Data Science is a rapidly growing field, and many people are interested in becoming Data Scientists. However, the path to becoming a Data Scientist can be challenging, and it requires a deep understanding of statistics, programming, machine learning, and business. In this blog post, we will provide you with a three-step guide to becoming a Data Scientist.
Step 1: Statistics
Statistics is the first pillar of Data Science, and it is essential to learn it to become a good Data Scientist. To get started with Statistics, you need to know some basic concepts of mathematics. A free four-week course on Coursera called “Data Science Math Skills” by Duke University covers important concepts like Mean, Variance, Derivatives, and Bayes theorem. Another great thing about this course is that it does not try to teach you everything. It provides you just enough knowledge to get started with Statistics. Once you feel confident about your math skills, you can learn Statistics. However, the majority of Data Science roles are analytics roles, and they do not require an in-depth understanding of Statistics. Therefore, to learn all the key concepts that you actually need, we recommend taking the “Introduction to Statistics” course by Stanford University. This course covers all the important ideas like Probability, Normal distribution, and Confidence Intervals, among many others.
Step 2: Programming
The second pillar of Data Science is programming. When it comes to programming for Data Science, we have primarily two languages to choose from: R and Python. We recommend picking Python as your programming language because it is a full-fledged programming language that can be used for applications beyond Statistics and Machine Learning. To learn Python, we recommend doing actual coding on the “learnpython.org” website. Complete the tutorials covering basics as well as Data Science. As always, play with the code and complete the exercise portion.
Step 3: Machine Learning
The third pillar of Data Science is Machine Learning. To apply Machine Learning algorithms, you need data. When you are working on your personal projects for Machine Learning, you can go to websites like UC Irvine’s Machine Learning Repo and choose data to work on. However, in the real world, you rarely get well-defined, cleaned-up data. You have to decide what data makes sense for your application and then use SQL to extract it.
In conclusion, becoming a Data Scientist requires a deep understanding of Statistics, Programming, and Machine Learning. By following this three-step guide, you will be able to learn all the necessary skills to become a Data Scientist. However, you will also need to be strategic about what you learn and how you learn it. Remember, becoming an employable Data Scientist is where the real challenge lies.
Disclaimer: We may not be able to provide 100% perfect information. We gather questions and get answers from experts. The experts provide answers but the text is not written directly by them so there may be some typos. I prefer to put this note at top but that causes some issues with display. Thank for reading.
Originally published at https://www.hitreader.com on April 14, 2023.
