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

# Aggregation
**Aggregation** is the procedure of converting a large number of values, or a dataset, into a single value or quantity aimed to summarize or describe the data. During data analysis, we always want to summarize data in one way or another. When we take a *sum*, *count* the number of items, or take the *average* of some values we aggregate data. Common aggregation methods are:
* *sum*
* *count*
* *maximum*
* *minimum*
* *average*

Aggregation is an essential step in analyzing data as it tells us the nature of the data in a single quantity. Let's look at examples on our [California Census Housing Dataset](https://www.kaggle.com/camnugent/california-housing-prices) to see how this is true. 

This lesson introduces us to aggregation of data and focuses on how we can use entire columns to aggregate using Pandas.

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

Aggregating Data

Aggregation