It certainly feels like winter is here judging by the lack of sunlight. But a December like no other, as we have a World Cup to watch – although half empty, beer-less, air-conditioned stadiums in repressive Qatar does not sit well …Perhaps time for a breather, with a wrap up of data science developments in the last month. Following is the December edition of our Royal Statistical Society Data Science and AI Section newsletter. Hopefully some interesting topics and titbits to feed your data science curiosity. If you like these, do please send on to your friends- we are looking to build a strong community of data science practitioners.

Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to self-learn and improve over time without being explicitly programmed. In short, machine learning algorithms are able to detect and learn from patterns in data and make their own predictions. In traditional programming, someone writes a series of instructions so that a computer can transform input data into a desired output. Instructions are mostly based on an IF-THEN structure: when certain conditions are met, the program executes a specific action. Machine learning, on the other hand, is an automated process that enables machines to solve problems and take actions based on past observations.

Create supervised machine learning algorithms to predict classes. According to Harvard Business Review, Data Scientist is the sexiest job of the 21st century. With exponential growth in the amount of data generated every day, the world needs specialists who can extract value from that data. Data science had a tremendous impact on many industries, but machine learning has always been a key driver to digital transformation and automatization. Machine learning is now everywhere around us: in music, healthcare, social networks, even in chess.

Make powerful analysis, Make robust Machine Learning models Master Machine Learning on Python Know which Machine Learning model to choose for each type of problem Implement Machine Learning Algorithms Explore how to deploy your machine learning models. "Algorithms that parse data, learn from that data, and then apply what they've learned to make informed decisions" An easy example of a machine learning algorithm is an on-demand music streaming service. For the service to make a decision about which new songs or artists to recommend to a listener, machine learning algorithms associate the listener's preferences with other listeners who have a similar musical taste. This technique, which is often simply touted as AI, is used in many services that offer automated recommendations. Machine learning fuels all sorts of automated tasks that span across multiple industries, from data security firms that hunt down malware to finance professionals who want alerts for favorable trades.

How much does a Data Scientist make in the United States? The national average salary for a Data Scientist is US$1,20,718 per year in United States, 2.8k salaries reported, updated at July 15, 2021 (source: glassdoor)

Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to self-learn and improve over time without being explicitly programmed. In short, machine learning algorithms are able to detect and learn from patterns in data and make their own predictions. In traditional programming, someone writes a series of instructions so that a computer can transform input data into a desired output. Instructions are mostly based on an IF-THEN structure: when certain conditions are met, the program executes a specific action. Machine learning, on the other hand, is an automated process that enables machines to solve problems and take actions based on past observations.