Growth is inevitable in the AI sector. According to Statista, the already-booming industry looks set to go from a value of $100 billion in 2021 to $2 trillion by 2030, increasing by a multiple of 20 to become one of the world’s biggest industries. Naturally, the need for skilled AI professionals will grow alongside that enormous scaling.

That’s where you come in.

With the right applied AI course, you can develop both the knowledge of the foundational theory that sits behind AI and learn how to apply that theory in a real-world setting. Here are four of the best applied AI courses to get you started.

Factors to Consider When Choosing an Applied AI Course

Every search for a new course starts with figuring out the strengths and weaknesses of each one you consider. These factors help you do that, ensuring you don’t spend your hard-earned money on a course that fails to equip you with skills that make you desirable to employers.

Course Content and Curriculum

AI is such an expansive field that every applied AI course has the potential to cover different topics and subjects. Think about what you want to learn (and your prospective career path), then align your course selection with that intended path.

Course Duration

Applied AI courses can vary tremendously in length, from several years for degree-level courses to a few months for online courses. Ask yourself how long you wish to spend studying. Also, consider the flexibility of the course, such as whether you’ll be able to fit your studies around your existing work and family commitments.

Instructor Expertise

AI is a burgeoning industry, meaning expertise levels vary from course to course. For applied AI courses, in particular, you want professors who combine in-depth knowledge of the theory with real-world experience. What have they done in the industry? If the answer is “nothing,” they may not be able to guide you down the path to an AI-centric career.

Course Fees and Financial Aid

Course fees vary massively depending on the type of course you take. For example, those in the U.K. can easily spend between £15,000 and £25,000 on university-level courses, with Aston University’s tuition fees of £23,200 being somewhat typical. Online and self-learning courses cost considerably less, so you need to figure out how much you’re willing to spend (and if you can get any help with your fees) before moving forward.

Job Placement and Career Support

Though you need one eye pointed toward the present when choosing between applied AI courses, the other needs to be firmly pointed toward the future. What prospects will you have when you complete the course? In other words, does the course provide you with a direct path into the industry, along with support, or are you left to fend for yourself once you have your qualification?

Top Choices for Mastering Artificial Intelligence

Choices abound when you jump online to find applied AI courses. The following selection offers a nice mix, from online certifications offered by industry professionals to a couple of courses from some of the world’s most prestigious universities.

Course 1 – IBM Applied AI Professional Certification

If you’re fresh to the world of AI (though ideally not new to computer science), IBM’s industry-specific applied AI courses offer both foundational knowledge and a respected qualification. They’re flexible, too, with this course lasting for six months but only requiring three hours of work per work. Those in full-time work (or education) can fit the course around their lifestyle, while those who have time to burn can complete the entire course much quicker, earning degree credits along the way.

Key Features and Benefits

• Certification from one of the most respected companies in the AI space
• Direct exposure to use cases in the deep learning, machine learning, and neural network spheres
• Learn how to build AI-powered solutions (like chatbots) using Python and IBM’s Watson AI
• Over three-quarters (77%) of students report career improvement

Pricing and Enrollment

IBM’s course is available via Coursera and offers a seven-day trial you can use to get to grips with its structure and examine its modules. It’s fully online, which improves flexibility at the cost of not having direct access to a professor, and you’ll receive an IBM badge upon completion. You’ll pay a monthly fee of $35 (approx. €31) and can enroll at almost any time.

Course 2 – Computer Science for Artificial Intelligence (Harvard University)

Harvard University may be seen as the gold standard in the United States, but what many don’t know is that it offers a comprehensive suite of online courses that almost anybody can take. Its Computer Science for Artificial Intelligence course is a perfect example. Comprising of two courses – an introduction to computer science followed by an introduction to applying computer science principles to AI using Python – it lasts for five months. You get access to professors and can learn at your own pace, with the course recommending between seven and 22 hours of study per week.

Key Features and Benefits

• Two modules give you a crash course in applied AI and the computer science theory that underpins it
• Director access to Harvard professors Doug Lloyd, Brian Yu, and David J. Malan
• Complete flexibility in how and when you learn
• Get to grips with Python and build experience with machine learning libraries

Payment and Enrollment

As an online course, Computer Science for Artificial Intelligence is available for enrollment whenever you’re ready, with the five months starting once you’re enrolled. It costs £277 (approx. €312) and you’ll need to create an account with the EDX website (which hosts the course) to get started.

Course 3 – Artificial Intelligence Graduate Certificate (Stanford University)

Ranked as the third-best university in the United States for general computer science and AI teaching, Stanford University has opened up some of its best courses to online learners. Entirely online (and instructor-led for those who want more guidance) this is one of those applied AI courses that is equivalent to a full graduate degree. You’ll complete at least one required course – with a choice between machine learning and the principles of AI – and select up to three electives. It’s the electives that make this course stand out, as there are 18 to choose from, with the right combination giving you a chance to specialize for specific career paths.

Key Benefits and Features

• Direct tuition from prominent Stanford faculty members, including Andrew Ng and Chelsea Finn
• Some level of autonomy in how you study thanks to the online-centric nature of the course
• Specialize in specific areas of AI thanks to a wide range of electives
• You get a degree from one of the world’s foremost colleges in the AI field

Payment and Enrollment

Let’s get the bad news out of the way immediately – this isn’t a cheap course. As a full-on graduate degree, it costs between $18,200 and $22,400 to take (approx. €16,235 and €19,980), though financial aid may be available for some students. You can’t just hop onto the course, either, as a college-level understanding of calculus, linear algebra, Probability Theory, and several programming languages is required. Stanford itself calls this one of its most difficult courses and recommends that you take several foundation courses (ideally at degree level) before enrolling.

Course 4 – Master in Applied Data Science & AI (OPIT)

As a full postgraduate course that takes between 12 and 18 months to complete, OPIT’s Master in Applied Data Science & AI is an interesting case for one simple reason – there are no computer science prerequisites. The course is open to everybody and it teaches both advanced applied AI concepts and the foundational knowledge needed to understand them. You’ll complete a pair of terms containing courses, with your final term dedicated to a project or thesis that puts what you’ve learned into practice.

Key Benefits and Features

• The course is supplied by an institution with accreditation from the European Qualification Framework
• It’s a fully remote course that gives you control over how and when you learn
• Discounts and payment plans are available, as well as scholarship and funding options
• You come out of the course with a recognized postgraduate degree

Payment and Enrollment

Though the course usually costs €6,500, OPIT offers “early bird” discounts that allow you to enroll for €4,950, assuming you sign up early enough. Intakes are semi-regular, with the next one scheduled for October 2023 and international students get 90 credits under the European Credit Transfer and Accumulation System (ECTS) for successful completion.

Tips for Success in an Applied AI Course

As you can see, you have plenty of options for applied AI courses, from professional certifications designed to get you into a career quickly to full postgraduate degrees. Regardless of your choice, these tips will help you get your precious certification:

• Dedicate time for study – Time well managed is time well spent. Understand that you’ll need to dedicate self-learning time to get to grips with concepts you’re taught during classroom hours.
• Set clear goals – Going into an applied AI course with no sense of what you’re supposed to get out of that course leaves you directionless upon completion. Make sure you know exactly what you stand to gain before committing time (and money) to a course.
• Network often – Even online courses give you a chance to get involved in teamwork projects and speak to experienced industry professionals. Take those chances. The more connections you build during your studies, the more opportunities you’ll see coming out of the back end.
• Seek guidance – As attractive as the prospect of self-guided learning may be, we all need a helping hand from time to time. If a course provides direct access to tutors and professors, use it.
• Stay up to date – AI is a fast-moving field, with every change and advancement bringing new challenges and opportunities. Stay on top of what’s happening in the industry. You may just find that one course sets you up to be ready for those changes, while another may not.

