Data science is a vast field - deep and wide. It still does not cease to amaze me, after all, we are predicting the future. It is an evolving field so no degree program can cover its breadth. Therefore, it is important for data science professionals to choose their domain and specialize in it.

Working in the field of data science at an online education startup in India acquainted me with its various disciplines such as business analytics, natural language processing, and machine learning. I realized the impact caused by these disciplines on businesses and society while working with professionals from both industry and academia. Although the impact of each field is subjective, they all involve telling a story from the data at hand, and making decisions based on the insights from that story. The way how stories are told across these disciplines varies as some stories involve throwing statistics at their audiences while some rely purely on visualizations. In the process of choosing a data science specialization, I had to figure out my own way of telling a story to facilitate making impactful business decisions. Since I have always enjoyed math and programming, I wanted to choose a niche that blends both these subjects. As a result, I decided to specialize in machine learning. Mastering machine learning (ML) was a conscious and well-calculated decision that motivated me to apply to graduate school. Importance of planning for graduate students cannot be overstated. Therefore, in my pursuit of mastering ML, I have composed a list of topics that I plan to learn by the time I graduate in May 2021. The list contains important math and computer science courses that will strengthen my foundation as a machine learning professional. The sources of learning these topics will include university coursework, online platforms, and books.

The list of topics is present below along with an emoji showcasing my current progress. I will be continuously updating this post to reflect my progress and accommodate new topics.

Note on emoji reference:

• ✔️: Completed learning the topic and feel fairly confident in it.
• ❌: Yet to start.
• 🕑: Work-in-progress.

Mathematics

• Calculus ✔️
  • [Book, 2020] Calculus: Early Transcendentals ✔️

• Linear Algebra ✔️
  • [Course, 2019] Duke MATH 718: Matrices and Vector Spaces ✔️
  • [Book, 2019] Introduction to Linear Algebra by Gilbert Strang ✔️

• Probability ✔️
  • [Course, 2020] Duke MATH 740 - Advanced Introduction to Probability ✔️
  • [Book, 2020] A Natural Introduction to Probability Theory by Ronald Meester ✔️

• Statistics ✔️
  • [Course, 2019] Duke STA 611: Introduction to Mathematical Statistics ✔️
  • [Course, 2019] Duke IDS 702: Modeling and Representation of Data ✔️
  • [Book, 2020] OpenIntro Statistics ✔️

Computer Science

• Programming 🕑
  • [Course, 2020] Duke ECE 551: Programming, Data Structures, and Algorithms in C and C++ ✔️
  • [Course, 2021] Duke ECE 651: Software Engineering 🕑:

• Algorithms ✔️
  • [Course, 2020]: Duke CS 531: Introduction to Algorithms ✔️

• Machine Learning ✔️
  • [Course, 2020] Duke IDS 705: Principles of Machine Learning ✔️
  • [Book, 2020] An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani ✔️

• Deep Learning 🕑
  • [Course, 2019] Deep Vision by School of AI ✔️
  • [Book, 2018] Deep Learning with Python by François Chollet ✔️
  • Deep Learning by Aaron Courville, Ian Goodfellow, and Yoshua Bengio ❌:

• Reinforcement Learning ✔️
  • [Book, 2020] Reinforcement Learning: An Introduction ✔️

Last updated: February 19, 2021.