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

Exploratory Data Analysis is all about exploring relationships in the dataset that might be hidden or might not be easy to spot just by looking at the dataset. We will try to explore these kinds of relationships in the [Default of Credit Card Clients Dataset](https://www.kaggle.com/uciml/default-of-credit-card-clients-dataset). We will use the cleaned version of the dataset from the lesson [Inconsistent Data](https://www.educative.io/courses/data-science-for-non-programmers/inconsistent-data). The details of individual columns are mentioned below.



This lesson will focus on how to explore relationships between different categorical variables in the dataset with examples. 

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

Exploring Categorical Quantities