Statistics & Probability for Data Science | Data Science Tutorial | Edureka | DS Rewind - 1

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This session on Statistics And Probability will cover all the fundamentals of stats and probability concepts used in Data Science.
Topics covered in this video:
00:00:00 Introduction
00:00:33 Agenda
00:01:20 What is Data?
00:02:48 Categories of data
00:03:27 What is Statistic?
00:05:18 Basic terminologies in Statistics
00:06:23 Sampling techniques
00:12:49 types of Statistics
00:14:18 Descriptive Statistics
00:26:11 Probability

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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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