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

## Unsupervised learning
**Unsupervised learning** is when we use unlabeled data to allow a model to learn relationships between data observations and pick up on underlying patterns. Most data in the world is unlabeled, which makes unsupervised learning a very useful method of machine learning. The most common algorithms for unsupervised learning are *clustering* algorithms. We will look at some of these later in this lesson.

# Unsupervised learning
**Unsupervised learning** is when we use unlabeled data to allow a model to learn relationships between data observations and pick up on underlying patterns. Most data in the world is unlabeled, which makes unsupervised learning a very useful method of machine learning. The most common algorithms for unsupervised learning are *clustering* algorithms. We will look at some of these later in this lesson.

This lesson will introduce clustering techniques in Python for unsupervised machine learning. 

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

Clustering for Unsupervised Learning

Unsupervised learning