Build Your Skills With an Applied AI Course

Whether you go down the full postgraduate degree route or you choose a professional qualification, an applied AI course is a route into one of the world’s fastest-growing industries. Simply put, we’re set for an AI explosion. Over the next decade, AI will permeate everything we do, from complex computing to simple office tasks, and you can use the right course to give yourself the skills you need to take advantage of that fact. Explore the options shared in this article, ask yourself what you want to achieve in your career, and make the educational choice that’s right for you.

Combine mathematics with analytics, mix in programming skills, and add a dash of artificial intelligence, and you have the recipe for creating a data scientist. These professionals use complex technical skills to parse, analyze, and draw insights from complex datasets, enabling more accurate decision-making in the process.

As companies gather more data than ever before (both about their customers and themselves), these skills are in increasingly high demand. That’s demonstrated by data from the U.S. Bureau of Labor Statistics, which says that the number of data science jobs in the U.S. alone looks set to increase by 36% between 2021 and 2031.

That higher-than-average growth rate creates an opportunity for students, though grasping that opportunity requires a dedication to learning. This article explores the question of what is data science course material and highlights a selection of courses that set you on a data-propelled career path.

What to Expect From a Data Science Course

Answering the question of “what is data science course?” starts with examining the components of the typical course. Bear in mind that these components vary in nature and complexity depending on the specific course you take, though all are usually present.

Overview of Course Content

The content of a data science course is usually split into four core categories:

• Statistics and Probability – Math underpins everything a data scientist does, as they use numbers to spot patterns and determine the likelihood of various potential outcomes. Most data science courses delve into statistics and probability for this reason, with more advanced courses often requiring a degree in a field related to these areas.
• Programming – Whether it’s Python (the most popular data science programming language), R, or SQL, your course will teach you how to write in a language that machines understand.
• Data Visualization and Analysis – Anybody can collect reams of data. It’s the ability to visualize that data (and draw insights from it) that sets data scientists apart from other professionals. A good course equips you with the ability to use visualization tools to shine a spotlight on what a dataset actually tells you.
• Machine Learning and AI – The rise of machine learning transformed data science. Using algorithms created by data scientists, machines can analyze datasets presented to them and learn from the patterns to predict probabilities for different outcomes and even predict market trends. Your course will teach you how to create the algorithms that serve as a machine learning model’s “brain.”

Hands-On Projects and Real-World Applications

If you had the desire, you could read pages and pages on how to tune a car’s engine. But without practical and real-world wrench-in-hand experience working on an engine, you’ll never figure out how what you learn from books applies in the field.

The same line of thinking applies to data science, which is often so technically complex that it’s difficult to see how what you learn applies in the real world. A good data science course incorporates a real-world component through projects and exposure to faculty members who have direct experience in using the skills they teach.

Peer Collaboration and Networking

What is data science course for if not to learn how to become a data scientist? While learning the technical side is crucial, of course, a good course also puts you in contact with like-minded individuals who have the same (or similar) goals as you.

That contact helps you to build the collaborative skills you’ll need when you enter the workforce. But perhaps more importantly, it aids you in creating a network of peers who could lead you to job opportunities or work with you on entrepreneurial ventures.

Top Data Science Courses Available

With the components of a data science course established, you have a vital question to answer – what data science course should you take? The following are four suggestions (two online courses and two university courses) that give you a solid grounding in the subject.

Online Courses

Taking a data science course online gives you flexibility, though you may miss out on some of the collaborative and networking aspects that university-led courses provide.

Course 1 – What Is Data Science? (IBM via Coursera)

Coming with the stamp of approval from IBM, a leading name in the computer science field, this nine-hour course is suitable for beginners who want a self-paced learning approach. It’s part of a multi-part program (the IBM Data Science Professional Certificate) that’s designed to give you an industry-recognized qualification that could fast-track your entry into the field.

As for the course itself, it’s split into three parts, each containing multiple instructor-led videos and quizzes to test what you’ve learned. By the end, you’ll understand what data scientists do, build a basic understanding of various data science-related topics, and see how the profession relates to the modern business world. Granted, the course offers a surface-level understanding of the subject, with more complex topics examined in other classes. But it’s a superb tool for developing the foundation on which you can build with other courses.

Course 2 – Introduction to Data Science With Python (Harvard via edX)

Where IBM’s course equips you with general knowledge, Harvard’s online offering digs into the practical side of data science. Specifically, it focuses on using Python (and its many libraries) to solve data science problems drawn from real-world examples.

The course takes eight weeks, with study time between three and four hours per week. Ultimately, this class helps you build on your established programming skills and shows you how to apply them in a data science context.

As you may have guessed, that mention of building on existing skills means you’ll need a solid understanding of Python to participate in this free course. But assuming you have that, Harvard’s class is ideal for showing you just how flexible the language can be, especially when developing machine learning algorithms. Furthermore, simply having the word “Harvard” on your online certification adds credibility to your CV when you start applying for jobs.

University Programs

University programs demand a larger time (and monetary) commitment than purely online programs, though the upside is that you get a more prestigious qualification at the end. These two courses are ideal, with one even being a hybrid of online and university-level courses.

Course 1 – Master in Applied Data Science & AI (OPIT)

Let’s get the obvious out of the way first – you’ll need a BSc degree, or an equivalent, in a computer science or mathematical subject to take OPIT’s data science Master’s degree course.

Assuming you meet that prerequisite, this course comes in 18 and 12-month varieties, with the latter being a fast-tracked version that delivers the same content while asking you to dedicate more time to studying. It costs €6,500 to take, though early bird discounts are available, and an EU-accredited university delivers it.

The course eschews traditional exams by taking a progressive assessment approach to determine how well you’re absorbing the materials. It’s also focused on the practical side of things, with the application of data science in business problem-solving and communication being core modules.

Course 2 – MSc in Social Data Science (University of Oxford)

As the world’s leading university for seven consecutive years, according to Times Higher Education (THE) World University Rankings, the University of Oxford has outstanding credentials. And its MSc in Social Data Science is an interesting course to take because it specializes in a specific subject area – human behavior.

The degree stands on the precipice of an emerging field as it focuses on using data science to analyze, critique, and reevaluate existing social processes. It combines general machine learning models with more specialized data science tools, such as natural language processing and computer vision, to equip students with a high degree of technical knowledge.

That knowledge doesn’t come cheap, either in time or monetary commitment. The University of Oxford expects students to devote 40 hours per week to study, with overseas students having to pay £30,910 (approx. €35,795) to participate. While these investments are naturally intimidating, the university’s prestige makes the time and money you spend worthwhile when you start speaking to employers.

Factors to Consider When Choosing a Data Science Course

The four courses presented here each offer something different in terms of delivery and the expertise required of the student to participate. When choosing between them (and any other courses you find), you should consider the following questions:

• Does the course content and curriculum align with your career goals?
• Can you make time for the course within your schedule, and how much flexibility does it offer?
• Do the instructors provide the expertise you need and teach in a style that suits your preferred way of learning?
• Will you get an adequate return on your investment, both in terms of the prestige of the certification you receive and the knowledge you gain?
• Have past (or current) students recommended the course as a good option for prospective data scientists?

