![]() Statistics Applications – Math And Statistics For Data Science – Edureka ![]() Several Statistical functions, principles and algorithms are implemented to analyse raw data, build a Statistical Model and infer or predict the result. In simple words, Statistics can be used to derive meaningful insights from data by performing mathematical computations on it. Statistics is used to process complex problems in the real world so that Data Scientists and Analysts can look for meaningful trends and changes in Data. Statistics – Math And Statistics For Data Science – Edureka Statistics is a Mathematical Science pertaining to data collection, analysis, interpretation and presentation. Now the question arises, what exactly is Statistics? It is important to know the techniques behind various Machine Learning algorithms in order to know how and when to use them. Math and Stats are the building blocks of Machine Learning algorithms. To become a successful Data Scientist you must know your basics. Here’s a list of topics I’ll be covering in this Math and Statistics for Data Science blog: To get in-depth knowledge on Data Science and the various Machine Learning Algorithms, you can enroll for live Data Science with Python Training Course by Edureka with 24/7 support and lifetime access. In this blog post, you will understand the importance of Math and Statistics for Data Science and how they can be used to build Machine Learning models. Mathematics is embedded in each and every aspect of our lives.Īlthough having a good understanding of programming languages, Machine Learning algorithms and following a data-driven approach is necessary to become a Data Scientist, Data Science isn’t all about these fields. In fact, Mathematics is behind everything around us, from shapes, patterns and colors, to the count of petals in a flower. Math and Statistics for Data Science are essential because these disciples form the basic foundation of all the Machine Learning Algorithms. “Data Scientist is a person who is better at statistics than any programmer and better at programming than any statistician.”
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