Statistics for Data Analysis Using Python

Learn Python from Basics • Descriptive, Inferential Statistics • Plots for Data Visualization • Data Science

What you'll learn:

• Python from basics - No prior knowledge required
• Statistics from basics - No prior knowledge required
• You will first learn the basic statistical concepts, followed by application of these concepts using Python. This course is a nice combination of theory and practice.
• Inferential Statistics - One and two sample z, t, Chi Square, F Tests, ANOVA and more.
• Descriptive Statistics - Mean, Mode, Median, Standard Deviation, Variance and Interquartile Range
• Probability Distributions - Normal, Binomial and Poisson

Perform simple or complex statistical calculations using Python! - You don't need to be a programmer for this :)

You are not expected to have any prior knowledge of Python. I will start with the basics. Coding exercises are provided to test your learnings.

The course not only explains, how to conduct statistical tests using Python but also explains in detail, how to perform these using a calculator (as if, it was the 1960s). This will help you in gaining the real intuition behind these tests.

Learn statistics, and apply these concepts in your workplace using Python.

The course will teach you the basic concepts related to Statistics and Data Analysis, and help you in applying these concepts. Various examples and data-sets are used to explain the application.

I will explain the basic theory first, and then I will show you how to use Python to perform these calculations.

The following areas of statistics are covered:

Descriptive Statistics - Mean, Mode, Median, Quartile, Range, Inter Quartile Range, Standard Deviation.

Data Visualization - Commonly used plots such as Histogram, Box and Whisker Plot and Scatter Plot, using the Matplotlib.pyplot and Seaborn libraries.

Probability - Basic Concepts, Permutations, Combinations

Population and Sampling - Basic concepts

Probability Distributions - Normal, Binomial and Poisson Distributions

Hypothesis Testing - One Sample and Two Samples - z Test, t-Test, F Test and Chi-Square Test

ANOVA - Perform Analysis of Variance (ANOVA) step by step doing the manual calculation and by using Python.

The Goodness of Fit and the Contingency Tables.