As a well-known programming language, Python dominates the data science field. Its prominence in the industry represents the main reason why so many job offers include Python skills as a hard requirement.
Of course, all of the hype around Python has practical ramifications. This programming language is suitable for people without a programming background. If you have a sufficient grasp of technology, chances are you’ll get how Python works in a few weeks.
Besides being beginner-friendly, Python is practically built for math and statistical analysis. Plus, data visualization becomes nearly effortless when you use specific Python libraries dedicated to the task.
The point is that Python makes numerous data science tasks and operations easier. If you’re interested in data science, learning this versatile programming language will take your professional development to a new level.
Fortunately, you can find plenty of courses teaching everything from the basics to advanced functions in Python. Let’s look at the best Python data science tutorial and course options.
Factors to Consider When Choosing a Python Data Science Course
Before you start a particular course, it would be best to consider the specifics. The criteria that should guide your decision include:
- The content of the course: Some courses will be introductory, while others will offer advanced lessons. You should start with a course that aligns with your proficiency level.
- Instructor’s expertise: Ideally, you’ll want an industry expert to teach you about Python. Experienced lecturers or proven professionals will know all of the ins and outs, and they’ll be able to transfer that knowledge to you.
- Course duration and flexibility: If you’re looking for a course, you don’t want an experience that will last an entire year. On the other hand, you shouldn’t expect too much from an hour-long course. Additionally, the course structure should be flexible enough to allow you to complete it at your own pace.
- Practical projects and applications: Python is a living programming language that sees plenty of use in the real world. On that note, the course you take should offer a hands-on experience and show you how to apply your new knowledge in practice.
- Course reviews and ratings: Although this shouldn’t be your primary clue when making a decision, taking a look at what others say about the course certainly won’t hurt. You’ll want to stay away from courses with mostly negative reviews, especially if the reviewers make unsubstantiated claims.
- Pricing and value: Course pricing may vary from ludicrously expensive to free. While our list doesn’t include any outrageously overpriced courses, you’ll find a quality free one in there. The bottom line here is straightforward: Does the course fit in with your budget and what do you get for the price?
Top Python Data Science Courses and Tutorials
ILX Group – Python E-Learning
This Python data science course deals with the basic functionality of the programming language and teaches you how to apply it in practice. It contains in-depth information about command running, dictionaries, methods, and shell scripting. No final exam is necessary to complete the course.
Key Topics
- The basics of Python programming
- File and data operations
- Logging and test infrastructure
- Conditional statements
- Networking
- Shell scripting
- Django web framework
Instructor’s Background
Information about the instructor for this course isn’t available on ILX Group.
Course Duration and Format
The course is in e-learning format and is delivered entirely online. It will take you about eight hours to complete. Instead of a final exam, you’ll complete the course by submitting the required project that must meet specific set criteria.
Pricing and Enrollment
Enrolling in this course will cost €450 +VAT. You won’t need to fulfill any additional requirements to make a start. Paying the one-time fee will grant you a full year of access to the course resources.
Pros
- Provides a solid foundation for Python programming
- No limitations on enrollment or availability
- Offers practical knowledge and projects
Cons
- E-learning tools used throughout the course aren’t defined
- No information about the instructor or their credentials
Python Institute – Data Analysis Essentials With Python
The Python Institute is a group devoted to Python education. The Data Analysis Essentials with Python is only one of the courses this institution provides. It’s an intermediate-level program focused on data analysis using the tools within the Python programming language.
Key Topics
- Data analysis
- Algorithmic and analytical thinking
- Data visualization
- Statistics
- Data mining and modeling
- Programming
- Data-based decision-making
Instructor’s Background
No instructor information can be found on the Python Institute site regarding this particular course. However, it’s worth mentioning that the institute is run by industry experts with substantial experience in the IT sector. These experts are also responsible for the institute courses.
Course Duration and Format
The Data Analysis Essentials with Python course will last for up to six weeks, provided you devote about eight hours weekly to studying the material. The course is delivered online.
Pricing and Enrollment
One of the greatest advantages of this course is its pricing: Data Analysis Essentials with Python is completely free. However, this course isn’t for beginners. You’ll need previous knowledge of the key concepts in Python programming. The Python Institute recommends completing their beginner courses or coming into this program with some experience.
Pros
- Course designed by industry professionals
- Free for all users
- May serve as a preparatory course for Python Certified Associate in Data Analytics (PCAD) certification
Cons
- No information about the lecturer
- Exact delivery methods aren’t specified
Python-Course – Fundamental Python Course
The Fundamental Python Course is designed as a comprehensive introduction to programming methods in Python. The course will take you through the fundamentals of the programming language and include practical solutions in the Python environment.
