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Python for Data Science
Learning PathPython for Data Science

This path is for data professionals looking to build job-ready machine learning skills with Python, including regression, classification, unsupervised learning and more.

81 hours
4 courses
3 projects
Python
Start learning this path
Overview

This path is for data professionals looking to build job-ready data science & machine learning skills with Python.

We'll start by mastering the foundations of data prep & EDA, including scoping projects, gathering & cleaning data, performing exploratory data analysis, and preparing the data for modeling.

Next we'll dive into Regression Analysis, a popular supervised learning technique for making predictions with numerical data. We'll introduce simple & multiple linear regression, review key model assumptions, and walk through the steps for testing and validating your models. We'll also cover multiple techniques for regularized regression and time series analysis, including ridge & lasso regression, moving averages, decomposition, and more.

From there we'll explore Classification Modeling, another supervised learning technique for making predictions with categorical data. We'll the k-nearest neighbors and logistic regression models, review evaluation metrics like accuracy, precision & recall, then explore methods for working with imbalanced data. We'll then dive into decision trees and ensemble models, including random forests & gradient boosting.

Last but not least we'll cover Unsupervised Learning, a popular approach for discovering hidden patterns & relationships in data. We'll use clustering algorithms for segmentation & anomaly detection, and then leverage dimensionality reduction algorithms for visualizing complex data, identifying clusters, and building recommendation engines.

This path is designed to help you learn job-ready skills, solve real business problems, and build a project portfolio to showcase your skills to peers and employers.

WHO SHOULD TAKE THIS PATH?

  • Data analysts or BI professionals looking to transition into data science
  • Data scientists who want to learn how to build and interpret machine learning models in Python
  • Students looking for a hands-on, project-based learning experience

WHAT ARE THE PATH REQUIREMENTS?

  • Jupyter Notebooks (free download, we'll walk through the install)
  • Familiarity with base Python and Pandas is recommended, but not required
Curriculum
Course
Data Science in Python: Data Prep & EDAMaster the foundations of Python for data science, including scoping, data gathering & cleaning, EDA, and feature engineering
Guided Project
Bank Customer Data PrepClean and explore bank customer data to prepare it for machine learning models
Course
Data Science in Python: RegressionMaster the foundations for regression analysis in Python, including linear & regularized regression, forecasting, and validation & testing
Guided Project
Predicting Fuel EconomyBuild a linear regression model to predict a vehicle's fuel efficiency
Course
Data Science in Python: ClassificationMaster the foundations of classification modeling in Python, including KNN, logistic regression, decision trees, random forests, and GBMs
Guided Project
Bank Customer ClassificationBuild a classification model to predict which bank customers are most likely to churn
Course
Data Science in Python: Unsupervised LearningMaster the foundations of unsupervised learning in Python, including clustering, anomaly detection, dimensionality reduction, and recommenders
Instructors
Chris Bruehl
Chris BruehlChris is a Python expert, certified Statistical Business Analyst, and seasoned Data Scientist, having held senior-level roles at large insurance firms and financial service companies. He earned a Masters in Analytics at NC State's Institute for Advanced Analytics, where he founded the IAA Python Programming club.

Alice Zhao
Alice ZhaoAlice 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.
Overview

This path is for data professionals looking to build job-ready data science & machine learning skills with Python.

We'll start by mastering the foundations of data prep & EDA, including scoping projects, gathering & cleaning data, performing exploratory data analysis, and preparing the data for modeling.

Next we'll dive into Regression Analysis, a popular supervised learning technique for making predictions with numerical data. We'll introduce simple & multiple linear regression, review key model assumptions, and walk through the steps for testing and validating your models. We'll also cover multiple techniques for regularized regression and time series analysis, including ridge & lasso regression, moving averages, decomposition, and more.

From there we'll explore Classification Modeling, another supervised learning technique for making predictions with categorical data. We'll the k-nearest neighbors and logistic regression models, review evaluation metrics like accuracy, precision & recall, then explore methods for working with imbalanced data. We'll then dive into decision trees and ensemble models, including random forests & gradient boosting.

Last but not least we'll cover Unsupervised Learning, a popular approach for discovering hidden patterns & relationships in data. We'll use clustering algorithms for segmentation & anomaly detection, and then leverage dimensionality reduction algorithms for visualizing complex data, identifying clusters, and building recommendation engines.

This path is designed to help you learn job-ready skills, solve real business problems, and build a project portfolio to showcase your skills to peers and employers.

WHO SHOULD TAKE THIS PATH?

  • Data analysts or BI professionals looking to transition into data science
  • Data scientists who want to learn how to build and interpret machine learning models in Python
  • Students looking for a hands-on, project-based learning experience

WHAT ARE THE PATH REQUIREMENTS?

