env.tar.gz

Python3

Ready to move past Excel for complex business analysis? Then you’ll find this course very helpful.

This hands-on introductory Data Science course is aimed at professionals and students who don't have any experience with programming. It will help you advance your career by preparing you to conduct meaningful data analysis in Python on any dataset — large or small.

You’ll begin with the fundamentals of Python, with focus on CSV files in Python, covering concepts like data preprocessing and Exploratory Data Analysis (EDA). In the second half, you'll focus on predictive and inferential analysis using statistical and machine learning techniques, and learn how these techniques can help solve business problems.

Data Science for Non-Programmers

## Two-sample t-test
A **two-sample t-test** checks whether means of two independent samples differ from each other. We can use the function `ttest_ind` from the `scipy.stats` module to perform the two-sample t-test.

We will be using the [Student Alcohol Consumption Dataset](https://www.kaggle.com/uciml/student-alcohol-consumption/). We will divide the data into two groups based on alcohol consumption. Then we will find the *mean* grade for both groups. We will check if the mean grades for both groups differ from each other or not.

**Null hypothesis $H_0$**: $\bar{x_1} = \bar{x_2}$

**Alternate hypothesis $H_a$**: $\bar{x_1} \neq \bar{x_2}$

We choose $\alpha$ to be at $95\%$ confidence level which means $\alpha =1 - (conf.level) = 1 - 0.95 = 0.05$

# Two-sample t-test
A **two-sample t-test** checks whether means of two independent samples differ from each other. We can use the function `ttest_ind` from the `scipy.stats` module to perform the two-sample t-test.

We will be using the [Student Alcohol Consumption Dataset](https://www.kaggle.com/uciml/student-alcohol-consumption/). We will divide the data into two groups based on alcohol consumption. Then we will find the *mean* grade for both groups. We will check if the mean grades for both groups differ from each other or not.

**Null hypothesis $H_0$**: $\bar{x_1} = \bar{x_2}$

**Alternate hypothesis $H_a$**: $\bar{x_1} \neq \bar{x_2}$

We choose $\alpha$ to be at $95\%$ confidence level which means $\alpha =1 - (conf.level) = 1 - 0.95 = 0.05$

This lesson will focus on how to perform a two-sample t-test in Python.

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

Two Sample t-Test

Two-sample t-test