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Data Science in Python: Data Prep & EDA
Data Science in Python: Data Prep & EDA
Rating 4.7
237 reviews
237 reviews
237 reviews
Course 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.
If you’re an aspiring data scientist looking for an introduction to the world of machine learning with Python, this is the course for you.
Course 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.
If you’re an aspiring data scientist looking for an introduction to the world of machine learning with Python, this is the course for you.
Course Content
2 video hours
Skills you'll learn in this course
Clean messy datasets with pandas chaining & robust data types
Engineer meaningful features through scaling, encoding & binning
Uncover patterns via descriptive stats & visual EDA
Build reproducible data pipelines ready for modeling
Meet your instructors
Alice Zhao
Lead Data Science Instructor
Alice Zhao is a seasoned data scientist and author of the book, SQL Pocket Guide, 4th Edition (O'Reilly). She has taught numerous courses in Python, SQL, and R as a data science instructor at Maven Analytics and Metis, and as a co-founder of Best Fit Analytics.
Student reviews
It was a very insightful course on data preparation and exploratory data analysis. The concepts were clearly explained, and the step-by-step approach made it easy to follow and understand.
Bharath Kumar M
I recently completed the Data Science in Python: Data Prep & EDA course offered by Maven Analytics, and it was an outstanding learning experience. The course provides a solid foundation in data preparation and exploratory data analysis using Python, focusing on practical skills with tools like pandas, NumPy, and seaborn. The lessons were clearly explained, with engaging visuals and hands-on projects that helped reinforce key concepts. The real-world datasets used throughout the course made the material feel relevant and immediately applicable to real data science tasks. I especially appreciated how the course emphasized best practices for cleaning, transforming, and visualizing data — essential steps in any data science workflow. Overall, I highly recommend this course to anyone looking to improve their Python data skills and gain confidence in handling data exploration and preparation tasks. Maven Analytics has done a fantastic job creating a course that’s both accessible and impactful.
Syed Rayyan Hussain
The Data Science in Python: Data Prep & EDA course is a great first step for those who already have a solid knowledge of Python and want to apply it to data manipulation projects. This course also dives into some statistics concepts that can be a little hard to understand at the beginning, but Alice manages to explain them amazingly well.
Mateus Monteiro da Costa
Included learning paths
Course credential
You’ll earn the course certification by completing this course and passing the assessment requirements
Data Science in Python: Data Prep & EDA
Data Science in Python: Data Prep & EDA
CPE Accreditation
CPE Credits:
0
Field of Study:
Information Technology
Delivery Method:
QAS Self Study
Maven Analytics LLC is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have the final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.nasbaregistry.org.
For more information regarding administrative policies such as complaints or refunds, please contact us at admin@mavenanalytics.io or (857) 256-1765.
*Last Updated: May 25, 2023
Course 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.
If you’re an aspiring data scientist looking for an introduction to the world of machine learning with Python, this is the course for you.
Course 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.
If you’re an aspiring data scientist looking for an introduction to the world of machine learning with Python, this is the course for you.
Curriculum
1
Orientation & Benchmark Assessment
1
Orientation & Benchmark Assessment
1
Orientation & Benchmark Assessment
2
Getting Started
2
Getting Started
2
Getting Started
3
Intro to Data Science
6 MIN
6 MIN
6 MIN
3
Intro to Data Science
6 MIN
6 MIN
6 MIN
3
Intro to Data Science
6 MIN
6 MIN
6 MIN
4
Scoping a Project
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
4
Scoping a Project
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
4
Scoping a Project
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
5
Installing Jupyter Notebook
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
5
Installing Jupyter Notebook
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
5
Installing Jupyter Notebook
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6
Gathering Data
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6
Gathering Data
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6
Gathering Data
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
7
Cleaning Data
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
7
Cleaning Data
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
7
Cleaning Data
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
8
Exploratory Data Analysis
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
8
Exploratory Data Analysis
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
8
Exploratory Data Analysis
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
9
Mid-Course Project
9
Mid-Course Project
9
Mid-Course Project
10
Preparing for Modeling
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
10
Preparing for Modeling
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
10
Preparing for Modeling
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
6 MIN
11
Final Project
11
Final Project
11
Final Project
12
Final Assessment
6 MIN
12
Final Assessment
6 MIN
12
Final Assessment
6 MIN
13
Course Feedback & Next Steps
13
Course Feedback & Next Steps
13
Course Feedback & Next Steps
Meet your instructors
Alice Zhao
Lead Data Science Instructor
Alice Zhao is a seasoned data scientist and author of the book, SQL Pocket Guide, 4th Edition (O'Reilly). She has taught numerous courses in Python, SQL, and R as a data science instructor at Maven Analytics and Metis, and as a co-founder of Best Fit Analytics.
Student reviews
It was a very insightful course on data preparation and exploratory data analysis. The concepts were clearly explained, and the step-by-step approach made it easy to follow and understand.
Bharath Kumar M
I recently completed the Data Science in Python: Data Prep & EDA course offered by Maven Analytics, and it was an outstanding learning experience. The course provides a solid foundation in data preparation and exploratory data analysis using Python, focusing on practical skills with tools like pandas, NumPy, and seaborn. The lessons were clearly explained, with engaging visuals and hands-on projects that helped reinforce key concepts. The real-world datasets used throughout the course made the material feel relevant and immediately applicable to real data science tasks. I especially appreciated how the course emphasized best practices for cleaning, transforming, and visualizing data — essential steps in any data science workflow. Overall, I highly recommend this course to anyone looking to improve their Python data skills and gain confidence in handling data exploration and preparation tasks. Maven Analytics has done a fantastic job creating a course that’s both accessible and impactful.
Syed Rayyan Hussain
The Data Science in Python: Data Prep & EDA course is a great first step for those who already have a solid knowledge of Python and want to apply it to data manipulation projects. This course also dives into some statistics concepts that can be a little hard to understand at the beginning, but Alice manages to explain them amazingly well.
Mateus Monteiro da Costa
Included learning paths
Course credential
You’ll earn the course certification by completing this course and passing the assessment requirements
Data Science in Python: Data Prep & EDA
Data Science in Python: Data Prep & EDA
CPE Accreditation
CPE Credits:
0
Field of Study:
Information Technology
Delivery Method:
QAS Self Study
Maven Analytics LLC is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have the final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.nasbaregistry.org.
For more information regarding administrative policies such as complaints or refunds, please contact us at admin@mavenanalytics.io or (857) 256-1765.
*Last Updated: May 25, 2023
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