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

**Inconsistency** in data arises due to errors in collecting data. For instance, if the data was collected from multiple sources, or if the data was collected by multiple people who did not follow the same format of collecting data, then there is a high chance of inconsistencies in the data.

In this lesson, we will be cleaning the [Credit Cards Default Dataset](https://www.kaggle.com/uciml/default-of-credit-card-clients-dataset). This dataset is a very good example of the kind of inconsistencies that are present in most datasets. 


This lesson will focus on some of the common inconsistencies present in datasets and how to deal with them using pandas.

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

Inconsistent Data