The Benefits of Completing a Data Science Course

Given the technical nature of the subject, you may be asking yourself what is data science course content going to deliver in terms of benefits to your life. The answers are as follows:

• Your skills improve your job prospects by putting you in pole position to enter a market that’s set for substantial growth over the next 10 years.
• The problem-solving and analytical tools you gain are useful in the data science field and other career paths.
• Any course you select puts you in contact with industry professionals who offer networking opportunities that could lead to a new job.
• You get to learn about (and experiment with) cutting-edge tools and technologies that will become the standard for modern business, and more, in the coming years.

What Is Data Science Course – It’s Your Route Into a Great Career

Let’s conclude by reiterating something mentioned at the start of the article – the data science sector will grow by 36% over the next decade or so.

That growth alone demonstrates the importance of data science, as well as why choosing the right course is so critical to your future success. With the right course, you make yourself a desirable candidate to organizations that are quickly accepting that they need data scientists to help them make decisions for the future.

Digital technologies pretty much run the modern world. From our phones and computers to manufacturing, finance, and retail, so many aspects of life rely on machines crunching unimaginable quantities of data.

As a discipline at the core of this digital era, data science is still expanding its scope. Leading organizations in this sector never seem to get enough of new talent, and the demand for data science specialists is constantly rising.

Luckily, the same digital-first environment that depends on data science also gives ample opportunities for learning this essential trade. You can easily find a data science course online, and the same goes for certifications. Better yet, there are Masters programs you can take without leaving your home.

If the prospect of online data science courses sounds exciting, this article will recommend some of the best available programs.

Top Data Science Online Courses

There’s no shortage of options to learn data science online. The courses that made our list come from prestigious institutions and offer the most comprehensive approach to the subject.

When choosing the top courses, we followed straightforward criteria. We looked into institution reputation, hands-on experiences, lecture quality, and comprehensiveness. Here are the best online data science courses that excelled in these categories.

Metis – Data Science & Analytics Training

If you’re looking for an online course with live lectures, then Data Science & Analytics Training from Metis will be a great choice. The lecturers come from leading tech companies, giving lessons that cover the complete data science process.

While there are advanced bootcamps on offer, Metis provides a comprehensive beginner data science online course with certificate, which lasts for six weeks. The price for this course is $750 (roughly 695 euros at time of writing). This course offers an accredited certificate.

Dataquest – Introduction to Python Programming

Dataquest is somewhat unique as it represents a knowledge repository for standalone learning or as a supplementary resource. If you want to learn data science with this platform, the Introduction to Python Programming course is a quality choice.

The class is brief, informative, and suited for beginners. It consists of six lessons and a practical project, with an estimated 12 hours needed to complete the self-paced course. While the introductory course doesn’t offer certification, it will open up a learning path with Dataquest that does end up in winning an expert-reviewed credential.

A third of the learning resources is available for free. The full access to Dataquest courses will require a subscription to the service with a monthly or yearly model.

Harvard University – CS109 Data Science

Getting education from Harvard is about as elite as one can get. The CS109 Data Science course embodies all the benefits of learning from a prestigious institution like Harvard. The course teaches data science essentials, including Python programming, statistics, and machine learning. The complete material is accessible on dedicated GitHub pages. You can clone the repository to get access to the entire curriculum.

Since this is just the repository of resources, going through them won’t give you a certificate. However, it’s free and completely available online, making it an educational opportunity you shouldn’t miss. With the detailed knowledge of the basics under your belt, you’ll progress to more complex (and pricier) courses with ease.

Online Data Science Master’s Programs

You might think that getting a master’s diploma requires you to physically attend a college. And while that used to be the case only a few decades ago, you can enroll in a master’s program online. Better yet, you may do so at a reputable institution with a world-leading data science department.

We picked several top-tier online data science masters programs online. Our choice was based on similar criteria as for the courses:

• How reputable is the institution?
• Does the program offer practical knowledge?
• Are the lectures comprehensive and quality-made?

With all that in mind, here are our top choices of online master’s programs in data science.

University of Aberdeen – Data Science MSc

The University of Aberdeen is one of the leading educational institutions in the UK. The Data Science MSc program is the university’s regular MSc data science online program that’s also completely available online. The curriculum includes vital skills concerning algorithms, data analysis, mathematical modeling, and more.

With full-time learning, the degree can be completed in one year. However, you can study at your own pace and take as much time as you need between individual courses. The limit for completion is six years, and enrolling in the program will cost £14,920.

Rome Business School – International Online Master in Data Science

The International Online Master in Data Science from the Rome Business School represents an excellent opportunity to learn, get in touch with industry-leading companies, and build a professional network. The school houses bootcamps across Europe and worldwide, which may increase your job market reach.

The participation fee for this program is €6,700. If paid after starting the course, applicants can split the cost into six installments, free of interest. Covering the fee in installments in advance will grant you a 5% discount. Paying in a lump sum comes with a 10% discount.

European Leadership University – Professional Master in Data Science & Leadership

The European Leadership University offers a comprehensive program that includes individual and group work, as well as interactive workshops. Completing the Professional Master in Data Science & Leadership program will earn you a master’s degree and two recognized certificates: in data science and leadership.

The program is priced at €5,000, with the option to pay the fee in five installments during the study period. Upfront payments come with a 10% discount. The program includes classes on machine learning, statistics, data collection and handling, Python programming, and more. This master’s course lasts for 19 months.

Key Skills to Learn in Data Science

Data science consists of numerous fields, some of which are more theoretical while other lean heavily towards practical applications. The later data science aspects include essential skills that you can use in the market:

• Programming languages
• Data visualization and reporting
• Machine learning and AI
• Big data
• Statistics

In programming, languages like Python, R, and SQL are used to create program environments and write specific commands. As a data science skill, the study of programming languages explores the limitations and possibilities of existing and new languages.

Data visualization deals with representing complex datasets in a more comprehensive way. It’s related to reporting and may be viewed as its subset. Visualization tools include charts, graphs, and presentations.

Machine learning might be the most well-known aspect of data science. Technologies like deep learning are at the core of AI development, enabling machines to learn from limited data input. Recently, great advances were made in unsupervised learning, which doesn’t require human input at all.

Big data refers to processing and analyzing large amounts of information. Handling massive data volumes presents specific challenges in terms of computational capacity and error reduction.

Finally, statistics form one of the cornerstones of practical data science use. Statistical analysis is helpful in business, demographics, and numerous social and natural sciences. Reliable statistics help researchers create predictive models and projections, allowing for efficient planning down the line.

Benefits of Earning a Data Science Certificate or Degree

Getting a degree or certificate in data science offers you an edge both in professional improvement and in the job market. The very process of gaining credentials is an opportunity to learn and practice essential skills. Plus, you can build a respectful portfolio along the way.

A degree or certificate means better job opportunities. Every reputable employer in the field will want to see recognized credentials from their applicants, and that’s particularly true when hiring for better-paid positions.

If you’ve already got a starting-level job in data science, credentials from reputable institutions will help advance your career. That kind of growth also creates a potential for better salaries and work benefits.

Finally, once you enroll in a data science degree or certificate program, you’ll meet other people pursuing similar interests. This will be an excellent opportunity for networking. Combined with the credentials, your new network of colleagues can help you advance even further.

Tips for Choosing the Right Data Science Online Course or Program

When you start searching for the right program online, it’s vital to consider several factors:

• The content and curriculum of the course
• Instructor expertise and reputation in the industry
• The duration of the program
• How flexible the course is
• Pricing and whether there are options for financial aid
• Testimonials or reviews from previous students

Besides these considerations, you should account for your personal preferences. Define your goals and what you want to achieve with the program. Also, it’s important for the program to match the learning style that suits you the best.