  • Jupyter Notebooks (free download, we'll walk through the install)
  • Familiarity with base Python and Pandas is recommended, but not required
Instructors
Chris Bruehl
Chris BruehlChris is a Python expert, certified Statistical Business Analyst, and seasoned Data Scientist, having held senior-level roles at large insurance firms and financial service companies. He earned a Masters in Analytics at NC State's Institute for Advanced Analytics, where he founded the IAA Python Programming club.

Alice Zhao
Alice ZhaoAlice 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.
Curriculum
Course
Data Science in Python: Data Prep & EDAMaster the foundations of Python for data science, including scoping, data gathering & cleaning, EDA, and feature engineering
Guided Project
Bank Customer Data PrepClean and explore bank customer data to prepare it for machine learning models
Course
Data Science in Python: RegressionMaster the foundations for regression analysis in Python, including linear & regularized regression, forecasting, and validation & testing
Guided Project
Predicting Fuel EconomyBuild a linear regression model to predict a vehicle's fuel efficiency
Course
Data Science in Python: ClassificationMaster the foundations of classification modeling in Python, including KNN, logistic regression, decision trees, random forests, and GBMs
Guided Project
Bank Customer ClassificationBuild a classification model to predict which bank customers are most likely to churn
Course
Data Science in Python: Unsupervised LearningMaster the foundations of unsupervised learning in Python, including clustering, anomaly detection, dimensionality reduction, and recommenders
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"Thinking like an analyst" course is a very complete compact course if you have to go through the process of getting to know your customers business till building an insightful dashboard for them. I did a lot of courses the past year but this one is totally different. Key is satisfying your customer by giving them strong insights. I really enjoyed the course and put it on pause many times to check or use it directly in my own ongoing project. I can recommend this course to anybody working as a consultant in the data area.
Marjolein Opsteegh
Marjolein Opsteegh
Marjolein Opsteegh
Maven's Pivot Table & Charts class was a great intro to dashboard design but Advanced Excel Dashboard Design will take your approach to a whole new level. You'll learn the importance of formula-based dashboard design and how to manipulate colors, charts, and KPI metric cards to tell your story. By far one of my favorite Maven classes!
Nate Dunn
Nate Dunn
Thinking like an Analyst course has helped me to clearly understand what it takes to have a successful career in data analytics. I have learnt that my focus should be on the skills I acquire while learning and not the tools. I have also learnt a data analysis workflow for high quality analysis that drives actionable outcomes. I had fun while learning during this course. It is self pacing and elaborate.
Eniola Dada
Eniola Dada
Eniola Dada
For all aspiring or converted analyst out there, stop looking around, this is the way to get the role you are looking for. I am truly impressed with the quality of the information and the completeness of the package Maven Analytics put together. In this well rounded package you have everything you need, from finding your own path, writing or re-writing your resume, marketing on LinkedIn, to building the so needed project portfolio, and even interview skills and approaches.
Radu Tecuceanu
Radu Tecuceanu
Radu Tecuceanu
Love the Maven courses! The instructors always have great slide content to help solidify the concepts and drive home the key aspects. The examples and very relatable making it so much easier to understand how to apply my own projects.
Joseph Collins
Joseph Collins
Joseph Collins
Maven Analytics changed my life, I found a better job position taking the Excel and Power BI paths, nowadays I can apply my skills in my company innovating everyday. Thankful forever.
Rodrigo Chavez
Rodrigo Chavez
Rodrigo Chavez
An excellent course with top-notch data sets to work around with. As usual, Chris Dutton did a fabulous job of covering each element with a simplistic but analytical approach. Although I've been using pivot tables for 5+ years, the new learnings will open a whole array of opportunities for extracting actionable insights. Thank you!
Faizan Qadri
Faizan Qadri
Faizan Qadri
Before deciding to join this learning platform, I struggled to understand and figure out what direction I needed to take for my skill development to be a Data Analyst. This first course helped ease some of my fears and confusion. I am more passionate about my decision to become a data analyst. THANK YOU Chris, John, Aaron, and Enrique , and I look forward to learning more through Maven.
Angie
Angie
Angie
The Maven Analytics Courses are packed full of practical lessons and useful pro tips that can be immediately applied to data in the workplace. One of the best trainings I've ever taken!
Randy McCauley
Randy McCauley
Randy McCauley
Once again Maven blows it out of the water. In this fantastic, well structures course about the way to "Think Like an Analyst". It has so many good points and actions to take when putting together a project. I found it very useful and was able to put it into practice at work straight away. Thank Guys!
Catherine Taylor
Catherine Taylor
Catherine Taylor
Maven really makes it possible to get into data analytics without taking (and paying for) a traditional education program. The courses I took left me with a better understanding and deeper knowledge of what I like most: analyzing data.
Erik van’t Ende
Erik van’t Ende
Erik van’t Ende
The course is fantastic and highly recommended. It gave me the confidence to perform the logical analysis in any given data source and of course build kickass visuals business dashboards for my clients :). Loved the course from the first second to the last.
Tonmoy Hashmi
Tonmoy Hashmi
Tonmoy Hashmi
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