When I began my career in data science, I was confused about where to start, what to learn or what the best courses were. A simple Google search won’t help you and you might end up wasting a lot of time searching for the right resources. After experiencing this hardship, I promised myself that I would simplify things for others. This blog contains a list of the best books, courses, platforms, certifications, and social networks that will help you jump-start your career.
Some people prefer reading books, so for them there are some interesting books available this year. Before we jump to the book list, take a moment to ask yourself which programming language you prefer. Most people will pick Python but R and Julia are highly in demand as they both are designed for data science.
Intermediate level: Practical Statistics for Data Scientists
R lovers: R for Data Science
Language of the future: Julia Data Science
My journey started with Codecademy and DataCamp courses that have taught me the basics of Python, R, SQL, and statistics. I highly recommend beginners to give any data science course a try and try to complete it on time.
The list below contains free courses, paid courses, and short tutorials. These courses will help you build a strong base for you to start working on real-life data projects.
- Data Science Full Course — Learn Data Science in 10 Hours
- Learn Data Science Tutorial — No Code — Full Course for Beginners
- Intro to Data Science Master’s course
- Udacity: Learn to Become a Data Scientist Online
- DataCamp: Data Scientist with Python Track
- Coursera: IBM Data Science Professional Certificate
- Codecademy: Data Scientist Career Path
- Data Science Tutorial for Beginners: What is, Basics & Process
- Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)
- Data Visualization Python
My life would have been easier if I had known about Kaggle before starting my data science journey. These platforms come with examples, easy guides, and the ability to perform experiments. You can learn from notebooks or discuss ideas with a community of expert data scientists.
If you want to succeed in data science and machine learning, just create an account on Kaggle and start participating in various discussions. Also, create a GitHub account to learn from the larger developer community.
The data science blogs provide bite-size information about specific tools, new concepts, and beginner-friendly tutorials. After completing a few courses, I immediately fell in love with easy-to-follow blogs on Towards Data Science and Analytics Vidhya. They are still helping me get better in data analytics and machine learning.
If you don’t have a data science degree, a certification will give you an edge in the job market. Most of the platforms are now offering certification examinations and many are worth it.
These certification examinations will test you on data analytics, programming skills, statistical thinking, modeling, and presentation.
- DataCamp Certification
- Databricks Certified Professional Data Scientist
- HarvardX Data Science Professional Certificate | edX
- Big Data Scientist Certifications | Certified Data Scientist | DASCA
Data Social Network
Image by author | Elements by freepik
How do you keep yourself updated with the ever-changing data science field? By following experts, influencers or joining data science social networks. These social networks include Discord (Learn AI Together), LinkedIn, Twitter, Dev.to, and Reddit (r/datascience).
I have been receiving direct messages and emails asking “how to start learning data science.” Most were saying that they thought if they could learn the basics, they would start earning six figures, which is the wrong mentality. So, if you are in it for the wrong reasons, I will suggest you stop here and start looking for the things you love. The journey will become hard and you will lose direction. In short, you will end up wasting your time and money doing things that you don’t love.