Data Science in Python: Data Prep & EDA

Learn how to use Python & Pandas to gather, clean, explore and analyze data for Data Science and Machine Learning

What you'll learn

• Master the core building blocks of Python for data science BEFORE applying machine learning algorithms
• Scope data science projects by clearly defining the goals, techniques, and data sources needed for your analysis
• Import and export flat files, Excel workbooks, and SQL database tables using Pandas
• Clean data by converting data types, handling common data issues, and creating new columns for analysis
• Perform exploratory data analysis (EDA) by sorting, filtering, grouping, and visualizing data to discover patterns and insights
• Prepare data for machine learning models by joining tables, aggregating rows, and applying feature engineering techniques

Requirements

• Jupyter Notebooks (free download, we'll walk through the install)
• Familiarity with base Python and Pandas is recommended, but not required

Description

This is a hands-on, project-based course designed to help you master the core building blocks of Python for data science.

We'll start by introducing the fields of data science and machine learning, discussing the difference between supervised and unsupervised learning, and reviewing the data science workflow we'll be using throughout the course.

From there we'll do a deep dive into the data prep & EDA steps of the workflow. You'll learn how to scope a data science project, use Pandas to gather data from multiple sources and handle common data cleaning issues, and perform exploratory data analysis using techniques like filtering, grouping, and visualizing data.

Throughout the course, you'll play the role of a Jr. Data Scientist for Maven Music, a streaming service that’s been struggling with customer churn. Using the skills you learn throughout the course, you'll use Python to gather, clean, and explore the data to provide insights about their customers.

Last but not least, you'll practice preparing data for machine learning models by joining multiple tables, adjusting row granularity, and engineering useful fields and features.

COURSE OUTLINE:

Intro to Data Science

Introduce the field of data science, review essential skills, and introduce each phase of the data science workflow

Scoping a Project

Review the process of scoping a data science project, including brainstorming problems and solutions, choosing techniques, and setting clear goals

Gathering Data

Read flat files into a Pandas DataFrame in Python, and review common data sources & formats, including Excel spreadsheets and SQL databases

Cleaning Data

Identify and convert data types, find and fix common data issues like missing values, duplicates, and outliers, and create new columns for analysis

Exploratory Data Analysis

Explore datasets to discover insights by sorting, filtering, and grouping data, then visualize it using common chart types like scatterplots & histograms

MID-COURSE PROJECT

Put your skills to the test by cleaning, exploring, and visualizing data from a brand-new data set containing Rotten Tomatoes movie ratings

Preparing for Modeling

Structure your data so that it’s ready for machine learning models by creating a numeric, non-null table and engineering new features

FINAL COURSE PROJECT

Apply all the skills learned throughout the course by gathering, cleaning, exploring, and preparing multiple data sets for Maven Music

Who this course is for:

• Data scientists looking to learn core techniques and best practices for data prep and exploratory data analysis
• Python users who want to build the core skills required before applying AI and machine learning models
• Data analysts or BI experts looking to transition into a data science role
• Anyone interested in learning one of the most popular open source programming languages in the world