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.

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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 will look at the third stage of the Data Science Lifecycle - *Data Cleaning*. But before we look at what steps are involved in Data Cleaning, a question arises; why do we need to clean data?


# Why clean data?
The data that we receive and use is not perfect. Numerous factors such as data collection from multiple sources, or data corruption while storing or retrieving data, human errors in entering data, data loss while transferring data on some network, etc, can lead to incomplete, inconsistent, and incorrect data. If we use data as received in our analysis, then we will perform incorrect analysis and any conclusion drawn from the data will be wrong. Therefore, data cleaning is a necessary step before doing any analysis on the data.

This lesson will focus on why data cleaning is necessary and go into some methods to clean data.

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

Introduction to Data Cleaning

Why clean data?