Statistics for Data Analysis Using Python
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.
Who this course is for:
- Anyone who want to use statistics to make fact based decisions.
- Anyone who wants to learn Python for career in data science.
- Anyone who thinks Statistics is confusing and wants to learn it in plain and simple language.
- Basic school level mathematics will be helpful.
- You will need to download and install Python on your PC or laptop.
Last Updated 1/2021
Statistics for Data Analysis Using Python.zip (6.6 GB) | Mirror