My 2019 reading list for data science aspirants
By: Anand Das
Tags: Machine Learning, Analytics, Data Science Leadership, Data Science, AI

I have been working as a data science manager (erstwhile Analytics) for last 12+ years, but I did not have the opportunity to get trained as a computer scientist during my graduation days (I was trained as a mechanical engineer and later on pursed MBA). All of my learnings in AI (flavors of data science, machine learning, deep learning) have been at workplace, from my colleagues and most importantly, from online courses.

I have benefitted a lot from these online courses and I wanted to share my learnings to folks who come from a similar background like me and work as data science managers or leaders.

Who might benefit from this reading list ?

If you lead the AI or Data science charter in your department or company. If you lead a team of data scientists, data/ML engineers and analyst. If you are not formally trained as a computer scientist but have basic background in mathematics (matrix algebra, calculus) and have exposure to basics of programming or scripting (SQL, SAS)

Beginners-level reading list for you.

AI for everyone : Andrew NG’s new course ‘AI for everyone’ explains AI in layman terms. You can use this content to educate and influence your non-technical leadership stakeholders and colleagues.( course link | Free subsription) AI transformation roadmap : If you are leading the AI or Data science charter in your department or company, read Andrew NG’s recommendations on how to drive AI transformation in your company ( course link | Free subsription) Basics of Computer science : If you are not from a computer science backgound, its important to understand the basics of computer science. Introduction to computer science, taught by David J. Malan at Harvard is a widely recognized course. ( Course link : Select ‘audit course’ option if you dont want to pay) Python for ML and visualization : Jose Portilla is one of my favorite trainers on udemy. This course will give you good high level understanding on how to use python for machine learning (supervised and unsupervised learning problems) and data visualization. (Course link, its paid course but udemy is affordable) Interactive visualization : A data science team is as successful as its ability to help business users make data-driven decisions. For adoption to this new way of decision-making, its important to provide easy-to-comprehend visual insights. This course helps you buid Interative visualization solutions using plotly (course link, its a paid course)

Advanced-level reading list.

Source code management : Its important that a data science manager knows how to use git or other equivalent source code management tools. He should be able to log-in and review the codes written by his team during deep-dive meetings. (Course link, its a free course) Advanced python : Its an advanced level python course. It will improve your appreciation for large scale coding work done by engineers and give insights on best practices like Python object-oriented programming, advance objects, exception handing etc. (Course link, its a paid course but good value for money) Advanced AI : With the computational advancements in deep-learning in last 2 years and its increasing application in solving different use-cases, its critical that you have a conceptual understanding to how areas like natural language processing, computer vision, reinforcement learning is being used by Machine learning engineers or scientists. One of the option is to take a deep-learning specialization in coursera. The specialisation involves 5 broad topics. If you don’t want to take the full specialisation, you can pick and choose. Alternatively, there are many good courses on this topic on other MOOC platforms.

Neural networks and deep learning, Imporving deep neural networks, structuring ML projects, Convolutional neural networks and sequence models.

4. Micro-services architecture : More and more organizations are transforming their monolithic tech stacks into organized, maintainable and scalable services, leveraging the microservices architectureMicroservices fundamentals. As a data science manager its important that you understand the fundamentals of micro-services architecture. (Course link, Paid course)

5. Distributed Systems : I found this course helpful to understanding Distributed systems at a high level. (Course link, its a paid course)

It provides a good conceptual understanding on areas like storage, computing, messaging, synchronization and messaging

This reading list by no means is a comprehesive list or the best list available on internet. This is what I found very useful and I thought its worth sharing.

Let me know if you find this list useful. I would love you hear from your experiences and your list.