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

In the last chapter, we learned how to explore data using different graphs and extract information from the data. We discovered relationships between different variables in our datasets, but how do we decide whether these relationships are real or just by coincidence? How do we decide whether we can make a generalization about the dataset or not? This is where *inferential statistics* comes in.

This lesson will introduce statistical inference and point estimates, along with the central limit theorem. 

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

The Basics of Statistical Inference