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Are you a beginner in data science and want to start your career as a data scientist? Or have you learned them previously and need a refresher? Then, you just read the perfect article!
There are so many free Data Science courses out there, and it can take too much time and a lot of skills. So, this article will guide you in taking the right free course to optimize your learning.
What are these courses? Let’s get into it.
1. IBM: Introduction to Data Science
Before you jump into the data science field, you must understand what this field is about. With a good understanding of the work responsibilities and what the job entails, you might gain in the future.
That’s why he first must take a course that could introduce the importance of data science: the IBM: Introduction to Data Science course.
In this course, you would learn essential knowledge such as what data science definition is and what data scientists do, what tools are usually used, the necessary skills for success, and the data scientist’s role in the business.
It’s a short course that would lay the foundation for your future career.
2. Introduction to Data Science for Complete Beginners
Let’s continue learning for you, and this time, a little in-depth study on the data science concept. You might have understood what data science is and how it works, but there are still some concepts you must learn.
In the Introduction to Data Science for Complete Beginners, you will learn more about the data science application, the machine learning concepts, and the difference between data science and similar data roles.
It’s also a short course that takes around a day to finish, but learn it well, and it could support your career well.
3. Introduction to Statistics
The data science field is identic with statistics. While it’s a different concept, they are closely intertwined as the statistic techniques were used in data science. It is why we need to learn statistics if we want to succeed in the data science career,
The Introduction to Statistics course by Stanford would introduce you to statistical thinking, essential for learning about data and sharing insight with others. In this course, you will learn all the basic statistical concepts such as descriptive statistics, inferential statistics, probability, resampling, regression, and many more.
It may be quite a challenging course for a beginner, but you can take it slowly, as it would help tremendously in your data science career.
4. Python for Data Science, AI & Development
Once you have a great understanding of the data science field, it’s time to plunge into the technical skills.
In the modern era, data science is now inseparable from the programming language as it allows the user to speed up the world. That’s why we would start by learning the basics of data science skills: Python programming.
Python for Data Science, AI & Development by IBM is the perfect course for you to start learning Python, which is necessary for the data science field. By learning through five different modules, you would learn all the basics, including Python fundamentals, data structures, how to work with Python for data, and API.
It’s a self-paced course that you can spend over a few weeks to get your basics on.
5. Machine Learning for Everybody – Full Course
With Python knowledge, let’s learn more about machine learning. Machine learning has become a must-use tool for data scientists to solve business problems. That is why we must understand the concept of machine learning much more.
In the Machine Learning for Everybody – Full Course by freecodecamp.org, you would learn the concept from an experienced instructor and how the model works with Python. The main takeaway is more about understanding the machine learning concept than the hands-on one, so you should focus on learning the concept.
It’s a short course you could try to finish in a day, but you should take a moment here and there to understand the course.
6. Introduction to Data Science with Python
With programming skills as a foundation, we would then learn more in-depth how to use Python for data science. In the next course, we will take Introduction to Data Science with Python from Harvard University.
This course is intended for those who want to learn more about data science but already have a minimum understanding of Python programming. It’s not a course to learn Python, but more about how to use them in data science works.
This is because many of the courses were about hands-on applications of Python in the data science field, such as using statistical learning, model development, model selection, and developing your first data science project.
If you finish this course, it could serve as your first data science portfolio.
7. Machine learning in Python with scikit-learn
The next course you should learn is Machine Learning in Python with scikit-learn from Inria. It’s a beginner course in developing your machine learning model but still requires understanding the programming and machine learning concepts.
A predictive machine learning model is an important tool for data scientists, and this course would teach you all the foundations to develop it. Using the popular library Scikit-Learn, the course would guide you on creating a pipeline, developing the best model, fine-tuning it, and evaluating it.
The course is self-paced, so you can take your time to finish them.
8. Learn SQL Basics for Data Science Specialization
Python is not the only Programming Language that data scientists should know. The importance of SQL in the data role has become even more prominent with how companies are now storing their data. This means that data scientists are expected to understand SQL for data querying.
Learn SQL Basics for Data Science Specialization from UC Davis is the right course for studying SQL, which data scientists require, as it is intended for any beginner who doesn’t have programming skills.
The course contains four modules that progressively become harder as you go on. Starting from the SQL basics, you will learn more about using SQL for data wrangling and analysis. You would also learn how to use distributed computing and end with developing your SQL project.
Going through with this course would take your career to the next level, so don’t miss it.
9. Introduction to Data Visualization
For data scientists, communicating your results to the audience is as important as the result. If you can’t make the audience understand your data science project and convince stakeholders of the importance of your project, then it’s the same as a failed project.
Data visualization is one way to present your results more aesthetically and in a much more friendly way than presenting the raw data. The Introduction to Data Visualization by Simplilearn would be a great start in learning how to visualize your data.
The course would teach you the data visualization principle, how to communicate with your visualization, and how to use several visualization tools such as PowerBI, Excel, and Matplotlib.
It’s a short course but could be effective if you learn them well.
10. Communicating Data Science Results
The last course we would learn is how to communicate, especially with the stakeholders and non-technical audiences. It’s a vital soft skill that every data scientist needs to understand as they are a part of data scientist work.
We might have our data science technical skills and excellent results, but wrong communication could lead to a disastrous project. The Communicating Data Science Results course by the University of Washington is necessary.
The course would teach you how to visualize your data results effectively, the privacy and ethics related to the data science project, and data science reproducibility with cloud computing. By learning all these skills, you could certainly be at the top of your career.
Conclusion
All the courses I have mentioned above are intended to be taken from top to bottom but feel free to take those necessary. The critical point in this article is that the free courses are a must-take because they teach you the necessary skills to survive as a data scientist.
Enjoy the process and believe that you can become a data scientist.
Cornellius Yudha Wijaya is a data science assistant manager and data writer. While working full-time at Allianz Indonesia, he loves to share Python and data tips via social media and writing media. Cornellius writes on a variety of AI and machine learning topics.