Gain the Essential Skills for the Hottest Profession Today

Our data science course suggestions include a selection of programs from the most respected industry leaders. With the high-quality courses on offer, all you’ll need to do is pick the program that matches your career goals.

Today’s job market has a high demand for data science experts. Getting certified or earning a degree in the field will help you start a career easier, which is why you should consider this important move as soon as possible.

Every time you’ve chatted with a bot on a website, you’ve seen the basis of artificial intelligence (AI) and machine learning (ML) in action. Your experiences with augmented reality, any prompts you’ve ever delivered to ChatGPT, and a host of other technologies that businesses are already leveraging show us how crucial these two fields are, both today and in the future.

AI and ML are taking over the world. And with the right AI & ML courses, you put yourself in the ideal position to forge a career in an industry that’s set for a continuous annual growth rate of 36.2% between 2023 and 2030.

Factors to Consider When Choosing an AI and ML Course

AI ML courses come in all shapes and sizes, with some delivering the basics you need to build a foundation in the subjects and others moving on from those foundational concepts and into specializations. These five things are your biggest considerations when choosing a course.

1 – Course Content and Curriculum

What does the course teach? That’s not just an important question in terms of figuring out if the course helps you develop the skills you need, but it’s a crucial one for your future career prospects. The curriculum informs every step you take on your learning journey. If the content isn’t up to scratch (or takes you in a different direction than the one you intend to go in) it’s not the course for you.

2 – Course Duration and Flexibility

Combine work and family with your personal life and existing educational commitments and you have the recipe for a quagmire of time-consuming tasks that may not fit with a long-term course. The best AI and ML courses online offer flexibility, allowing you to fit your studies around other commitments and opening the door to self-paced learning.

3 – Your Instructors

Imagine you walk into a classroom and your instructor introduces themself. They tell you they have a couple of qualifications in the fields of AI and ML, but they haven’t worked in either industry and can’t expose you to professionals who have. Is that any use to you in your career? A good instructor combines technical expertise (which they’ll need a lot of) with industry experience they can draw on to lead you down the right career path.

4 – Course Reviews and Ratings

As any online marketer will tell you, user-generated content in the form of reviews, social media posts, and simple ratings tells you a ton about what a product delivers. That’s as true for AI and machine learning courses as it is for anything else. Check out what other people have to say about the course, paying special attention to former students and what’s happened to them in the wake of earning their certification.

5 – Pricing and Affordability

Money is always a challenge when it comes to education. Some universities charge tens of thousands of euros for their courses, which is fine if you can commit time and money to a full-time educational experience. It’s not so fine if you’re working on a budget. Your course’s cost plays a huge role in determining whether you take it. Just remember one thing – people tend to get what they pay for (for better or worse).

Top AI and ML Online Courses

Machine learning and artificial intelligence courses run the gamut from fast, industry-led courses designed to get you into a job to deeper degrees designed to equip you with everything you need to advance in your career. The following four are some of the best AI and ML courses online.

Course 1 – Master in Applied Data Science & AI (OPIT)

Designed for those at the postgraduate level, this Master’s degree requires you to have a background in computer science (or a relevant alternative). It’s a 100% online course that delivers an accredited degree under the European Qualification Framework (EQF), with the course also counting toward the college credits you may need to apply for future courses. Tutors are available for direct learning 24/7 and you learn via both recorded and live content delivered over the web.

Key Features and Benefits

• Offers tons of exposure to how machine learning and artificial intelligence apply in real-world scenarios
• You get a Master’s degree from a fully-accredited institution
• Favors progressive assessments over high-stress exams
• Control your own learning by arranging the course’s modules around your schedule

Enrollment Details

OPIT’s Master in Applied Data Science & AI comes in two flavors – the regular 18-month variety and a fast-tracked 12-month course. Enrollment is annual, with intake occurring every October, and the price varies depending on when you apply. Early birds get an extensive discount, paying €4,950 compared to the regular price of €6,500. You’ll need a relevant Bachelor’s degree in a subject like computer science to apply.

Course 2 – Machine Learning Introduction for Everyone (IBM via Coursera)

If OPIT’s Master’s degree is for people who are already halfway through the metaphorical marathon of machine learning and AI, IBM’s beginner’s course is for those at the starting line. It’s a seven-hour course that teaches the basics of AI and ML, in addition to helping you get to grips with the development cycle for a machine learning model. As a primer for the concepts, it’s one of the best AI ML online courses available.

Key Features and Benefits

• Provided by a Fortune 50 company that’s one of the leaders in the AI field
• Created by a Senior Data Scientist who currently works for IBM
• You receive a sharable certificate that looks great on your LinkedIn profile
• No completion of other AI machine learning courses is required to apply

Enrollment Details

“Free” is always a nice price tag to see on anything, and that’s what you get with this course, at least when trialing the course. Enrolment is semi-regular, with batches of students accepted every few months, and you get to reset deadlines based on when you can complete its modules. IBM says the course contains seven hours of content. Your experience may vary depending on how quickly (or otherwise) you adapt to the content.

Course 3 – Post Graduate Program in AI and Machine Learning (Purdue University)

Career Karma ranks this as one of the best AI ML courses online, and it’s hard to argue given that this is a near-year-long course offered with backing from industry professionals at IBM. It’s more bootcamp than formal course, though, so expect to be put through your paces with intensive hackathons and sprints that cover a huge number of AI tools. Combine that with real-world projects (using datasets from companies like Twitter and Uber) and you have a fast-paced and valuable course.

Key Features and Benefits

• Any extremely modern curriculum that takes in real-world examples from tech industry giants
• Backed by IBM to further the real-world experience delivered
• You receive a postgraduate certificate from an established university
• The online bootcamp experience is great for people who prefer fast-paced and intensive learning

Enrollment Details

Enrollment is set for May of each year, with the course lasting for 11 months thereafter. You’ll need to hit some criteria to apply. The course asks for a minimum of a Bachelor’s degree where you’ve obtained at least 50% on your modules, as well as a couple of years of work experience. That work experience requirement may be an issue for people who haven’t started their careers. Still, it’s a cost-effective program, with the course costing £2,990 (approx. €3,400).

Course 4 – Machine Learning Crash Course (Google AI)

If time is of the essence and you just want a crash course in what machine learning is and how it applies to your business, Google provides the answer with this option. At just 15 hours, it’s a course you can complete over an intensive weekend of study. It’ll introduce you to some real-world case studies, with lectures coming directly from industry heads at Google.

Key Features and Benefits

• Contains 25 lessons (with 30 exercises) to expand and test your knowledge
• Get industry insight from Google experts who work in the AI and ML fields
• You don’t have to pay a euro to take part in this course
• Includes interactive visualizations of real-world models that are great for tinkerers

Enrollment Details

Google presumes no prior knowledge of machine learning in this course, though it recommends that you’re comfortable with programming in Python and understand complex statistical concepts. Knowledge of the NumPy library is especially helpful. Assuming you build up a knowledge base (Google offers other courses to cover these foundations), you can enroll at any time and get a free course that you can fit around your schedule.

Additional Resources for AI and ML Learning

Great AI ML courses can teach you the fundamentals and offer direct experience, ideally coming from professionals in the industry. But it’s what you do outside of your formal and certified studies that can make the biggest difference to your career prospects. These additional resources both supplement what you learn from the above courses and allow you to continue developing your skills once you have your shiny new certificate:

• Online forums and communities
• Podcasts and YouTube channels dedicated to machine learning and AI
• Books and eBooks
• Conferences, workshops, and career-centric bootcamps

Use AI & ML Courses Today to Benefit Tomorrow

Consider these facts if you need any more convincing that AI and machine learning courses are right for you. The average machine learning engineer earns between €66,585 and €118,169 per year, with jobs in AI easily climbing into the six-figure range as well. Your career prospects get a boost when you study AI and ML. But remember – a certification alone is not enough.

