Self-paced course
Self-paced course
Data Science in Python: Data Prep & EDA
Data Science in Python: Data Prep & EDA
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
15 course hours
16 assignments & solutions
7 quizzes
7 skill assessments
Who should take this course
Data analysts or BI experts looking to transition into a data science role
Python users who want to build the core skills required before applying for Machine Learning models
Anyone interested in learning one of the most popular open source programming languages in the world
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.
Featured review
"This course was clear, well-structured, and packed with practical techniques that are directly applicable to real-world data problems. Alice’s teaching style made even complex topics easy to follow, and I especially appreciated how she explained important concepts like missing value handling, feature engineering, merging, and groupby operations. Thanks to this course, I now feel more confident in preparing and analyzing data using Python and pandas. I highly recommend it to anyone starting out or looking to strengthen their foundations in data science!"
Manikandan S.
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
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
This course was clear, well-structured, and packed with practical techniques that are directly applicable to real-world data problems. Alice’s teaching style made even complex topics easy to follow, and I especially appreciated how she explained important concepts like missing value handling, feature engineering, merging, and groupby operations. Thanks to this course, I now feel more confident in preparing and analyzing data using Python and pandas. I highly recommend it to anyone starting out or looking to strengthen their foundations in data science!
Manikandan S.
In the course 'Data Science in Python: Data Prep, EDA, and Modeling,' I had an incredibly positive learning experience. It equipped me with essential skills to kickstart my journey in data science. From mastering data cleaning techniques to diving into advanced data modeling, each lesson was engaging and immediately applicable. I am deeply grateful for the valuable insights and guidance provided throughout the course
Theofanis Tsitroulis
This course was extremely helpful for me to get the grasp with the EDA process from the get-go to the advanced levels. The course is perfectly comprehensive(with lots and lots of practices) and guides you all the way from even installing Anaconda! to the end stage which is preparing a messy dataset for machine learning modelling and that was what I didn't find somewhere else. Also, I should mention that the instructor of the course excels at delivering the materials(even the most advanced concepts) in a simple, easy to understand manner. This is especially important because Data Prep & EDA is a foundational topic and most of its audience are people(like myself) who are getting familiar with data science and need to learn both coding and analysis concepts at the same time.
Omid Golchin
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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