Python Data Science: Data Prep & EDA with Python from Udemy

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

We'll start by introducing the fields of data science and machine learning, discussing the difference between supervised and unsupervised learning, and reviewing the Python 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 Python and Pandas to gather data from multiple sources and handle common data cleaning issues, and perform exploratory data analysis (EDA) 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 data science and machine learning models by joining multiple tables, adjusting row granularity, and engineering useful fields and features.

COURSE OUTLINE:

Intro to Data Science & Machine Learning
- 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 quality issues like missing values, duplicates, and outliers, and create new columns for analysis
Exploratory Data Analysis (EDA)
- 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

Ready to dive in? Join today and get immediate5 hours of high-quality video

16 homework assignments

7 quizzes

2 projects (1 mid-course, 1 final)

Data Science in Python: Data Prep & EDA ebook (190+ pages)

Downloadable project files & solutions

Expert support and Q&A forum

30-day Udemy satisfaction guarantee

If you're an aspiring data scientist or business intelligence professional looking for an introduction to the world of machine learning and data science with Python and Pandas, this is the course for you.

Happy learning.

-Alice Zhao (Python Expert & Data Science Instructor, Maven Analytics)

Looking for our full business intelligence stack? Search for "Maven Analytics" to browse our full course library, including Excel, Power BI, MySQL, Tableau and Machine Learning courses.

See why our courses are among the TOP-RATED on Udemy:

"Some of the BEST courses I've ever taken. I've studied several programming languages, Excel, VBA and web dev, and Maven is among the very best I've seen. " Russ C.

"This is my fourth course from Maven Analytics and my fourth 5-star review, so I'm running out of things to say. I wish Maven was in my life earlier. " Tatsiana M.

"Maven Analytics should become the new standard for all courses taught on Udemy. " Jonah M.

What's inside

Learning objectives

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

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

Syllabus

Getting Started

Course Introduction

About This Series

Course Structure & Outline

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Provides hands-on experience with Python and Pandas, which are essential tools for data analysis and manipulation in the field

Uses a real-world project with Maven Music to provide practical experience in addressing customer churn, a common business problem

Covers data preparation techniques like joining tables and feature engineering, which are crucial for building effective machine learning models

Includes a mid-course project using Rotten Tomatoes movie ratings, offering an opportunity to apply learned skills to a new dataset

Requires installing Anaconda and Jupyter Notebook, which may require learners to ensure their machines meet the minimum specifications

Teaches Pandas, which is a library that has been updated frequently since 2008, so learners should ensure they are using the most recent version

Reviews summary

Practical hands-on data prep & eda

According to learners, this course offers a largely positive experience, particularly for those seeking practical skills in Python for data science. Students highlight the hands-on approach and relevant projects as major strengths, finding the content directly applicable. The instructor's clarity is frequently praised, making complex topics accessible. While providing a solid foundational understanding of data preparation and exploratory data analysis, some reviewers note it's a starting point requiring further study for advanced techniques. Overall, it's seen as a highly effective introduction.

Pace is generally well-suited for beginners to intermediate learners.

"The pace was just right – not too fast for beginners, but challenging enough to keep you engaged."

"Moves at a comfortable pace, allowing time to grasp each concept before moving on."

"Good pacing, builds knowledge step-by-step."

"Suitable pace if you already have a basic understanding of Python syntax."

Skills taught are highly applicable to professional roles.

"The skills taught here are exactly what I need for my aspiring data science role."

"I can immediately apply what I learned about data cleaning and EDA in my current job."

"Relevant content for anyone looking to get into data analysis or data science professionally."

"This course fills a critical gap in foundational data skills needed in business intelligence."

Provides a strong base for data science and Python skills.

"It provided me with a solid foundation in using Python and Pandas for data cleaning and exploration."

"Great introductory course to the data science workflow steps of data prep and EDA."

"I feel much more confident tackling real-world data problems after completing this course."

"Excellent course to get started with Python for data analysis."

Emphasis on doing and coding enhances learning significantly.

"This hands-on, project-based format is exactly what I needed. Learning by doing is the best way for coding."

"The course isn't just theoretical; you're constantly coding and applying the techniques which is great."

"Assignments and demos reinforced the lecture material well. It's not just passive learning."

"I learn best with practical examples, and this course provides plenty of those."

Instructor explains complex concepts with remarkable clarity.

"Alice is a fantastic instructor. She explains things very clearly and makes sure you understand the 'why' behind each step."

"The explanations are concise and easy to follow, even for someone relatively new to data science concepts."

"Instructor's delivery and ability to break down technical concepts is excellent. Never felt lost during the lectures."

"I appreciate how the instructor walked through the code step-by-step in the demos."

Course projects are highly praised for their real-world relevance.

"The projects were incredibly useful for applying the concepts taught. They feel very real-world and challenging in a good way."

"The hands-on coding and projects are the strongest part of the course for me. I learned more doing the projects than just watching lectures."