These are fast-evolving fields, and only those who dedicate themselves to continued learning (and the adaptation that comes with market changes) excel. Start your journey with one of the four courses in this article and then continue down the educational path.

It can often feel like a computer has a “brain,” especially given modern machines’ abilities to run complex calculations and handle instructions. But all of those machines need people behind them to program algorithms and help them to learn based on explicit instructions. That’s where machine learning comes in.

This branch of artificial intelligence brings a machine’s “brain” closer to the real thing than ever before. It’s all about teaching the machine how to do more than simply execute, as machine learning is all about making a machine “think” (based on instructions and algorithms) so it can improve over time. That ability to “think” is crucial in modern business because it gives companies the ability to analyze patterns – both operational and consumer-based – enabling them to make smarter decisions.

But these businesses need people who understand how to create machine learning models. That’s where you come in. With the right machine learning tutorial under your belt, you set yourself up for a career in a field that has only just started to show glimpses of its potential.

The Best Machine Learning Tutorials

Finding the best online tutorial for machine learning isn’t easy given the sheer volume of options available. Analyzing each one based on what it teaches (and how useful it will be to your career) takes time, though you can save yourself that time by checking out the three tutorials highlighted here.

Tutorial 1 – Intro to Machine Learning (Kaggle)

As tempting as it may be to run before you can walk, you need an introduction to the basic concepts of machine learning prior to focusing on more practical applications. Enter Kaggle’s machine learning tutorial. This seven-lesson course takes about three hours of self-guided learning to complete and will leave you with a solid grounding in machine learning that you can take into more industry-focused courses.

The majority of the seven lessons – barring the first – is split into two parts. First comes a tutorial where you’ll learn about the concepts that the lesson introduces, with the second part being an exercise that tests your new skills. Along the way, you’ll learn the basics of how machine learning models work and why you need them to explore large datasets. Other lessons focus on building and validating a model, with the later lessons introducing more complex algorithms, such as random forests, and giving you a chance to test your skills in competitions.

Though this is a beginner-focused tutorial, you’ll need a solid understanding of Python before making a start. Without experience in this programming language, you’ll feel like you’re truly lost in a random forest before you ever get to learn what that term actually means. On the plus side, the tutorial has an active discussion community (which includes the course instructor Dan Becker) that can help you along and point you in the direction of other courses that supplement this one.

Tutorial 2 – Making Developers Awesome at Machine Learning (Machine Learning Mastery)

This machine learning tutorial is less a structured course and more a series of articles and step-by-step instructional lessons that take you from the foundations of machine learning to more advanced concepts. That method of breaking the course into multiple stages is ideal for students of all experience levels. Complete beginners can start with the “Foundations” level and work their way up while those with more experience can dip into specific subjects that give them trouble or will build on their existing skills.

The course is split into four sections – Foundations, Beginner, Intermediate, and Advanced. At the Foundations level, you’ll learn about the statistical concepts and models that underpin machine learning, giving you a solid basis to move into the Python programming taught in the Beginner section. Once you have a grasp of Python, the Intermediate section teaches you about deep learning and how to code machine learning algorithms. By the time you hit the Advanced stage, you’ll be working on complex subjects like computer vision and natural language processing.

With its less structured nature, this tutorial is great for people who want to dip in and out and those who need to hone in on a specific aspect of machine learning. It’s also a good choice for beginners because it covers practically everything you’ll need to know. Unfortunately, the lack of structure means you don’t get an official certification from the tutorial. Some students may also not like the “hub” nature of the tutorial, as it links you to tons of different web pages that can lead to confusion over time.

Tutorial 3 – Machine Learning Crash Course With TensorFlow APIs (Google)

If you already have a mathematical foundation (as well as some basic understanding of machine learning), Google’s tutorial helps you take your skills to the next level. You’ll need to understand algebra, statistics, and basic trigonometry, in addition to having some understanding of Python, to get started. But assuming you have all of that, this machine learning tutorial exposes you to real-world examples of the technology in action.

It’s a 25-lesson course that contains 30 exercises covering topics like model development and testing, data representation, and building neural networks. According to Google, it takes about 15 hours of self-guided study to complete, though your time may vary depending on how much you already know before you start the course.

The biggest advantage of this tutorial is the name attached to it. Google is a major player in the tech industry and the presence of its name on your CV instantly shows employers that you know your stuff. The course material is also delivered by lecturers who work at or for Google, allowing them to bring their real-world experiences into their lessons. On the downside, the tutorial’s prerequisites make it unsuitable for beginners, though Google does offer more basic courses (both in machine learning and Python) to help you build the required foundation.

Factors to Consider When Choosing a Machine Learning Tutorial

The three options presented above all make a solid case for the best online tutorial for machine learning, though each offers something different based on your current skill level. To make the best choice between the three (and any other tutorials you find) you should consider these factors before committing yourself.

Your Current Skill Level

Diving into neural networks before you even know how machine learning works is like trying to row upstream without a paddle. You’re going to get stuck in rough waters and the end result won’t be what you want it to be. Be honest with yourself about your current skill level to ensure you don’t start a tutorial that’s too difficult (or too simple) for your abilities.

Programming Languages

There’s no getting away from the fact that you’ll need to feel comfortable with programming before taking a machine learning tutorial. Specifically, you’re likely to need some knowledge of Python, though how much depends on the course you take. Other languages can help, at least in the sense of ensuring you’re familiar with programming, but you need to check the language the course uses before starting.

Specific Topics

Though the basic idea of building a machine “brain” is simple enough to understand, the machine learning waters run deep. There are tons of topics and potential specializations you could study, and not all are useful for your intended career path. Check what the course covers and ensure those topics align with what you hope to achieve once you’ve completed the tutorial.

Time Commitment

If a tutorial takes an hour or two to complete, you don’t really need to worry about how you’ll fit it around your other commitments. But if it takes you down a machine learning rabbit hole (i.e., the Machine Learning Mastery Course), you need to get serious with scheduling. Figure out how much time you can commit to your course per week and choose a tutorial that fits around your commitments.

The Cost

On the plus side, many machine learning tutorials are available free of charge. But if you’re looking for more official certification, or you want to take a more formal course, you’ll usually have to pay for the privilege. Weigh up the course’s cost against the benefit you get out of the backend.

Tips for Getting the Most Out of a Machine Learning Tutorial

Anybody can start a machine learning tutorial, but only the truly committed will complete and actually get the most out of the materials. Follow these tips to ensure you’re spending your time wisely on the tutorial you choose:

• Set clear goals from the outset that define what you want to achieve with the tutorial and where it’s supposed to lead you.
• Dedicate time to learning every week because regularity is the key to making the information you absorb stick in your mind.
• Engage with any communities related to your tutorial to learn from your peers and ask questions about the tutorial’s content.
• Apply what you learn to real-world problems, either via the course itself or by searching for examples of what you’ve learned being put into action.
• Update your knowledge and skills regularly with further tutorials because what you learn today may be out of date tomorrow.

Find the Best Online Tutorial for Machine Learning for You

There is no single “best” machine learning tutorial on the web because each approaches the subject differently. Some assume you have no knowledge at all and will start with basics before moving you into deeper subjects. Others require you to understand the computing concepts (mathematical and programmatical) that underpin machine learning before you can get started. Understand what the course offers, and what it needs from you, before you get started.

