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How to Become a Data Scientist in 2021: Data Science Roadmap

January 1, 2021 by systems

We are at a point where more data is generated than ever before in history. These data are extremely useful when analyzed to generate insights to make business decisions. And also, increased computing capabilities that lead to the construction of systems that can analyze all this data in a reasonable time.

1. Math Fundamentals

To get started in data science you need to learn the concepts of linear algebra, calculus, optimization, and functions.

  • Part 1 of deeplearningbook.org
  • Math track of Khan Academy
  • Linear algebra by fast.ai
Photo by Jeswin Thomas on Unsplash

2. Programming

You must choose some programming language that allows you to interact with data, you must also learn the CS fundamentals.

  • learnpython.org
  • Python crash course
  • CS50x by Harvard
Photo by Fotis Fotopoulos on Unsplash

3. Data Wrangling and Visualization

Understanding Data Wrangling techniques (data collection, cleaning and, exploration) is very important as a data scientist. Also, learn how to create and study the visual representation of the data.

  • Python for Data Analysis by Wes McKinney
  • Towards Data Science (Medium Publication)
  • Hands-On Data Analysis with Pandas
Photo by Chris Liverani on Unsplash

4. Statistics and Probability

Learn descriptive and inferential, associative, and differential statistical concepts and also probability (Bayes’ Theorem is very important)

  • Khan Academy
  • Intro to Statistics by Udacity
  • MIT OpenCourseWare
Photo by Roman Mager on Unsplash

5. Databases

Most of the companies use relational databases instead of text files to store data. You must bring the data from somewhere.

  • SQL for Data Science (Coursera)
  • SQL and Database course by freecodecamp
  • MongoDB university
Photo by Jan Antonin Kolar on Unsplash

6. Machine Learning

First, understand the terminologies around machine learning and its types (supervised and unsupervised learning, dimensionality reduction techniques, time series, etc.)

  • Machine Learning by Andrew Ng (Coursera)
  • Fast.ai Machine Learning course
  • Hands-On Machine Learning with Scikit-Learn and TensorFlow
Photo by Arseny Togulev on Unsplash

7. Resources to Practice

Get good hands-on experience by practicing what you learned by doing projects, participate in competitions, meet fellow data scientists and learn from them.

  • Kaggle
  • LinkedIn and GitHub
  • Fast.ai forum
Photo by Brett Jordan on Unsplash

8. Big Data

A single machine is not enough when you are working with huge amounts of data. Learn Big Data Fundamentals, Hadoop Ecosystem, MapReduce, Apache Spark, etc. Also, learn how to deploy the models and maintain them.

  • Big Data Specialization by UCSanDiego (Coursera)
  • fullstackdeeplearning.com
  • Getting started with Apache Spark by James A. Scott
Photo by imgix on Unsplash

9. Find a Job

The best way to test your skills is to work on a real-life problem. You can get a job, an internship or take a Bootcamp, or work on your own venture where you can do this. Start with an internship and with the time you will get a full-time job as a Data Scientist.

  • Follow and engage with the community
  • Find jobs in LinkedIn, Angel.co, Kaggle, etc.
  • Create a perfect resume before applying to the jobs
Photo by Alex Kotliarskyi on Unsplash

10. Advance Concepts

It is time to learn advanced concepts as Deep Learning. Based on your job requirements or your own projects (or your own curiosity), learn the related concepts.

  • Deeplearning.ai
  • Fast.ai
  • Natural Language Processing
Photo by Thought Catalog on Unsplash

Extra.

You must bear in mind that you will never finish learning everything. There are many different libraries, many machine learning algorithms, many resources. There is so much information that it is impossible to keep up to date. You have to make sure you always keep learning a little more. If you do that, learn new things and practice daily, you will never be short of job offers.

Filed Under: Machine Learning

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