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.

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

## E-Commerce data
In this lesson, you are going to be tested on exploring E-commerce data. The [dataset](https://archive.ics.uci.edu/ml/datasets/online+retail#) was made available on the UCI Machine Learning Repository. This is a transnational data set that contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail. We will be using a **sample** of it. 

In the below exercise, you will be writing functions for every task. The functions will receive a dataframe `df` as an input argument. Your task will be to perform the required operations to answer a question and return the answer to that. 

# E-Commerce data
In this lesson, you are going to be tested on exploring E-commerce data. The [dataset](https://archive.ics.uci.edu/ml/datasets/online+retail#) was made available on the UCI Machine Learning Repository. This is a transnational data set that contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail. We will be using a **sample** of it. 

In the below exercise, you will be writing functions for every task. The functions will receive a dataframe `df` as an input argument. Your task will be to perform the required operations to answer a question and return the answer to that. 

This lesson tests the learners on EDA on an e-commerce dataset. 

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

Exercise: Exploring E-Commerce

E-Commerce data