The year 2022 has seen significant progress in terms of the adoption and expansion of AI and data science. The year has been rightly called the year of Text-to-Anything, with some phenomenal artwork being generated by AI-based tools like Dalle-2, Imagen, Midjourney and StableDiffusion. Striding on, we expect generative AI to become more accessible and expand into newer horizons. As governments and enterprises have increasingly moved towards digitisation with data driving their operations and decision-making, very often, questions cropped up about data privacy and security. One of the prominent is the Indian Government's initial scrapping of the Data Protection Bill to replace it with a more comprehensive Digital Personal Data Protection Bill. With more regulations introduced, it is likely that future developments in data science and the AI field will be based on the framework surrounding data privacy and security.

In our previous article, we saw some of the free courses in data science and AI that were launched this year. With this article, we are listing down some of the degree and diploma programmes that were launched by Indian institutes, colleges and universities along with analytics and data science training institutes. The course list includes all the graduate, postgraduate, diploma and certificate programmes that were launched in domains such as AI, data science, analytics, cybersecurity, blockchain and other new-age technologies. These courses are not free and have programme fee associated, which in most cases have been mentioned here. The list is in no particular order.

Top Data Science is looking for a Senior Data Scientist to join our growing team. You own a deep understanding of the landscape of machine learning and data science with experience in software engineering? You possess professional communication skills so that you are able to coach team members and convince our clients when needed? In the role of a senior, you are in charge of being a hands-on data scientist in projects. You could be coaching others to learn more, and definitely we value if you continuously acquire knowledge of new technologies yourself.

Python continues to lead the top Data Science platforms, but R and RapidMiner hold their share; Almost 50% have used Deep Learning tools; SQL is steady; Consolidation continues.

Which Data Science / Machine Learning methods and algorithms did you use in 2018/2019 for a real-world application? This, in turn, mirrors the results of the 2017 poll, which found that the top 10 methods remained unchanged from the 2016 poll (although, again, they were in a different order). The average respondent used 7.4 methods/algorithms, which is in-line with both the 2017 and 2016 results. Below is a comparison of the top methods and algorithms in this year's poll with their 2017 shares. The most notable increases this year were found in the usage of various neural network technologies, including GANs, RNNs, CNNs, reinforcement learning, and vanilla deep neural networks.

Not only can you follow the work happening in different domains, but you can also collaborate on multiple open source projects. All tech companies, from Google to Facebook, upload their open source project codes on GitHub so the wider coding / ML community can benefit from it. But, if you are too busy, or find following GitHub difficult, we bring you a summary of top repositories month on month. You can keep yourself updated with the latest breakthroughs and even replicate the code on your own machine! This month's list includes some awesome libraries.

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In 2014, three young men from far-flung parts of the world teamed up in Finland with an audacious plan that could soon help doctors save more lives, not to mention money, and chart a new course for healthcare. Oguzhan Gencoglu, who hails from Turkey, is an AI and machine-learning whiz currently working on his Ph.D. in computer science, Hung Ta is a Vietnamese math prodigy with a doctorate in biotechnology, and Timo Heikkinen is a Finnish entrepreneur with a software industry background. Together, they launched Top Data Science, an AI startup based in Helsinki that's developing software that can make sense of millions of data points, alert doctors to unseen medical patterns, help them diagnose disease and track patients during treatment. Their "intelligent" code is already analyzing thousands of MRI images and could one day help radiologists at the Helsinki University Central Hospital diagnose prostate cancer. Another set of algorithms is crunching data from the hospital's intensive care unit and using it to identify high-risk cases that may soon need urgent medical care, as well as flag patients who are progressing well and who could be released to standard hospital care.

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The average respondent used 7.7 tools/methods, similar to 2016 poll. Next, we compared the top 16 methods in this year's poll with their share last year - see Figure 1. We note a significant increase in Random Forests, Visualization, and Deep Learning share of usage, and decline in K-nn, PCA, and Boosting. Gradient Boosting Machines was a new entry in 2017. Deep Learning, despite its amazing successes, is reported used by only about 20% of KDnuggets readers.