Regardless of your choice, getting started is the most important thing you can do. Once you’ve chosen a tutorial, commit yourself to it fully to take your first step (or potentially a giant leap) into a career that’s only going to grow as machine learning models become more common in business.

Data science is likely the most sought-after profession today. With top tech organizations looking for talent across the world, this field is highly competitive. That’s why professional improvement represents a crucial aspect of this rapidly-evolving industry.

Getting an approved certificate is the best way to gain the necessary knowledge and a confirmation of your data science skills. This article will give you a list of the 10 best online courses and data science certificate programs that offer worldwide recognized certification.

Factors to Consider When Choosing a Data Science Certification Course

There’s plenty of criteria to look at when choosing a data science certification course online. Of course, the content of the course will be of most interest, especially since data science is a broad field. But several other aspects are also worth researching:

• Program duration
• Flexibility – is it on a fixed timeline or self-paced
• Instructor quality and the reputation of the institution
• Pricing
• Whether the program offers practical projects and hands-on work
• Whether the institution will help you land your next job

Top 10 Online Data Science Certification Courses & Programs

Here’s a brief overview of what the top online courses in data science have to offer. Courses and programs on our list come from respected institutions that hire world-class lecturers and will provide the best certification for data science you could get without setting foot on campus.

Harvard University – Professional Certificate in Data Science

Getting an education in data science from Harvard University is one of the best options in the market. This online course teaches essential skills in programming, modeling, statistics, data visualization, and numerous data science tools.

The Professional Certificate in Data Science course is self-paced and represents an introductory course tailored for beginners who want to advance their skills. You’ll also learn through relevant case studies by analyzing data from real-life examples. The program includes working in the R environment.

The price of this Harvard program is $991, with an available 10% discount. The course runs through the edX platform, and allows you free access to the entire curriculum at your leisure. If you decide for the minimal 2-3 hour weekly commitment, the certificate will take roughly 17 months to complete.

Cloudera – Data Platform Generalist Certification

The Data Platform Generalist test by Cloudera is excellent because it enables learners to take various roles within the data science industry. While the exam focuses on Cloudera’s data platform, the program certifies you as a general data science professional, meaning you can pursue a career in data engineering and analytics, development, administration, and similar fields.

The certification consists of a single 90-minute exam with 60 questions. Cloudera doesn’t state the minimal score needed to pass the exam because the point of the certification program is to do it the best you can rather than aiming for a specific score.

According to the Cloudera website, this certification costs $330. Upon completing the exam, you’ll get a certificate that lasts for two years.

IBM – Data Science Professional Certificate

As one of the industry leaders, IBM provides an exceptional course in data science. The course teaches the basics of data science, focusing on the work methodology via Python and SQL. The Data Science Professional Certificate program helps beginners in the field via hands-on work, with exercises in data set importing, analysis, cleaning, and visualization.

The online certificate course in data science consists of 10 parts. After the first three introductory courses, the following six focus on working in Python, while the final one deals with applied data science. This is a flexible, self-paced program suitable for beginners.

Enrolling in this IBM data science program is free via Coursera, provided you have a monthly subscription. The courses require about three hours of work per week. At that tempo, you should complete the program and receive your data science certification within five months.

Data Science Council of America – Senior Data Scientist

As the name implies, the Data Science Council of America (DASCA) counts among the leading authorities on data science in the U.S. and worldwide. The Senior Data Scientist program enjoys global recognition and takes place entirely online.

This program provides excellent resources that candidates can use to prepare for the exams. Plus, the resources are quality reading for the purposes of professional improvement. The learning material and the program itself are suitable for more experienced learners.

Upon enlisting, you’ll need to cover a one-time fee of $775. Once you receive the resources, you’ll have six months to prepare for the exam. The recommended study time is up to 10 hours weekly.

John Hopkins University – Data Science Specialization

When a reputable institution like the John Hopkins University offers a specialization in data science, there’s no reason to miss that opportunity. Hosted by Coursera, this Data Science Specialization course is built around practical applications of actual data.

The online program provides learners the chance to create a genuine data product. Along with learning, you’ll also be building a respectable portfolio that will come in handy as a demonstration of your newly acquired skills.

Like other Coursera programs, this specialization is also free with a subscription to the service. The program is flexible in terms of time commitment. If you devote an hour a day to it, you can complete the specialization in about 11 months.

Microsoft – Azure AI Fundamentals

Microsoft has proven to be not only a tech giant but an excellent knowledge hub. With Azure AI Fundamentals, this renowned company offers expertly crafted training in the basics of working with artificial intelligence. Through this certification program, learners can gain a thorough understanding of AI and become skilled in the latest technologies.

This online data science certificate course will be suitable even for complete beginners, although a basic level of programming skills would give you an easier start. The program comes in two variants: self-paced and led by a professional instructor.

The program costs only €99 and awards a permanent Microsoft certificate. You can also try out the course with a trial subscription, and there’s an available practice assessment test that will help you understand where you stand before enrolling.

MIT – MicroMasters Program in Statistics and Data Science

If you’re looking for an intensive program that will teach you advanced data science skills, MIT has just the thing. The MicroMasters in Statistics and Data Science is a result of a collaboration between the world-renowned MIT and edX, a trusted learning platform.

This program includes working on data sets from real-world examples, as well as understanding the leading machine learning models. Upon finishing, candidates will be eligible for different titles within the field of data science.

The program consists of five courses and may last up to 14 months with about 14 hours of weekly engagement. The edX platform lists the program price at $1,350.

Open Group – Certified Data Scientist

The Open Group consists of numerous global organizations, with some of the most distinct members being technology giants like IBM, Intel, Fujitsu, and Huawei. The Certified Data Scientist certification that the group provides is a credential recognizes around the globe.

The structure of this program is quite unique. It doesn’t include courses or exams. Instead, applicants need to demonstrate practical data science skills in written form. The point of this certification isn’t to educate, but rather to verify the candidate’s professional capabilities.

The time needed to get the certificate will vary depending on your proficiency level. The certificate is permanent, and Open Group discloses its price via contact.

Stanford University – Machine Learning Certification

Stanford University is home to some of the world’s finest lecturers. The institution provides a machine learning program in collaboration with Coursera and, as a practical, hands-on experience, it’s something eager learners shouldn’t miss.

The Machine Learning Certification is an ideal opportunity for beginners to grasp the intricacies of advanced AI and its applications. The program consists of three courses. By the end of the third course, the applicant should be able to build Python machine learning models from the ground up.

Following Coursera’s standard model, this program is free to enroll into, provided the user has a Coursera subscription. With up to nine hours of work weekly, the program shouldn’t last more than three months.

SAS – Certified AI and Machine Learning Professional

SAS is a certification program that operates globally. It offers a Certified AI and Machine Learning Professional program that’s built for people looking for top practical education in these areas. As the name says, this certification is aimed at future data science professionals.

The program includes five courses after which attendees get permanent certification. Upon registering, learners will receive a full year of access to the complete course material, as well as 70 hours of complimentary software use via cloud.

This program is self-paced, but you have to complete it within one year. The price for one year is €1,295.

Tips for Success in Data Science Certification Courses

Enrolling in a data science course is only a part of the process. To be successful, you’ll need to do your best and employ certain techniques:

1. Manage your time effectively. Make sure to commit enough time to progress through the course and meet requested deadlines.
2. Start building a network with your peers from day one. Collaborate with people who share your interest in data science so that you can build off of each other.
3. Never assume you’ve learned everything there is to know. Data science is evolving constantly, and there’s always new skills to develop and additional knowledge to gain.
4. Build a strong portfolio that will increase your chances of finding a job in the field. The best data science certification programs represent an ideal start.

