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 this chapter, we have seen how we can extract useful information from raw data very easily using *pandas* in Python. But we have only scratched the surface. A lot more can be done to obtain useful insights from the data. Professionals can use their domain expertise to perform different kinds of analysis on the data. In this lesson, we will explore a dataset from the perspective of a business or a marketing professional. We will be doing an *RFM analysis* of the [Sample Sales Data](https://www.kaggle.com/kyanyoga/sample-sales-data) that we have been using. 

This lesson will focus on how to do RFM analysis in Python with Pandas as an example of the usability of pandas. 

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

Business Example: RFM Analysis in Python