"Loved working through the case studies. The project files provided were also super helpful for following along."

"The mid-course and final projects really solidify your understanding of the data prep and EDA workflow."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Python Data Science: Data Prep & EDA with Python with these activities:

Review Python Fundamentals

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Reinforce your understanding of Python fundamentals, including data types, control flow, and functions, to ensure a solid foundation for the data science concepts covered in the course.

Browse courses on Python Basics

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Review Python syntax and data structures.
Practice writing basic Python functions.
Complete online Python tutorials or exercises.

Practice Pandas Data Manipulation

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Sharpen your Pandas skills by practicing data manipulation techniques like filtering, grouping, and merging DataFrames, which are essential for data preparation and EDA.

Show steps

Work through Pandas tutorials on data selection and filtering.
Practice grouping and aggregating data using Pandas.
Experiment with merging and joining different DataFrames.

Read 'Python for Data Analysis' by Wes McKinney

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Supplement your learning with a comprehensive guide to data analysis using Pandas, covering data cleaning, transformation, and analysis techniques in detail.

View Python for Data Analysis on Amazon

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Read the chapters on data cleaning and transformation.
Work through the examples in the book using your own datasets.
Refer to the book as a reference while working on course projects.

Four other activities

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Show all seven activities

Complete Pandas Exercises on Kaggle

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Reinforce your Pandas skills by completing practical exercises on Kaggle, focusing on data manipulation, cleaning, and EDA techniques.

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Find Pandas exercises on Kaggle.
Complete the exercises, focusing on data manipulation and cleaning.
Compare your solutions with others and learn from their approaches.

Create a Data Cleaning Cheat Sheet

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Solidify your understanding of data cleaning techniques by creating a cheat sheet summarizing common data issues and their solutions using Pandas.

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Identify common data cleaning issues.
Research Pandas functions for addressing each issue.
Create a cheat sheet with code examples and explanations.

Analyze a Public Dataset

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Apply your data preparation and EDA skills by working on a personal project involving a public dataset, such as those available on Kaggle or UCI Machine Learning Repository.

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Choose a public dataset that interests you.
Clean and prepare the data using Pandas.
Perform EDA to discover insights and patterns.
Document your findings in a report or presentation.

Read 'Storytelling with Data' by Cole Nussbaumer Knaflic

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Enhance your data visualization skills by learning how to create compelling visuals that effectively communicate insights from your EDA.

View Daphne Draws Data: A Storytelling with Data... on Amazon

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Read the chapters on data visualization principles.
Analyze examples of effective and ineffective visualizations.
Apply the principles to improve your own data visualizations.

Career center

Learners who complete Python Data Science: Data Prep & EDA with Python will develop knowledge and skills that may be useful to these careers:

Data Scientist

A data scientist uses programming, statistics, and domain expertise to extract meaningful insights from data. This course directly prepares you for the data preparation and exploratory data analysis aspects of a data scientist's work, especially in Python. The course helps build a foundation in using Pandas for data manipulation, cleaning, and visualization, all essential skills a data scientist requires to understand a dataset and create value. In addition, this course includes a realistic simulated project that gives learners experience in the challenges inherent in real-world situations. This course may be useful to future data scientists.

See salaries and explore the career path for Data Scientist

Data Analyst

A data analyst examines data sets to report trends and insights. This course may provide an introduction to the core skills used by a data analyst, particularly the use of Python and Pandas to manipulate, clean, and explore data. The course walks through the steps of a data science workflow. The data analyst may use these skills to clean and transform data into digestible findings. The hands-on projects in the course offers practical experience with the kind of data analysis that one will perform as a data analyst.

See salaries and explore the career path for Data Analyst

Business Intelligence Analyst

A business intelligence analyst transforms data into actionable insights to inform business decisions. This course may help one prepare for this role by focusing on practical data gathering, cleaning, and exploratory data analysis (EDA) using Python with Pandas. Business intelligence analysts conduct analysis with data, and this course provides training on a wide range of tools used for that purpose. Through the project-based approach, one gains essential hands-on experience. This course may be useful for an entry-level business intelligence analyst.

See salaries and explore the career path for Business Intelligence Analyst

Machine Learning Engineer

A machine learning engineer builds and maintains machine learning systems. This course helps build a foundation by covering the steps of data preparation and exploratory analysis, which are crucial for building models. A machine learning engineer must be proficient in cleaning and preparing data for models, and this course provides an introduction to the basic techniques. The course includes hands-on projects where you use Python to handle data challenges, mimicking the experience of a machine learning engineer.

See salaries and explore the career path for Machine Learning Engineer

Statistician

A statistician collects, analyzes, and interprets numerical data. A statistician often works with the kind of data that is covered in this course, and therefore may find the data prep and exploratory analysis skills helpful. The course offers a hands-on approach to using Python for data analysis, which will be very useful in a statistician's work. This course may be useful for an aspiring statistician.

