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

# Grouping
During analysis, we may want to gather information about specific types of items in a column. For instance, we may want to separate the data for household blocks based on their proximity to the ocean in our [California Housing Dataset](https://www.kaggle.com/camnugent/california-housing-prices) and calculate the total population of household blocks in each type of area. 

We simply want to *group* data for each type of area and then aggregate population. This can be easily done with Pandas using the function `groupby`. Below is an illustration of how grouping is done. 

As we can see in the above illustration, for every row that has the value **NEAR OCEAN** in the `ocean_proximity` column, the `population` values go to the red block and these values are aggregated to calculate a sum. 

The same process is followed for all other values of `ocean_proximity` column. Groups are created and we say that the data was 
*grouped by* `ocean_proximity`. 

This lesson introduces us to grouping data and focuses on how grouping can be done with Pandas in Python.

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

Grouping Data

Grouping