Top 10 Free Data Science Courses to Learn Online | Become a Data Scientist

Eric Nkom May 20, 2024

0 0 6 minutes read

Top 10 Free Data Science Courses to Learn Online | Become a Data Scientist

Do you want to pursue a career in data science but are just starting? Or perhaps you already know them and just need a reminder? You’ve just read the ideal article, then! It can take a lot of time and skills, and there are a ton of free data science courses available.

To maximize your learning, this article will assist you in selecting the appropriate free course. These courses: what are they? Now let’s get started.

1. IBM: Introduction to Data Science

You need to be aware of the basics of data science before diving in headfirst. You might benefit in the future if you have a solid grasp of the duties and obligations of the position. For this reason, he must first enroll in the IBM: Introduction to Data Science course, which could serve as an introduction to the field’s significance.

You would acquire fundamental information in this course, including the definition of data science, the duties of data scientists, typical technologies utilized, success criteria, and the data scientist’s place in the company. This brief course would serve as the groundwork for your future professional endeavors.

2. Introduction to Data Science for Complete Beginners

This time, let’s take a closer look at the data science notion as we continue our education together. Even though you may be aware of the basics of data science, there are still some ideas you need to grasp.

You will discover more about machine learning principles, data science applications, and the distinctions between data science and related data roles in the Introduction to Data Science for Complete Beginners book. It’s a quick course that takes about a day to complete, but if you study it effectively, it could help your career.

3. Introduction to Statistics

Statistics and data science are the same field. Despite being distinct concepts, they are closely related since data science uses statistical approaches. This is the reason why learning statistics is necessary if you want to pursue a career in data science. Stanford’s Introduction to Statistics course will teach you statistical reasoning, which is crucial for understanding data and imparting knowledge to others.

All of the fundamental statistical ideas, including probability, resampling, regression, inferential statistics, descriptive statistics, and many more, will be covered in this course. For a novice, this course could be fairly difficult, but you should go slowly because it will be very beneficial to your data science career.

4. Python for Data Science, AI & Development

It’s time to go into the technical aspects after you have a firm grasp of the data science discipline. Data science and programming languages are now inextricably linked in the current day since they enable users to speed up the globe. For this reason, we would begin by studying Python programming, the foundational language of data science.

The ideal course for you to begin learning Python, which is required for the data science area, is IBM’s Python for Data Science, AI & Development. All the essentials, such as data structures, APIs, working with Python for data, and Python fundamentals, will be covered in five distinct modules. You can take the self-paced course over several weeks to master the fundamentals.

5. Machine Learning for Everybody: Full Course

Now that we know more about Python, let’s explore machine learning. Data scientists now have to use machine learning as a tool to solve business problems. For this reason, we need to gain a deeper understanding of the machine learning idea.

You would study the idea from a knowledgeable educator and how the model functions using Python in the Machine Learning for Everyone—Full Course offered by freecodecamp.org. You should concentrate on mastering the concept of machine learning rather than its practical application because that is the primary takeaway.

Although the course is brief and might be completed in a single day, you should spend some time to fully comprehend it.

6. Introduction to Data Science with Python

After gaining a foundation in programming, we would study Python for data science in further detail. We’ll be taking Harvard University’s Introduction to Data Science using Python as our next course.

This course is designed for people who have some programming experience in Python and want to learn more about data science. It focuses more on using Python in data science projects than it does on teaching the language itself.

This is because a large portion of the courses focused on practical Python data science applications, including model construction, statistical learning, model selection, and creating your first data science project. This course may act as your first data science portfolio if you complete it.

7. Machine learning in Python with scikit-learn

You should take Inria’s Machine Learning in Python with Scikit-learn as your next course. Even though this is a beginner’s course on creating your machine learning model, you still need to master the fundamentals of programming and machine learning.

For data scientists, a predictive machine learning model is a crucial tool, and this course will teach you the fundamentals of creating one. The course would walk you through building a pipeline, selecting the best model, honing it, and assessing it using the well-known Scikit-Learn library. You can complete the assignments at your own pace because the course is self-paced.

8. Learn SQL Basics for Data Science Specialization

Data scientists should be proficient in more programming languages than only Python. The way businesses currently store their data has further highlighted SQL’s significance in this role. This implies that to query data, data scientists should be familiar with SQL.

Since Learn SQL Basics for Data Science Specialization from UC Davis is designed for beginners without programming experience, it is an excellent choice for learning SQL, which is a prerequisite for data scientists.

There are four modules in the course, and the difficulty increases with each one. You will learn more about utilizing SQL for data wrangling and analysis, starting with the fundamentals. Along with learning how to use distributed computing, you would finish off by working on your SQL project. Don’t miss this opportunity to advance your career by enrolling in the course.

9. Introduction to Data Visualization

For data scientists, sharing the findings with the public is just as crucial as the actual findings. A data science project is considered unsuccessful if its stakeholders cannot be persuaded of its significance and the audience cannot be made to understand it.

Presenting your results in a more aesthetically pleasing and approachable manner than just the raw data can be achieved through the use of data visualization. To get started with learning how to visualize your data, Simplilearn’s Introduction to Data Visualization is a wonderful place to start.

You would learn the fundamentals of data visualization in this course, along with how to interact with your visualization and use a variety of tools, including Excel, PowerBI, and Matplotlib. Although the course is brief, if you learn them, it might be useful.

10. Communicating Data Science Results

The final course we would take would be communication, particularly with non-technical audiences and stakeholders. Since they are a component of data scientists’ work, it is an essential soft skill that all data scientists must comprehend.

Even when we have outstanding technical skills in data science and achieve great outcomes, poor communication can make a project go wrong. It is required that you take the University of Washington’s Communicating Data Science Results course.

You will learn how to properly visualize your data results, as well as about data science project privacy and ethics, and how to use cloud computing for data science reproducibility. Developing all of these abilities could undoubtedly propel you to the pinnacle of your profession.

Conclusion

Although it is recommended to take all of the courses listed above in order, you are welcome to take just the ones that are required. This article’s key takeaway is that taking the free courses is essential if you want to learn the skills you’ll need to succeed as a data scientist. Have fun and have faith in your ability to become a data scientist.