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

# One-sample t-test
A **One sample t-test** checks whether a population mean differs from the sample mean or not. We can use the function `ttest_1samp` from the `scipy.stats` module.  

We will be using the [Student Alcohol Consumption Dataset](https://www.kaggle.com/uciml/student-alcohol-consumption/). We will take a sample from the data and find the *mean* grade. We will check if the population mean grade ($\mu$) differs from the sample mean grade ($\bar{x}$) or not. 

**Null hypothesis $H_0$**: $\bar{x} = \mu$

**Alternate hypothesis $H_a$**: $\bar{x} \neq \mu$

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 one sample t-tests in Python.

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

One Sample t-Test

One-sample t-test