Statistics for Beginners in Data Science: Theory and Applications of Essential Statistics Concepts using Python-P2P
Statistical methods are an integral part of data science. Hence, a formal training in statistics is indispensable for data scientists. If you are keen on getting your foot into the lucrative data science and analysis universe, you need to have a fundamental understanding of statistical analysis. Besides, Python is a versatile programming language you need to master to become a career data scientist.
As a data scientist, you will identify, clean, explore, analyze, and interpret trends or possible patterns in complex data sets. The explosive growth of Big Data means you have to manage enormous amounts of data, clean it, manipulate it, and process it. Only then the most relevant data can be used.
Python is a natural data science tool as it has an assortment of useful libraries, such as Pandas, NumPy, SciPy, Matplotlib, Seaborn, StatsModels, IPython, and several more. And Python’s focus on simplicity makes it relatively easy for you to learn. Importantly, the ease of performing repetitive tasks saves you precious time. Long story short—Python is simply a high-priority data science tool.

Statistics for Beginners in Data Science: Theory and Applications of Essential Statistics Concepts using Python-P2P
English | April 17, 2020 | ASIN: B0878YNBJ6 | 196 Pages | PDF/EPUB | 7.65 MB
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