In light of the accelerating Digital Transformation across industries in the past years, it has never been more relevant than it is now during the global pandemic that you should improve your digital literacy and upskill yourself with data analytics skillsets. You'll be able to understand how each of the Chief Data Office function works and roles and responsibilities underpinned each pillar which covers the key digital concepts you need to know. There is a focus on the end-to-end data quality management lifecycle and best practices in this course which are critical to achieving the vision set out in the data strategy and laying the foundations for advanced analytics use cases such as Artificial Intelligence, Machine Learning, Blockchain, Robotic Automation etc. You will also be able to check your understanding about the key concepts in the exercises and there are rich reading materials for you to better assimilate these concepts. In light of the accelerating Digital Transformation across industries in the past years, it has never been more relevant than it is now during the global pandemic that you should improve your digital literacy and upskill yourself with data analytics skillsets.

Recently, I actively started looking for a job change to Data science, and I don't have any formal education like a Master's or Ph.D. background in AI/Machine Learning. I started learning it completely out of my own interest (not just because of the hype). It was one of the challenging tracks to opt-in, especially if you are working simultaneously on some other technology. I started my journey by enrolling myself in many MOOCs(Massive Open Online Courses) and started reading multiple blogs. It slowly started making sense.

If you think technology has only credited the Retail, F&B, eCommerce, Financial, and Agriculture verticles, then it's time for you to amaze yourself with the known yet unknown fact, the educational sector. The black & whiteboard to the projector screen and after COVID-19 it's the technology that makes education alive among the students. And it's important to acknowledge that the students enjoy the use of technology at every level of their learning process. Like how workplace digital transformation helped with high productivity and employee retention rate, similarly, creative ways to use technology in the classroom has created an excitement for learning among the students. The use of technology in teaching and learning has made life easier for both the teachers and students, the communication between the students and teachers are no more in a distress spot, right from the academic calendar schedules to lessons with media and visuals, technology integration has truly benefited education institutes.

By 2021, one in four forward-thinking enterprises will push AI to new frontiers, such as holographic meetings for remote work and on-demand personalised manufacturing, as per new predictions by Forrester Research. Even today, all of us are subconsciously using Machine Learning in our daily lives. Wish to stay home and yet be social? A nascent domain that's roughly 60 years old, has changed the way humans and machines perform, that's for sure. AI will create 2.3 million jobs in 2020.

Human-AI Interaction (HAII) was taught twice in a fully remote fashion over 12 weeks at Williams College to only undergraduates. The course was organized around 11.5 modules (i.e., topics), and each module included: 2x pre-recorded lecture videos, 2-3 readings (1 research paper 2 popular media), a 7 question quiz on that module's materials, a 60 minute synchronous [remote] class meeting with 8 students, followed by discussion forum posts and 2x responses to peers. The course was implemented in Canvas, referred to as "GLOW" at Williams, but I have made available some versions of the materials via Google documents on this website (apologies for any formatting issues!). Context for each course component is below, but details can be found on the Schedule and Syllabus pages. Lectures: Pre-recorded lectures for a module were posted twice per week (Thursdays & Mondays), which were recorded in 15 minute sections and linked together in a YouTube playlist for no more than 50 minutes total, although usually around 30-40 minutes.

Data science is one of the top skills in demand as jobs in the field sees an upward trend, especially after the pandemic. As a matter of fact, many online platforms are providing online data science courses. These programs also offer certifications on their completion, making aspiring data scientists more employable. Analytics India Magazine has collated some of the top subscription-based ed-tech platforms that provide learning data science in various formats. DataCamp is an ed-tech platform purely made only for data science.

Data scientists, machine learning engineers, and AI researchers all have their own skillsets. But what is that one special thing they have in common? They are all masters of deep learning. We often hear about AI, or self-driving cars, or the'algorithmic magic' at Google, Facebook, and Amazon. But it is not magic - it is deep learning. And more specifically, it is usually deep neural networks – the one algorithm to rule them all.

In this tutorial, you will learn how to break deep learning models using image-based adversarial attacks. We will implement our adversarial attacks using the Keras and TensorFlow deep learning libraries. Imagine it's twenty years from now. Nearly all cars and trucks on the road have been replaced with autonomous vehicles, powered by Artificial Intelligence, deep learning, and computer vision -- every turn, lane switch, acceleration, and brake is powered by a deep neural network. Now, imagine you're on the highway. You're sitting in the "driver's seat" (is it really a "driver's seat" if the car is doing the driving?) while your spouse is in the passenger seat, and your kids are in the back. Looking ahead, you see a large sticker plastered on the lane your car is driving in.

'An excellent book that treats the fundamentals of machine learning from basic principles to practical implementation. The book is suitable as a text for senior-level and first-year graduate courses in engineering and computer science. It is well organized and covers basic concepts and algorithms in mathematical optimization methods, linear learning, and nonlinear learning techniques. The book is nicely illustrated in multiple colors and contains numerous examples and coding exercises using Python.' John G. Proakis, University of California, San Diego'Some machine learning books cover only programming aspects, often relying on outdated software tools; some focus exclusively on neural networks; others, solely on theoretical foundations; and yet more books detail advanced topics for the specialist.

There is a vast and growing number of Data Science resources. It can be hard to find the best ones for you. It may even be hard to find the right "Roadmap for Data Science" or "Top Skills to Learn for Data Science". I don't claim to have the best resources or the correct path to a career in Data Science. What I have is a list of useful resources and if even one of them furthers your learning my goal is accomplished.