Python Data Science: Data Prep & EDA with Python
Python Data Science: Data Prep & EDA with Python
Learn Python + Pandas for data cleaning, profiling & EDA, and prep data for machine learning & data science with Python
Product Brand: Udemy
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Udemy Coupon Code for Python Data Science: Data Prep & EDA with Python Course. Learn Python + Pandas for data cleaning, profiling & EDA, and prep data for machine learning & data science with Python
Created by Maven Analytics, Alice Zhao | 8.5 hours on-demand video course | 2 downloadable resources
Python Data Science Course Overview
Python Data Science: Data Prep & EDA with Python
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.
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
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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