Key Topics
- Python introductory lessons
- Script editing and execution
- Working in the Python shell
- Expressions, operators, assignments, and variables
- Dictionaries, stacks, loops, and lists
- Handling files and exceptions
- Conditional statements
- Packages and modules
Instructor’s Background
The instructor for live courses is Bernd Klein. A Python expert with a Saarland University diploma in Computer Science, specializing in computer languages, Klein has taught at the Saarland University, EWH, Koblenz, and the University of Freiburg, where he still holds a teaching position.
Klein is also the founder of the programming language teaching platform, Bodenseo.
Course Duration and Format
The course lasts for five days and includes a live class format. While Klein usually holds classes in person, courses are currently provided online. To participate on this course, you’ll need a network-ready computer with a microphone. No additional software is needed.
Pricing and Enrollment
The on-site variant of the course costs €1,450 per day, while open classes start from €349 daily. There are no other requirements for the course.
Pros
- Taught by an experienced lecturer
- Offers a complete coverage of Python-related subjects
- Advanced optional topics
Cons
- Very pricey compared to the competitors
- Doesn’t provide a certificate
Additional Resources for Mastering Python Data Science
If you want an alternative to an actual Python data science course, you may wish to turn to other resources that will help you master the subject. In particular, these would be books and digital resources like forums, eBooks, podcasts, YouTube channels, websites, and blogs.
For some of the best Python forums and online communities, check out the following:
- Python org forums
- StackOverflow Python forum page
- FreeCodeCamp Python category
Great books on Python include:
- Head-First Python, by Paul Barry
- Think Python, by Allen B. Downey
- Learn Python 3 the Hard Way, by Zed A. Shaw
- Python Crash Course, by Eric Matthes
If printed media isn’t your style, you can find an excellent list of free Python eBooks on Codeburst.io.
On the other hand, you might not want to read too much while learning Python. In that case, you’ll be glad to learn that there are numerous podcasts on the subject that you can tune in to right now:
Unsurprisingly, YouTube also has plenty of Python data science course and tutorial channels. Here are our top picks:
- The New Boston
- Sentdex
- Real Python
- PyCon – This isn’t a particular YouTube channel, but rather a search query. Browse the search results on YouTube, and you’ll find videos for Python-dedicated conferences from around the world.
- Michael Kennedy
Finally, there’s an abundance of blogs and websites dedicated to Python resources and knowledge:
- Python Blogs
- The PyCharm Blog on Jet Brains
- The Invent with Python Blog
- The Python Library Blog
- Finxster
Learn to Program in Python Like a Pro
The internet is full of quality Python data science tutorial and course pages. You can find free and premium resources to hone your skills in the programming language or get familiar with the fundamental concepts.
Whichever resource type you choose, rest assured that learning practical Python skills will be a valuable addition to your resume. After all, data science is a constantly developing field in which expanding your knowledge base and skillset can only be a huge plus. If you’ve found a program you like in this article, don’t hesitate to jump right into it and expand your horizons.
Related posts
Source:
- The Yuan, Published on October 25th, 2024.
By Zorina Alliata
Artificial intelligence is a classic example of a mismatch between perceptions and reality, as people tend to overlook its positive aspects and fear it far more than what is warranted by its actual capabilities, argues AI strategist and professor Zorina Alliata.
ALEXANDRIA, VIRGINIA – In recent years, artificial intelligence (AI) has grown and developed into something much bigger than most people could have ever expected. Jokes about robots living among humans no longer seem so harmless, and the average person began to develop a new awareness of AI and all its uses. Unfortunately, however – as is often a human tendency – people became hyper-fixated on the negative aspects of AI, often forgetting about all the good it can do. One should therefore take a step back and remember that humanity is still only in the very early stages of developing real intelligence outside of the human brain, and so at this point AI is almost like a small child that humans are raising.
AI is still developing, growing, and adapting, and like any new tech it has its drawbacks. At one point, people had fears and doubts about electricity, calculators, and mobile phones – but now these have become ubiquitous aspects of everyday life, and it is not difficult to imagine a future in which this is the case for AI as well.
The development of AI certainly comes with relevant and real concerns that must be addressed – such as its controversial role in education, the potential job losses it might lead to, and its bias and inaccuracies. For every fear, however, there is also a ray of hope, and that is largely thanks to people and their ingenuity.
Looking at education, many educators around the world are worried about recent developments in AI. The frequently discussed ChatGPT – which is now on its fourth version – is a major red flag for many, causing concerns around plagiarism and creating fears that it will lead to the end of writing as people know it. This is one of the main factors that has increased the pessimistic reporting about AI that one so often sees in the media.
