Statistics and Probability for DS | Data Science | Edureka | DS Rewind - 1

This session on Statistics And Probability will cover all the fundamentals of stats and probability.

00:00:00 Introduction
00:01:11 Probability
00:02:13 Probability - Terminologies
00:03:33 Probability - Types of Events
00:04:46 Probablity - Distribution
00:05:10 Probability - PDF
00:07:00 Probability - Normal Distribution
00:11:36 Probability - Central Limit Theorem
00:11:40 Probability - Types of Probability
00:12:01 Probability - Marginal Probability
00:12:48 Probability - Joint Probability
00:13:14 Probability - Conditional Probability
00:15:11 Probability - Bayes Theorem
00:16:30 Probability - Applications
00:17:12 Statistics
00:17:40 Statistics - Terminologies
00:19:12 Statistics - Sampling technique
00:21:30 Statistics - Types of Statistics
00:24:26 Statistics - Measures of Center
00:26:02 Statistics - Measures of Spread
00:32:22 Statistics - Hands-on
00:33:26 Statistics - Information Gain
00:38:32 Statistics - Inferential Statistics

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About the Master's Program

This program follows a set structure with 6 core courses and 8 electives spread across 26 weeks. It makes you an expert in key technologies related to Data Science. At the end of each core course, you will be working on a real-time project to gain hands on expertise. By the end of the program you will be ready for seasoned Data Science job roles.

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Topics Covered in the curriculum:

Topics covered but not limited to will be : Machine Learning, K-Means Clustering, Decision Trees, Data Mining, Python Libraries, Statistics, Scala, Spark Streaming, RDDs, MLlib, Spark SQL, Random Forest, Naïve Bayes, Time Series, Text Mining, Web Scraping, PySpark, Python Scripting, Neural Networks, Keras, TFlearn, SoftMax, Autoencoder, Restricted Boltzmann Machine, LOD Expressions, Tableau Desktop, Tableau Public, Data Visualization, Integration with R, Probability, Bayesian Inference, Regression Modelling etc.

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