain fundamental concepts. That way, students are exposed to the “real world” early on and can understand how networking works with ease.

 

Operating Systems

An operating system for a computer is like oxygen for a human; it can’t live without it. Operating systems are interfaces that support everything computers do. Here are the best books about them.

Operating Systems: Three Easy Pieces by Remzi Arpaci-Dusseau and Andrea Arpaci-Dusseau

How do operating systems work? What are the three basic concepts hiding behind every OS? Find the answers to these questions and learn everything OS-related in this book. While beginner-friendly, this amazing study can be combined with more advanced materials and offer a deeper understanding of modern OSs.

Guide to Operating Systems by Greg Tomsho

This book represents a detailed guide on installing, updating, maintaining, and configuring operating systems and everything related to them. Besides offering general info, the book explores specific OSs and allows you to peek into this world without feeling overwhelmed.

Database Systems

Database systems are like virtual warehouses where you can keep your data secure. They’re the ones we can “thank” for easy information retrieval, browsing, and organization. If you want to learn the ins and outs of database systems, these books can help.

Database Systems: The Complete Book by Hector Garcia-Molina, Jeffrey D. Ullman, and Jennifer Widom

This book is the holy grail for many computer science students. It offers a comprehensive approach and detailed explanations of everything related to database system design, use, and implementation. The book is extensive, but it’s written in an engaging way, so reading through it is a breeze.

Database Systems: Design, Implementation, & Management by Carlos Colonel and Steven Morris

Building your virtual warehouses for storing data may seem impossible. But it can become your reality thanks to this excellent book. It contains clear and comprehensive instructions on building database systems, offers concrete examples, but also focuses on the bigger picture and latest industry trends.

Software Engineering

Designing and constructing software is no walk in the park. If you’re interested in this industry, you need to build your skills meticulously. Books that can help you on this exciting (and sometimes frustrating) journey are reviewed below.

Clean Code: A Handbook of Agile Software Craftsmanship by Robert C. Martin

In this book, Robert C. Martin, a software engineering legend, discusses the seemingly insignificant differences between bad and poorly-written codes. He explains which “symptoms” bad codes manifest and how to clean them.

Code Complete: A Practical Handbook of Software Construction by Steve McConnell

One of the first (and smartest) steps toward building quality code is getting this book. Here, the author summarized everything there is to know about constructing software. Since the book contains both the basics and the more advanced construction practices, everyone finds it useful, both beginners and pros.

Additional Resources for BSc Computer Science Students

BSc Computer Science books aren’t the only spring you should drink water from if you’re thirsty for knowledge on the subject.

Online Platforms and Courses

Online platforms and courses are great resources for those who want to expand their knowledge and learn how to cash it in. The internet is overflowing with great courses focusing on various aspects of computer science. Here are a few ideas to get you started:

• Open Institute of Technology (OPIT) – The institute offers a comprehensive online BSc in Computer Science. Throughout the program, students get acquainted with everything computer science-related. After completing their studies, they’ll be able to land high-paying jobs.

• Udemy and Coursera – Although not “official” institutes and universities, these platforms deserve a seat at the table. Both Udemy and Coursera offer quality computer science courses held by some of the most respected names in the industry.

Coding Practice Websites

You’ve read books, attended courses, and feel like you know everything there is to know about the theoretical part. But is there a way to put this theory into practice and see whether your codes work? The answer is yes! Practice makes perfect, and coding practice websites will become your best friends and help you conquer programming.

• Coderbyte – Solve real-life coding issues and drive your skills to perfection. With over a dozen available programming languages, you can try out as many ideas as you’d like.
• HackerRank – HackerRank is home to hundreds of coding challenges. Plus, it has leaderboards, so you can see how you compare to other coders. It’s also home to useful tutorials, and since the website is popular, you may even be able to land your dream job.

Computer Science Forums and Communities

Is there a better place for like-minded people to meet and discuss the topics they’re passionate about? Computer science forums and communities should be an important stop on your way to becoming an expert on the subject.

Tips for Success in BSc Computer Science

Success doesn’t happen overnight (at least for most people). If computer science is your true passion, here’s how to master it:

• Focus on the basics to create a good foundation.
• Put your thinking cap on and practice problem-solving and critical thinking skills.
• Participate in group projects and collaborations (teamwork makes the dream work).
• Keep up with the latest industry trends.
• Gain valuable hands-on experience through internships.

Acquire Computer Science Knowledge Effectively

Although books don’t offer practical knowledge, they can be invaluable allies in setting a great theoretical foundation. By carefully choosing the best books and putting effort into developing your skills, you’ll become a pro in a jiff.