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Are you an aspiring data scientist looking to start a career as a data scientist? Or maybe you’ve learned them before and need a refresher? Then you just read the perfect article!
There are many free data science courses that can take too much time and require a lot of skills. Therefore, this article will aid you choose the right free course that will optimize your learning.
What are these courses? Let’s get on with it.
1. IBM: Introduction to Data Science
Before you move into the field of data science, you need to understand what the field is all about. By having a good understanding of your job responsibilities and what it entails, you can benefit in the future.
Therefore, he first needs to take a course that could introduce the importance of data science: IBM: Introduction to Data Science course.
In this course, you will gain vital knowledge such as what the definition of data analytics is and what data scientists do, what tools are typically used, the skills necessary to achieve success, and the role of a data analyst in business.
This is a brief course that will lay the foundations for your future career.
2. Introduction to Data Science for Beginners
Let’s continue learning for you, this time with some in-depth study of data science concepts. You may have understood what data analytics is and how it works, but there are still some concepts you need to learn.
In An introduction to data science for beginnersyou’ll learn more about data science applications, machine learning concepts, and the differences between data science and similar data-related roles.
It is also a brief course that takes about a day to complete, but learn it well and it can support your career well.
3. Introduction to statistics
The field of data science is identical to statistics. Although it is a different concept, they are closely related as statistical techniques have been used in data science. This is why we need to learn statistics if we want to succeed in a data science career,
The Introduction to Stanford’s Statistics Course would introduce you to the statistical thinking necessary to understand data and share insights with others. In this course, you will learn all the basic statistical concepts such as descriptive statistics, inferential statistics, probability, resampling, regression, and much more.
It might be quite a challenging course for a beginner but you can take it slowly as it would aid your data science career immensely.
4. Python for data analytics, artificial intelligence and development
Once you have a solid understanding of the field of data analytics, it’s time to dive into technical skills.
In up-to-date times, data science is already inextricably linked to the programming language, as it allows the user to speed up the world. Therefore, we would start by learning the basics of data science: Python programming.
Python for data science, artificial intelligence and development for IBM is the perfect course to start learning Python, which is vital in the field of data science. By learning through five different modules, you’ll learn all the basics, including Python basics, data structures, how to work with Python for data, and APIs.
This is a self-study course that you can spend over a few weeks to master the basics.
5. Machine learning for everyone – full course
With knowledge of Python, let’s learn more about machine learning. Machine learning has become an vital tool for data scientists to solve business problems. Therefore, we need to understand the concept of machine learning much better.
In Machine learning for everyone – full course at freecodecamp.org, you’ll learn concepts from an experienced instructor and learn how the model works with Python. The main takeaway is more about understanding the concept of machine learning than it is about practical learning, so you should focus on learning the concept.
This is a brief course that you can complete in one day, but you should take a moment here and there to understand the course.
6. Introduction to Data Science in Python
With programming skills as a foundation, we will learn in more detail how to employ Python for data science. In the next course we will take up Introduction to Data Science in Python from Harvard University.
This course is intended for those who want to learn more about data science but already have minimal knowledge of Python programming. This is not a course to learn Python, but more about how to employ it in data science.
This is because many of the courses covered practical applications of Python in data science, such as using statistical learning, developing models, selecting a model, and developing your first data science project.
If you complete this course, it can 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. This is a beginner’s course in developing a machine learning model, but it still requires an understanding of programming and machine learning concepts.
A predictive machine learning model is an critical tool for data scientists, and in this course you will learn all the basics of developing one. Using the popular Scikit-Learn library, the course will guide you through creating a pipeline, developing the best model, tuning it, and evaluating it.
The course is self-paced, so you can take your time to complete it.
8. Learn the basics of SQL for Data Science specialization
Python isn’t the only programming language data scientists should know. The importance of SQL as data has become even more evident in the way companies now store their data. This means that data scientists are expected to have knowledge of SQL to query data.
Learn the basics of SQL with a data science major at the University of California, Davis is the right SQL course that data scientists need because it is designed for any beginner who has no programming skills.
The course consists of four modules that become increasingly more challenging as you progress. Starting with SQL basics, you’ll learn more about using SQL to manage and analyze data. You’ll also learn how to employ distributed computing and finish with creating a SQL project.
Completing this course will take your career to the next level, so don’t miss out.
9. Introduction to data visualization
For data scientists, communicating results to audiences is as critical as the result itself. If you can’t get your audience to understand your data science project and convince your stakeholders of the importance of your project, it’s tantamount to a failed project.
Data visualization is one of the ways to present results in a more aesthetic and affable way than presenting raw data. The Introduction to Data Visualization by Simplilearn would be a great start to learning data visualization.
The course will teach you the principles of data visualization, communicating with visualization and using several visualization tools such as PowerBI, Excel and Matplotlib.
It’s a brief course, but it can be effective if you learn it well.
10. Communicating the results of data analysis
The final course we would learn is communication, especially with stakeholders and non-technical audiences. This is an vital tender skill that every data scientist must understand because it is part of the job of a data scientist.
We may have technical skills in data science and excellent results, but penniless communication can lead to a disastrous project. The Communicating Data Science Results course from the University of Washington is necessary.
The course will teach you how to effectively visualize data results, the privacy and ethics associated with a data science project, and the reproducibility of data science in cloud computing. By learning all these skills, you can definitely be at the top of your career.
Application
All the courses I mentioned above are designed to go from top to bottom, but you can take the ones that are vital. The most critical point of this article is that free courses are a must because they allow you to gain the skills you need to survive as a data scientist.
Enjoy the process and believe that you can become a data scientist.
Cornelius Yudha Vijaya is an assistant data analytics manager and data writer. Working full time at Allianz Indonesia, he loves sharing Python tips and data through social media and writing media. Cornellius writes on a variety of topics related to artificial intelligence and machine learning.