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

## Outlier detection
Detecting outliers is a very important step in data cleaning and exploring. It gives us an idea of the anomalies in the data which can give us valuable insights into the data. So, how can we detect outliers? 

Outliers can be detected both visually and mathematically. Some plots are very helpful in visualizing outliers, such as box plots and scatter plots. However, it is sometimes tricky to decide whether or not to remove the outliers. We should remove outliers when we are certain that these outliers were results of some errors. 

We will discuss some of the methods to detect and remove outliers. We will be using the [Sample Sales Data](https://www.kaggle.com/kyanyoga/sample-sales-data). The data is in the file *sales_data.csv*.

# Outlier detection
Detecting outliers is a very important step in data cleaning and exploring. It gives us an idea of the anomalies in the data which can give us valuable insights into the data. So, how can we detect outliers? 

Outliers can be detected both visually and mathematically. Some plots are very helpful in visualizing outliers, such as box plots and scatter plots. However, it is sometimes tricky to decide whether or not to remove the outliers. We should remove outliers when we are certain that these outliers were results of some errors. 

We will discuss some of the methods to detect and remove outliers. We will be using the [Sample Sales Data](https://www.kaggle.com/kyanyoga/sample-sales-data). The data is in the file *sales_data.csv*.

This lesson will focus on how to detect outliers in the data and what to do with them.

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

Outlier Detection and Removal

Outlier detection