However, when one actually considers ChatGPT in its current state, it is safe to say that these fears are probably overblown. Can ChatGPT really replace the human mind, which is capable of so much that AI cannot replicate? As for educators, instead of assuming that all their students will want to cheat, they should instead consider the options for taking advantage of new tech to enhance the learning experience. Most people now know the tell-tale signs for identifying something that ChatGPT has written. Excessive use of numbered lists, repetitive language and poor comparison skills are just three ways to tell if a piece of writing is legitimate or if a bot is behind it. This author personally encourages the use of AI in the classes I teach. This is because it is better for students to understand what AI can do and how to use it as a tool in their learning instead of avoiding and fearing it, or being discouraged from using it no matter the circumstances.
Educators should therefore reframe the idea of ChatGPT in their minds, have open discussions with students about its uses, and help them understand that it is actually just another tool to help them learn more efficiently – and not a replacement for their own thoughts and words. Such frank discussions help students develop their critical thinking skills and start understanding their own influence on ChatGPT and other AI-powered tools.
By developing one’s understanding of AI’s actual capabilities, one can begin to understand its uses in everyday life. Some would have people believe that this means countless jobs will inevitably become obsolete, but that is not entirely true. Even if AI does replace some jobs, it will still need industry experts to guide it, meaning that entirely new jobs are being created at the same time as some older jobs are disappearing.
Adapting to AI is a new challenge for most industries, and it is certainly daunting at times. The reality, however, is that AI is not here to steal people’s jobs. If anything, it will change the nature of some jobs and may even improve them by making human workers more efficient and productive. If AI is to be a truly useful tool, it will still need humans. One should remember that humans working alongside AI and using it as a tool is key, because in most cases AI cannot do the job of a person by itself.
Is AI biased?
Why should one view AI as a tool and not a replacement? The main reason is because AI itself is still learning, and AI-powered tools such as ChatGPT do not understand bias. As a result, whenever ChatGPT is asked a question it will pull information from anywhere, and so it can easily repeat old biases. AI is learning from previous data, much of which is biased or out of date. Data about home ownership and mortgages, e.g., are often biased because non-white people in the United States could not get a mortgage until after the 1960s. The effect on data due to this lending discrimination is only now being fully understood.
AI is certainly biased at times, but that stems from human bias. Again, this just reinforces the need for humans to be in control of AI. AI is like a young child in that it is still absorbing what is happening around it. People must therefore not fear it, but instead guide it in the right direction.
For AI to be used as a tool, it must be treated as such. If one wanted to build a house, one would not expect one’s tools to be able to do the job alone – and AI must be viewed through a similar lens. By acknowledging this aspect of AI and taking control of humans’ role in its development, the world would be better placed to reap the benefits and quash the fears associated with AI. One should therefore not assume that all the doom and gloom one reads about AI is exactly as it seems. Instead, people should try experimenting with it and learning from it, and maybe soon they will realize that it was the best thing that could have happened to humanity.
Read the full article below:
Source:
- The European Business Review, Published on October 27th, 2024.
By Lokesh Vij
Lokesh Vij is a Professor of BSc in Modern Computer Science & MSc in Applied Data Science & AI at Open Institute of Technology. With over 20 years of experience in cloud computing infrastructure, cybersecurity and cloud development, Professor Vij is an expert in all things related to data and modern computer science.
In today’s rapidly evolving technological landscape, the fields of blockchain and cloud computing are transforming industries, from finance to healthcare, and creating new opportunities for innovation. Integrating these technologies into education is not merely a trend but a necessity to equip students with the skills they need to thrive in the future workforce. Though both technologies are independently powerful, their potential for innovation and disruption is amplified when combined. This article explores the pressing questions surrounding the inclusion of blockchain and cloud computing in education, providing a comprehensive overview of their significance, benefits, and challenges.
The Technological Edge and Future Outlook
Cloud computing has revolutionized how businesses and individuals’ access and manage data and applications. Benefits like scalability, cost efficiency (including eliminating capital expenditure – CapEx), rapid innovation, and experimentation enable businesses to develop and deploy new applications and services quickly without the constraints of traditional on-premises infrastructure – thanks to managed services where cloud providers manage the operating system, runtime, and middleware, allowing businesses to focus on development and innovation. According to Statista, the cloud computing market is projected to reach a significant size of Euro 250 billion or even higher by 2028 (from Euro 110 billion in 2024), with a substantial Compound Annual Growth Rate (CAGR) of 22.78%. The widespread adoption of cloud computing by businesses of all sizes, coupled with the increasing demand for cloud-based services and applications, fuels the need for cloud computing professionals.
