Do you have the right set of skills and knowledge to become a data scientist? Then, all you need to know is where to get started. What are the best resources/ ways to learn data science? As you know, there are hundreds of books, online courses, and graduate degrees to start with. But, what’s best for you?
Data science can naturally be taken up by those who have multiple skills and experiences acquired by working with computers, knowledge of maths, and statistics. You also need some background in computer programming. So, in this post, I have tried to pick 7 best ways to learn data science.
7 Best Ways to Learn Data Science
Online Courses:
Thankfully, there are a few Data Science courses that have risen above the pack in recent years. Whether you’re starting your journey into machine learning or you want to perfect your Python skills, you should find one Data Science course below that meets your skill level, expectations, and budget. If you’re looking to become a Data Scientist, check our extensive write-up To Become a DATA Scientist.
Introduction To Computational Thinking And Data Science
This course is for students who are beginners and have little or no knowledge of programming. It has a strong emphasis on Python programming, which is considered to be the go-to language for data science implementations. This course also focuses on providing an understanding of the role computation has to play in solving problems. Moreover, through this course, students will be able to feel confident about writing small programs that allow them to accomplish useful goals.
CS109 Data Science by Harvard University
Harvard University is a comprehensive resource for preliminary data science. This course causes you to increase valuable bits of knowledge and forecasts. It fundamentally centers around 5 key aspects of examination:
- Data wrangling
- Cleaning
- Sampling- to get suitable data set
- Data Management-to have the option to get to enormous data rapidly and dependably
- Data Analysis- to generate hypothesis and intuition
The Open Source Data Science Masters by Clare Corthell
It is a comprehensive collection of data science resources. The site covers a large array of topics of data science basics, mathematics, statistics, machine learning, programming, and data visualization. This storehouse of resources additionally explains why a strong establishment of data science through open-source instruments is fundamental to overcome any issues among ability and industry.
Data Science marries technical systems, human behavioral questions, and a beautiful world of mathematics and statistics.
Learning From Data By California Institute Of Technology
It is a machine learning course. It has a comprehensive take on the subject. Since machine learning has emerged as the hottest field of study taken up by undergraduates and graduates. It empowers the computational frameworks to adaptively improve their exhibition with experience collected from the watched data. It has an extensive interpretation of the subject.
Textbooks:
Think Python
By Allen B. Downey
This book is a fit for beginners to advanced levels of python users. This book reviews everything from the basics of data structures and functions to more advanced topics such as classes and inheritance.
Understanding Machine Learning: From Theory To Algorithms
By: Shai Shalev-Shwartz and Shai Ben-David
If you want a deeper understanding of machine learning topics then this book is great. This book provides an extensive theory of algorithms. It’s split into the following sections:
- Foundations
- Theory to algorithms
- Additional learning models
- Advanced theory
Think Stats
By Allen B. Downey
In order to become a data scientist, it’s important to have a solid grasp of probability and statistics. This book is geared towards programmers. So it takes more of an applied approach rather than conventional textbooks that focus on math and theory.
Conclusion
A natural curiosity blended with creative & critical thinking. You also need some background in computer programming. That’s all you require to become a data scientist. Learning is a procedure so take it in little pieces. Remember, it doesn’t have to be perfect. Therefore, the goal should be to learn, build, and showcase your skills.
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