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

## Duplicates
Repeated data rows in the dataset are called **duplicates**. These can arise from a number of ways. The most common are:

* The same data is entered twice by accident, such as the same article is scraped twice or booking for an online product is made twice.

* If data is being collected in online forms or surveys and the user presses the submit button twice.

* If data is collected from multiple sources.



# Duplicates
Repeated data rows in the dataset are called **duplicates**. These can arise from a number of ways. The most common are:

* The same data is entered twice by accident, such as the same article is scraped twice or booking for an online product is made twice.

* If data is being collected in online forms or surveys and the user presses the submit button twice.

* If data is collected from multiple sources.



This lesson will focus on how to deal with data that has duplicates. 

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

Duplicates

Duplicates