Get Certified in One of the Top Professions Today

Getting a data science certificate online can open up a career path in a top-paid profession that continues to grow. With certification from one of the leading institutions in the field, you’ll be on the right track to success.

Our list contains programs and courses from renowned organizations like Harvard, IBM, MIT, and Microsoft. The quality of lecturers is unquestionable, and the programs offer the most up-to-date courses. Whichever certification you choose, you can rest assured you’ll be the best data science certification online.

In a world of Big Data, companies need people who have the ability to analyze and reach conclusions from the reams of data they collect about customers. But data science extends far beyond the corporate. Any industry that uses data (i.e., practically all of them) needs data-minded people who can use the latest AI-driven tools to help them scour large datasets.

That’s where you come in. As a potential data scientist, you’ll enter an industry that’s experiencing enormous growth to the point where it will be worth $103 billion (approx. €96.37 billion) by 2027. That market growth translates into demand for talented data scientists, which is already seen today as Coresignal’s data – 8,000 available job postings across eight leading positions in the first five months of 2022 alone – demonstrates.

So, the benefits of earning a free data science certification are obvious – you’re entering a growing industry with huge demand that leads to a better salary. But you need to know which courses will help you break into that industry. This article highlights four of the best free data science courses around.

Top Four Free Data Science Courses

As wonderful as the word “free” may be to budget-conscious students, you still need to know that you’re getting something of value from your data science course. The following options deliver a stellar educational experience and leave you with a qualification that employers recognize.

An Introduction to Data Science (Udemy)

Every journey starts with a first step, and it’s crucial that you take the first step into data science with a course that covers the basics and lays a foundation on which you can build. An Introduction to Data Science does just that by teaching you what data science is and how it applies to the modern world.

That teaching starts with a history lesson that shows how interactions with data (and data collection methods) have evolved over the years. From there, you’ll learn how data science applies in modern industry and discover the difference between actual valuable data and the oodles of “noise” that are in datasets.

It’s a quick and easy course, weighing in at 43 minutes spread across six video lectures, so you don’t have to make a huge time commitment. It’s delivered online by a Google Certified Python Expert named Kumar Rajmani Bapat and is ideal for getting the basics of data science down before you move on to a more intensive or focused course.

But the focus on the basics is also the biggest issue with this course. Rather than showing you the techniques a data scientist uses, the course focuses on what data science is and offers a roadmap for getting into the industry. It’s more about “what” than “how,” which may make the course too rudimentary for people who already have some knowledge of the subject. It’s also worth noting that this isn’t one of those free data science courses with certificate, as you’ll need to pay for an Udemy subscription to get your hands on a certificate of completion. You can still watch the videos and complete the course for free, though.

Introduction to Data Science (SkillUP)

With a similar name to the above Udemy course, you’d be forgiven for assuming that SkillUP’s Introduction to Data Science program teaches the same stuff. Though the course is aimed squarely at beginners, it takes a more in-depth approach that makes it the ideal follow-up to Udemy’s offering.

You start with the basic spiel about what data science is and how it applies to modern industry. But from there, the course tips into actual application by demonstrating some of the best Python programming libraries to use in the field. You’ll also dig deep into the algorithms used in data science, with linear regression analysis, confusion matrices, and logistic regression all getting some time to shine.

Given this higher focus on the skills you’ll need to learn to become a data scientist, the course is longer than Udemy’s offering. It clocks in at seven hours of videos and tutorials, all of which you access online and work through at your own pace. The course also expects you to have a solid grasp of math and programming (some experience with Python is a must) so this isn’t ideal for complete beginners to computer science.

This is a data science free online course with certificate, though there is a caveat. SkillUP only provides 90 days of free access to the course. If you feel it will take longer than that to get through the seven hours of tutorials, you’ll need to enroll in a paid subscription. The best approach here is to only start the course when you’re confident that you can block out the time needed to wrap it up within 90 days.

IBM Data Science Professional Certificate (Coursera)

Aimed squarely at the career-focused individual, IBM’s data science course is all about building the skills that set you on the right path to a career in the field. It takes a more practical approach, starting you off with the fundamentals before pushing you into a project where you’ll work with a real-world dataset and publish a report that’s analyzed by stakeholders simulating what you’ll experience in the working world.

The good news is that you don’t need to know anything about data science to get started with the course. It holds your hand as you learn the basics of what data science is (including what a data scientist actually does) and teaches you about the tools and programming languages you’ll use in the field. Once you have a grasp on the fundamentals, you’ll learn how to analyze and visualize data, in addition to creating machine learning models using Python, before wrapping up with the previously mentioned project.

The IBM Data Science Professional Certificate is a more intensive course than the others on this list. It’s essentially a mini degree, requiring you to invest 10 hours per week for five months into your learning. However, the course is provided entirely online, allowing you to schedule that learning time as you see fit. You’ll work through 10 modules as part of the certificate.

That time commitment may be a downside for those who can’t put 10 hours per week into a course, though that downside is outweighed heavily by the fact that you come out with an IBM certification. Having one of the leading names in computing on your certificate is enough to make any employer sit up and take notice.

Data Analysis With Python (freeCodeCamp)

The Python programming language (along with SQL and a few others) underpins almost everything that the modern data scientist does. Data Analysis with Python takes that concept and runs with it by providing a course that digs into using Python to read, analyze, and visualize data.

Along the way, you’ll learn about the basics of both Python and data analysis, though the real highlight comes from the many libraries and tools the course introduces. You’ll use Seaborn, Numpy, Mayplotlib, and Pandas during the course. All of which are libraries used by professionals to extract and visualize data. The course wraps up with a series of five projects, each testing a different set of skills learned via the modules, with your certification coming after you’ve completed all five.

This is one of those free data science courses that’s entirely self-paced and there are no time constraints or commitments involved. Once you’ve signed up for freeCodeCamp, you can save your progress through the course at any point and return whenever you’re ready. Theoretically, this means you could start the course, save your progress, and then return to it months later, though that isn’t recommended if you want to keep the information fresh in your mind. All told, the course contains 37 modules, plus the five projects required for certification, making it one of the most in-depth Python courses around.

The focus on Python is great for those who are unfamiliar with the language, though it also creates some issues. Namely, this isn’t the right course for those who don’t understand data science fundamentals. It jumps straight into analyzing datasets using Python, so those who don’t really understand what datasets are or how they apply to the modern world should start with a more beginner-oriented course.

Tips for Choosing the Right Data Science Course

You get the same benefit from all of the listed data science online courses – free entry. But each course offers something different. Use these tips to determine which is the right choice for you:

• Assess your current skill level to pick a course that delivers what you need to know right now rather than a course that forces you to run before you can walk.
• Determine your learning goals so you can see how the course fits into your roadmap for becoming a data scientist.
• Consider the course’s format and duration as both will play a huge role in how you schedule your learning around your other commitments, be they work-related or personal.
• Look for courses that offer hands-on project work once you’ve moved beyond learning the basics of data science.
• Read reviews and testimonials from other students to see if people in your position get actual value from the course.

Start Your Journey With Free Data Science Courses Online

Every journey starts with a first step, and that first step could take you into a career that has massive potential for growth if you opt for the data science path. The four courses listed here each offer something different, from exploring the basics of what data science is to digging deep into the programming tools you’ll use to conduct data analysis and visualization. Completing one of the four sets you on the right path, though completing all four gives you a solid grounding (and a set of certifications) that make you immensely attractive to employers.

Large portions of modern life revolve around computers. Many of us start the day by booting a PC and we spend the rest of our time carrying miniaturized computer devices around – our smartphones.

