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Master the Statistics in Python and Data Science along with in-depth examples. Solid Foundation in Statistics Guaranteed

What you will learn

Introduction to the Course

Introduction to the Probability

Master the fundamentals of statistics for data science & data analytics

How to plot different types of data

Statistical Distributions

Quizzes and exercises.

Understand random variables,types & functions

Correlation,causation,spearman,kendall rank & pearson correlation

Detailed explaination of sampling methods

Bayes Theorem

Learn measures of central Tendency

Description

Welcome to the best online course on Statistics for Data Science.

Statistics are used to communicate data. With the help of Statistics understand the data and communicate our confidence in the statements.

Statistics for Data Science course is designed to give knowledge of the basic principles of statistical methods and concepts of statistical analysis using the Python programming language. This is a complete course on Statistics with Python and Data Science along with examples.

In this course, you will cover:

  • Introduction to the Statistics.

  • Introduction to the Probability.

  • Learn about the Descriptive Statistics concepts.

  • Learn about the Random Variables in Statistics.

  • Learn about Statistical Distribution in this course.

  • Learn about the Inferential Statistics topics.

  • Learn about the Correlations in the course.

  • Learn about the different Sampling Methods in the course.

  • Learn about the different types of Stochastic Processes.

  • Learn about the Regression in Statistics.

Not only this, you will get to attempt quizzes to test your knowledge. You will also have access to all the resources such as PPT and coding files used in this course.


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English
language

Content

Introduction to the Course
Why you should learn statistics and probability
Walking through the course content
Introduction to Probability
Applications of probability in Real Life
Basic Probability
Set Theory
Mutually Exclusive and Independent Events
Conditional Probability
Law of Total Probability
Bayes Theorem
Permutations and Combinations
Descriptive Statistics
Types of Data
Measures of Central Tendency
Measures of Spread
Measures of Dependence
Random Variables
Introduction to Random Variables
Types of Random Variables
Probability Mass Function
Probability Density Function
Cumulative Density Function
Statistical Distributions
Continuous vs Discrete Distributions
Types of Continuous and Discrete Distributions
Uniform Distribution
Bernoulli Distribution
Binomial Distribution
Poisson Distribution
Introduction to Normal Distribution
Importance of Normal Distribution
Concept of Skewness
Using QQ Plots to check Normal Distribution
Kurtosis
Inferential Statistics
Sample Mean and Population Mean
Central Limit Theorem
Applications of Central Limit Theorem
Bias and Variance
Maximum Likelihood Estimation
Confidence Intervals
Chebyshev’s Inequality
Weak Law of Large Numbers
Correlations
Correlation and Causation
Pearson Correlation
Kendall Rank Correlation
Spearman Correlation
Point-Biserial Correlation
Sampling Methods
Types of Sampling
Random Sampling
Stratified Sampling
Cluster Sampling
Systematic Sampling
Convenience Sampling
Judgemental Sampling
Stochastic Process
Introduction to Markov Chains
Markov Chain Limit Theorem
Regression
Introduction to Regression Analysis
Method of Least Squares
Best fit line
Outro
Conclusion
How to Get Your Certificate of Completion
Bonus Section
Bonus Lecture