Gain insights into conducting meaningful data analysis in Python without prior programming experience. Discover Python fundamentals, data preprocessing, EDA, and predictive analytics for solving business problems.

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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

# Hypothesis testing
During our analysis of the different datasets, we are often concerned with questions like whether males default more than females? Do self-driving cars crash more than normal cars? Does drug X help prevent/treat disease Y? To answer these questions, we can use another statistical technique known as **Hypothesis Testing**.

During data exploration, we discovered interesting patterns hidden in the data. Hypothesis testing enables us to confirm whether these patterns were present in the data by luck or by some real phenomena. 

## Null and Alternate hypothesis
The aim of the hypothesis test is to determine whether the null hypothesis can be rejected or not. The **null hypothesis** is a statement that assumes that nothing interesting is going on, or no relationship is present between two variables, or that there is no difference between a sample and a population. 

For instance, if we suspect that males default more than females, the null hypothesis would be that males do not default more than females. If there is little or no evidence against the null hypothesis, we accept the null hypothesis. Otherwise, we reject the null hypothesis in favor of the **alternate hypothesis**, which states that something interesting is going on, or there is a relationship between two variables, or that the sample is different from the population.

To reiterate, the null hypothesis is assumed true and statistical evidence is required to reject it in favor of the alternative hypothesis. 


This lesson will focus on the basics of hypothesis testing and how to perform different types of hypothesis tests.

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

Hypothesis Testing

Hypothesis testing