Such devices rely on complex software environments and programs to meet our personal and professional needs. And computer science deals with precisely that.

The job of a computer scientist revolves around software, including theoretical advances, software model design, and the development of new apps. It’s a profession that requires profound knowledge of algorithms, AI, cybersecurity, mathematical analysis, databases, and much more.

In essence, computer science is in the background of everything related to modern digital technologies. Computer scientists solve problems and advance the capabilities of technologies that nearly all industries utilize.

In fact, this scientific field is so broad that explaining what is computer science requires more than a mere definition. That’s why this article will go into considerable detail on the subject to flesh out the meaning behind one of the most important professions of our time.

History of Computer Science

The early history of computer science is a fascinating subject. On the one hand, the mechanics and mathematics that would form the core disciplines of computer science far predate the digital age. On the other hand, the modern iteration of computer science didn’t start until about two decades after the first digital computer came into being.

When examining the roots of computer science, we can go as far back as the antiquity era. Mechanical calculation tools and advanced mathematical algorithms date back millennia. However, those roots are too loosely connected to computer science.

The first people who started exploring the foundations of what is computer science today were Wilhelm Schickard and Gottfried Leibniz in early and late 17th century, respectively.

Schickard is responsible for the design of the world’s first genuine mechanical calculator. Leibniz is the inventor of a calculator that worked in the binary system, the universally known “1-0” number system that paved the way for the digital age.

Despite the early advances in the mentioned fields, it would be another 150 years after Leibniz before mechanical and automated computing machines saw industrial production. Yet, those machines weren’t used for any other purpose apart from calculations.

Computers became more powerful only in the 20th century. Like many other technologies, this branch saw rapid development during the last one hundred years, with IBM creating the first computing lab in 1945.

Yet, while plenty of research was happening, computer science wasn’t established as an independent discipline. That would take place only during the 1960s.

Early Developments

As mentioned, the invention of the binary system could be considered a root of computer science. This isn’t only due to the revolutionary mathematical model – it’s also because the binary number system lends itself particularly well to electronics.

The rise of electrical engineering moved forward inventions like the electrical circuit, the transistor, and powerful data storage solutions. This progress gave birth to the earliest electrical computers, which mostly found use in data processing.

It didn’t take long for massive companies to start using the early computers for information storage. Naturally, this use made further development of the technology necessary. The 1930s saw crucial milestones in computer theory, including the groundbreaking computational model by Alan Turing.

Not long after Turing, John von Neumann created a model of a computer that can store programs. By the 1950s, computers were in use in complex calculations and data processing on a large scale.

The rising demand made the binary machine language too unreliable and impractical. The successor, the so-called assembly language, soon proved just as lacking. By the end of the decade, the world saw the first program languages, which soon became the famed FORTRAN (Formula Translation) and COBOL (Common Business Oriented Language).

The following decade, it became obvious that computer science is a field of study in itself, rather than a subset of mathematical or physical disciplines.

Evolution of Computer Science Over Time

As technology kept progressing, computer science needed to keep up. The first computer operating systems came about in the 1960s, while the next two decades brought about an intense expansion in graphics and affordable hardware.

The combination of these factors (OS, accessible hardware, and graphical development) led to advanced user interfaces, championed by industry giants like Apple and Microsoft.

In parallel to these discoveries, computer networks were advancing, too. The birth of the internet added even more moving parts to the already vast field of computer science, including the first search engines that utilized advanced algorithms, albeit not at the same level as today’s engines.

Furthermore, greater computational capabilities created a need for better storage systems. This included larger databases and faster processing.

Today, computer science explores all of the mentioned facets of computer technology, alongside other fields like robotics and artificial intelligence.

Key Areas of Study in Computer Science

As you’ve undoubtedly noticed, computer science grew in scope with the development of computational technologies. That’s why it’s no surprise that computer science today encompasses many areas that deal with every aspect of the technology currently imaginable.

To answer the question of what is computer science, we’ll list some of the key areas of this discipline:

1. Algorithms and data structures
2. Programming languages and compilers
3. Computer architecture and organization
4. Operating systems
5. Networking and communication
6. Databases and information retrieval
7. Artificial intelligence and machine learning
8. Human-computer interaction
9. Software engineering
10. Computer graphics and visualization

As is apparent, these areas correspond with the historical advances in computational technology. We’ve talked about how algorithms predate the modern age by quite a lot. These mathematical achievements brought about early machine languages, which turned into programming languages.

The progress in data storage and the increased scope of the machines resulted in a need for more robust architecture, which necessitated the creation of operating systems. As computer systems started communicating with each other, better networking became vital.

Work on information retrieval and database management resulted from both individual computer use and a greater reliance on networking. Naturally, it didn’t take long for scientists to start considering how the machines could do even more work individually, which marked the starting point for modern AI.

Throughout its history, computer science developed new disciplines out of the need to solve existing problems and come up with novel solutions. When we consider all that progress, it’s clear that the practical applications of computer science grew alongside the technology itself.

Applications of Computer Science

Computer science is applied in numerous fields and industries. Currently, computer science contributes to the world through innovation and technological development. And as computer systems become more advanced, they are capable of resolving complex issues within some of the most important industries of our age.

Technology and Innovation

In terms of technology and innovation, computer science finds application in the fields of graphics, visualization, sound and video processing, mathematical modeling, analytics, and more.

Graphical rendering helps us visualize concepts that would otherwise be hard to grasp. Technologies like VR and AR expand the way we communicate, while 3D models flesh out future projects in staggering detail.

Sound and video processing capabilities of modern systems continue to revolutionize telecommunications. And, of course, mathematical modeling and analytics expand the possibilities of various systems, from physics to finance.

Problem-Solving in Various Industries

When it comes to the application of computer science in particular industries, this field of study contributes to better quality of life by tackling the most challenging problems in key areas:

• Healthcare
• Finance
• Education
• Entertainment
• Transportation

Granted, these aren’t the only areas where computer science helps overcome issues and previous limitations.

In healthcare, computer systems can produce and analyze medical images, assisting medical experts in diagnosis and patient treatment. Furthermore, branches of computer science like psychoinformatics use digital technologies for a better understanding of psychological traits.

In terms of finance, data gathering and processing is critical for massive financial systems. Additionally, automation and networking make transactions easier and safer.

When it comes to education and entertainment, computer science offers solutions in terms of more comprehensible presentation, as well as more immersive experiences. Many schools worldwide use digital teaching tools today, helping students grasp complex subjects with fewer obstacles compared to traditional methods.

Careers in Computer Science

As should be expected, computer science provides numerous job opportunities in the modern market. Some of the most prominent roles in computer science include systems analysts, programmers, computer research scientists, database administrators, software developers, support specialists, cybersecurity specialists, and network administrators.

The mentioned roles require a level of proficiency in the appropriate field of computer science. Luckily, computer science skills are easier to learn today – mostly thanks to the development of computer science.

An online BSc or MSc in computer science can be an excellent way to get prepared for a career in the most sought-after profession in the modern world.

On that note, not all computer science jobs are projected to grow at the same rate by the end of this decade. Profiles that will likely stay in high demand include:

• Security Analyst
• Software Developer
• Research Scientist
• Database Administrator

Start Learning About Computer Science

Computer science represents a fascinating field that grows with the technology and, in some sense, fuels its own development. This vital branch of science has roots in ancient mathematical principles as well as the latest advances like machine learning and AI.

There are few fields worth exploring more today than computer science. Besides understanding our world better, learning more about computer science can open up incredible career paths and provide an opportunity to contribute to resolving some of the burning issues of our time.