Blockchain, a distributed ledger technology, has paved the way by providing a secure, transparent, and tamper-proof way to record transactions (highly resistant to hacking and fraud). In 2021, European blockchain startups raised $1.5 billion in funding, indicating strong interest and growth potential. Reports suggest the European blockchain market could reach $39 billion by 2026, with a significant CAGR of over 47%. This growth is fueled by increasing adoption in sectors like finance, supply chain, and healthcare.
Addressing the Skills Gap
Reports from the World Economic Forum indicate that 85 million jobs may be displaced by a shift in the division of labor between humans and machines by 2025. However, 97 million new roles may emerge that are more adapted to the new division of labor between humans, machines, and algorithms, many of which will require proficiency in cloud computing and blockchain.
Furthermore, the World Economic Forum predicts that by 2027, 10% of the global GDP will be tokenized and stored on the blockchain. This massive shift means a surge in demand for blockchain professionals across various industries. Consider the implications of 10% of the global GDP being on the blockchain: it translates to a massive need for people who can build, secure, and manage these systems. We’re talking about potentially millions of jobs worldwide.
The European Blockchain Services Infrastructure (EBSI), an EU initiative, aims to deploy cross-border blockchain services across Europe, focusing on areas like digital identity, trusted data sharing, and diploma management. The EU’s MiCA (Crypto-Asset Regulation) regulation, expected to be fully implemented by 2025, will provide a clear legal framework for crypto-assets, fostering innovation and investment in the blockchain space. The projected growth and supportive regulatory environment point to a rising demand for blockchain professionals in Europe. Developing skills related to EBSI and its applications could be highly advantageous, given its potential impact on public sector blockchain adoption. Understanding the MiCA regulation will be crucial for blockchain roles related to crypto-assets and decentralized finance (DeFi).
Furthermore, European businesses are rapidly adopting digital technologies, with cloud computing as a core component of this transformation. GDPR (Data Protection Regulations) and other data protection laws push businesses to adopt secure and compliant cloud solutions. Many European countries invest heavily in cloud infrastructure and promote cloud adoption across various sectors. Artificial intelligence and machine learning will be deeply integrated into cloud platforms, enabling smarter automation, advanced analytics, and more efficient operations. This allows developers to focus on building applications without managing servers, leading to faster development cycles and increased scalability. Processing data closer to the source (like on devices or local servers) will become crucial for applications requiring real-time responses, such as IoT and autonomous vehicles.
The projected growth indicates a strong and continuous demand for blockchain and cloud professionals in Europe and worldwide. As we stand at the “crossroads of infinity,” there is a significant skill shortage, which will likely increase with the rapid adoption of these technologies. A 2023 study by SoftwareOne found that 95% of businesses globally face a cloud skills gap. Specific skills in high demand include cloud security, cloud-native development, and expertise in leading cloud platforms like AWS, Azure, and Google Cloud. The European Commission’s Digital Economy and Society Index (DESI) highlights a need for improved digital skills in areas like blockchain to support the EU’s digital transformation goals. A 2023 report by CasperLabs found that 90% of businesses in the US, UK, and China adopt blockchain, but knowledge gaps and interoperability challenges persist.
The Role of Educational Institutions
This surge in demand necessitates a corresponding increase in qualified individuals who can design, implement, and manage cloud-based and blockchain solutions. Educational institutions have a critical role to play in bridging this widening skills gap and ensuring a pipeline of talent ready to meet the demands of this burgeoning industry.
To effectively prepare the next generation of cloud computing and blockchain experts, educational institutions need to adopt a multi-pronged approach. This includes enhancing curricula with specialized programs, integrating cloud and blockchain concepts into existing courses, and providing hands-on experience with leading technology platforms.
Furthermore, investing in faculty development to ensure they possess up-to-date knowledge and expertise is crucial. Collaboration with industry partners through internships, co-teach programs, joint research projects, and mentorship programs can provide students with invaluable real-world experience and insights.
Beyond formal education, fostering a culture of lifelong learning is essential. Offering continuing education courses, boot camps, and online resources enables professionals to upskill or reskill and stay abreast of the latest advancements in cloud computing. Actively promoting awareness of career paths and opportunities in this field and facilitating connections with potential employers can empower students to thrive in the dynamic and evolving landscape of cloud computing and blockchain technologies.
By taking these steps, educational institutions can effectively prepare the young generation to fill the skills gap and thrive in the rapidly evolving world of cloud computing and blockchain.
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