You do not need a math or statistics degree to succeed as a data scientist but by taking up the list of free online statistics courses you can have an added advantage over other aspiring data scientists as these statistic courses online will equip you with all the basic concepts of statistical thinking needed for doing data science. These statistics courses online will help data science beginners learn the underlying theoretical concepts upfront without having to read a complete book. There are many free online statistics courses and resources that can help data science beginners learn the core concepts of statistics needed for doing data science. Machine Learning and Statistics are closely related disciplines and to master modern machine learning it is necessary to understand the statistical machine learning approach. Introduction to Statistics for Machine Learning – Learn basic machine learning concepts to understand how statistics fits in.These are major concepts for developing most of the machine learning models and hence it is important to master them. The level of uncertainty before data collection is often referred to as prior probability and after data collection is referred to as posterior probability. Bayesian Thinking Concepts – Conditional Probability, Posteriors, Priors, and Maximum Likelihood –Bayesian Thinking in statistics involves using probability to model sampling processes and measure uncertainty if any before data collection.All this decision-making process requires data scientists to have a strong foundation in core statistics concepts. Descriptive Statistics, Distributions, Regression, and Hypothesis Testing – The job role of a data scientist involves making meaningful decisions on a daily basis which could vary from making major decisions like designing the team’s R&D strategy or can be a small business decision on how to tune a machine learning model.The most important probability and statistical concepts required to learn data science include – Stock Price Prediction Project using LSTM and RNN View Project
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