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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 before and need a refresher? Then you've just read the perfect article!
There are so many free data science courses that can require too much time and too many skills. Therefore, this article will guide you to take the right free course to optimize your learning.
What are these courses? Let's get into it.
1. IBM: Introduction to Data Science
Before jumping into the field of data science, you need to understand what this field is all about. With a good understanding of job responsibilities and what the job entails, you could earn in the future.
That is why you should first take a course that can introduce the importance of data science: the IBM: Introduction to data science course.
In this course, you will learn essential knowledge such as what the definition of data science is and what data scientists do, what tools are commonly used, the skills needed for success, and the role of the data scientist in the business.
It is a short course that would lay the foundation for your future career.
2. Introduction to Data Science for Beginners
Let's continue learning for you, and this time, a small in-depth study on the concept of data science. You may have understood what data science is and how it works, but there are still some concepts you need to learn.
In it Introduction to Data Science for BeginnersYou will learn more about the application of data science, machine learning concepts, and the difference between data science and similar data functions.
It's also a short course that takes about a day to complete, but if learned well, it could support your career well.
3. Introduction to statistics
The field of data science is identical to statistics. While it is a different concept, they are closely intertwined as statistical techniques were used in data science. This is why we need to learn statistics if we want to succeed in a data science career.
He Introduction to Statistics Course by Stanford would introduce you to statistical thinking, essential for learning about data and sharing knowledge 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 can be quite a challenging course for a beginner, but you can take it easy as it would be of great help in your career in data science.
4. Python for data science, artificial intelligence and development
Once you have a great understanding of the data science field, it's time to dive into the technical skills.
In the modern era, data science is now inseparable from programming language as it enables the user to speed up the world. That's why we would start by learning the basics of data science skills: Python programming.
ai” target=”_blank” rel=”noopener”>Python for data science, artificial intelligence and development from 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 will learn all the basics including Python fundamentals, data structures, how to work with Python for data, and APIs.
It's a self-paced course that you can spend over a few weeks getting the basics down.
5. Machine Learning for Everyone: Complete Course
With the knowledge of Python, let's learn more about machine learning. Machine learning has become an essential tool for data scientists to solve business problems. That is why we must understand the concept of machine learning much more.
In it Machine Learning for Everyone: Complete Course from freecodecamp.org, you will learn the concept from an experienced instructor and how the model works with Python. The main takeaway is more about understanding the concept of machine learning than the practical concept, so you should focus on learning the concept.
It's a short course that 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 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 minimal knowledge of Python programming. It is not a course to learn Python, but rather how to use it in data science.
This is because many of the courses were about practical applications of Python in the field of data science, such as using statistical learning, developing models, selecting models, and developing your first data science project. data.
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 by Inria. It is a beginner's course in developing your machine learning model, but still requires understanding programming and machine learning concepts.
A predictive machine learning model is an important tool for data scientists and this course will teach you all the basics of developing it. Using the popular Scikit-Learn library, the course will guide you on how to create a pipeline, develop the best model, tune it, and evaluate it.
The course is self-paced, so you can take your time to finish it.
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 role of data has become even more prominent with the way companies now store their data. This means that data scientists are expected to understand SQL to query data.
Learn SQL Basics for UC Davis Data Science Specialization is the right course to study SQL, which is required by data scientists, as it is intended for any beginner who does not have programming knowledge.
The course contains four modules that progressively become more difficult as you progress. Starting with the basics of SQL, you will learn more about using SQL for data manipulation and analysis. You will also learn how to use distributed computing and will finish with the development of your SQL project.
Pursuing this course would take your career to the next level, so don't miss it.
9. Introduction to data visualization
For data scientists, communicating their results to the audience is as important as the result. If you cannot make the audience understand your data science project and convince the stakeholders of the importance of your project, then it is the same as a failed project.
Data visualization is a way to present results in a more aesthetic and much friendlier way than presenting raw data. He Introduction to Simplilearn Data Visualization It would be a great start to learning how to visualize your data.
The course will teach you the principle of data visualization, how to communicate with your visualization, and how to use various visualization tools such as PowerBI, Excel, and Matplotlib.
It's a short course but it could be effective if you learn it well.
10. Communicating data science results
The last course we would learn is how to communicate, especially with stakeholders and non-technical audiences. It is a vital soft skill that every data scientist must understand, as it is part of the job of data scientists.
We may have our technical data science skills and great results, but poor communication could lead to a disastrous project. He Communication of Data Science Results Course by the University of Washington it is necessary.
The course will teach you how to visualize your data results effectively, privacy and ethics related to the data science project, and the reproducibility of data science with cloud computing. By learning all these skills, you could definitely be at the top of your career.
Conclusion
All of the courses I've mentioned above are meant to be taken from top to bottom, but feel free to take as many as necessary. The critical point of this article is that free courses are essential because they teach you the skills necessary to survive as a data scientist.
Enjoy the process and believe that you can become a data scientist.
Cornellius Yudha Wijaya He is an assistant data science manager and data writer. While working full-time at Allianz Indonesia, he loves sharing data and Python tips through social media and print media. Cornellius writes on a variety of artificial intelligence and machine learning topics.