See salaries and explore the career path for Statistician

Quantitative Analyst

A quantitative analyst develops and implements mathematical and statistical models for financial applications. While this course does not focus on financial models it may help build a foundation in handling and exploring data, a crucial element of quantitative analysis. Much of a quantitative analyst's work involves using computational tools to understand, clean, and prepare data for analysis, and this course includes hands-on training in using Python and Pandas. This course may be useful for an aspiring quantitative analyst.

See salaries and explore the career path for Quantitative Analyst

Marketing Analyst

A marketing analyst studies market trends and consumer behavior to guide marketing campaigns. This course may be helpful for a marketing analyst, as it covers data gathering, cleaning, and exploratory data analysis. A marketing analyst will often work with data sets and need to use tools to extract relevant information. The practical approach of this course and its focus on Python will be very useful. This course may be useful to a marketing analyst.

See salaries and explore the career path for Marketing Analyst

Research Scientist

A research scientist designs and conducts studies to advance scientific knowledge. This course may help one prepare for research by providing tools to handle large datasets. A research scientist needs the ability to gather, clean, and analyze data, and this course provides training in practical techniques using Python. Through this course, one will learn to apply Python and Pandas to data-related problems, which would be helpful in the research field. This course may be useful for an aspiring research scientist.

See salaries and explore the career path for Research Scientist

Market Research Analyst

A market research analyst studies market conditions to examine the potential sales of a product or service. This course may fit into a market research analyst role because it helps build a foundation in data handling. A market research analyst may gather different data sources, clean this data, and look for trends and insights. This course provides this skill set using Python and Pandas, and with a hands-on project approach. This course may be useful to a market research analyst.

See salaries and explore the career path for Market Research Analyst

Bioinformatician

A bioinformatician develops methods and tools for analyzing biological data. This course may be useful for an aspiring bioinformatician because it helps build a foundation in data prep and exploratory data analysis. A bioinformatician must be fluent in handling and making sense of large datasets, and this course provides opportunities to practice in Python. This course may be useful for an entry-level bioinformatician.

See salaries and explore the career path for Bioinformatician

Financial Analyst

A financial analyst analyses financial data to help guide investment decisions. This course may be useful to a financial analyst because many aspects of this role involve data transformation and analysis. Although the course does not focus specifically on finance, it provides the tools to acquire, clean, and explore data using Python, especially with the Pandas library. A financial analyst would benefit from the hands-on experience in the course. This course may be useful for an aspiring financial analyst.

See salaries and explore the career path for Financial Analyst

Operations Analyst

An operations analyst examines business processes and performance data to improve efficiency. This course may be useful for an operations analyst because it helps build a foundation in data cleaning and analysis. An operations analyst relies on accurate and timely data to determine areas for improvement, and this course offers a hands-on approach for extracting insight from data. This course may be useful for an aspiring operations analyst.

See salaries and explore the career path for Operations Analyst

Consultant

A consultant provides expert advice to organizations on improving performance. This course may be useful to an aspiring consultant because it may provide tools for data analysis, an aspect of many consulting engagements. A consultant may gather diverse datasets, clean up the data, and extract valuable insights from it. The hands-on projects in the course may provide valuable skills. This course may be useful to an aspiring consultant.

See salaries and explore the career path for Consultant

Database Administrator

A database administrator manages and maintains databases. This course may be useful to a database administrator, as it introduces how to connect to and extract data from databases (including SQL databases) using Python and Pandas. Although this course does not touch on the day to day of a database administrator, this skill can be helpful for reporting, analysis, and troubleshooting. This course may be helpful to a database administrator.

See salaries and explore the career path for Database Administrator

Project Manager

A project manager plans, organizes, and manages projects to ensure they are completed on time, within budget, and scope. While a project manager doesn't need to be a data expert, this course may help them grasp the data science steps they may need to manage. A project manager may find knowledge of the data science process valuable. This course may be useful to a project manager involved in data science projects.

See salaries and explore the career path for Project Manager

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Python Data Science: Data Prep & EDA with Python.

Python for Data Analysis

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Comprehensive guide to data analysis with Pandas. It covers data cleaning, transformation, and analysis techniques in detail. It valuable reference for anyone working with data in Python, and is commonly used as a textbook in data science courses.

Python for Data Analysis

Paperback

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Python for Data Analysis

Kindle Edition

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Daphne Draws Data

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Focuses on the art of data visualization and communication. It teaches you how to create compelling visuals that effectively convey insights from your data. While not directly focused on Python, it is an excellent resource for improving your ability to present your EDA findings.

Daphne Draws Data: A Storytelling with Data...

Hardcover

Python Data Science

Data Prep & EDA with Python

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What's inside

Learning objectives

Syllabus

Traffic lights

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Reviews summary

Practical hands-on data prep & eda

Activities

Career center

Reading list

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