Python is a general-purpose programming language that is becoming ever more popular for data science. Companies worldwide are using Python to harvest insights from their data and gain a competitive edge. Unlike other Python tutorials, this course focuses on Python specifically for data science. In our Introduction to Python course, you’ll learn about powerful ways to store and manipulate data, and helpful data science tools to begin conducting your own analyses. Start DataCamp’s online Python curriculum now.

Master the basics of data analysis in Python. Expand your skillset by learning scientific computing with NumPy.

Discover how to use Python for data science, learning about the ways to store and manipulate data in the environment to begin conducting your own analyses.

Introduction to Python

The role of a data scientist is to turn raw data into actionable insights. Much of the world's raw data—from electronic medical records to customer transaction histories—lives in organized collections of tables called relational databases. To be an effective data scientist, you must know how to wrangle and extract data from these databases using a language called SQL . This course teaches syntax in SQL shared by many types of databases, such as PostgreSQL, MySQL, SQL Server, and Oracle. This course teaches you everything you need to know to begin working with databases today!

Master the basics of querying tables in relational databases such as MySQL, SQL Server, and PostgreSQL.

Learn the basics of syntax in SQL, how to extract and wrangle data, and use querying tables in relational databases like MySQL, SQL Server, and PostgreSQL.

Introduction to SQL

In this introduction to R course, you'll master the basics of this widely used open source language&mdash;including vectors, factors, lists, and data frames. With the coding skills you'll gain in this course, you'll be ready to undertake your own data analysis in R. There are millions of R users worldwide, cementing it as a leading programming language in statistics and data science. More and more organizations are using R, so begin your coding journey in one of DataCamp's most popular courses today!

Master the basics of data analysis by manipulating common data structures such as vectors, matrices, and data frames.

DataCamp's Into to R training course teaches you how to use R programming for data science at your own pace with video tutorials & interactive challenges.

Introduction to R Online

Introduction to R

Learning Python is crucial for any aspiring data science practitioner. Learn to visualize real data with Matplotlib's functions and get acquainted with data structures such as the dictionary and the pandas DataFrame. After covering key concepts such as boolean logic, control flow, and loops in Python, you'll be ready to blend together everything you've learned to solve a case study using hacker statistics.

Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.

In this intermediate Python course, learn to visualize real data with Matplotlib's functions and get to know new data structures such as the pandas DataFrame.

Intermediate Python

Now that you've learned the basics of SQL in our <a href="https://learn.datacamp.com/courses/introduction-to-sql">Introduction to SQL</a> course,  it's time to supercharge your queries using joins and relational set theory. In this course, you'll learn all about the power of joining tables while exploring interesting features of countries and their cities throughout the world. You will master inner and outer joins, as well as self joins, semi joins, anti joins and cross joins—fundamental tools in any PostgreSQL wizard's toolbox. Never fear set theory again after learning all about unions, intersections, and except clauses through easy-to-understand diagrams and examples. Lastly, you'll be introduced to the challenging topic of subqueries. You will be able to visually grasp these ideas by using Venn diagrams and other linking illustrations.

Join two or three tables together into one, combine tables using set theory, and work with subqueries in PostgreSQL.

Discover all the fundamental in PostgreSQL, like inner, outer self, semi, anti, and cross joins, as well as unions, intersections, and except clauses.

Joining Data in SQL

pandas is the world's most popular Python library, used for everything from data manipulation to data analysis. In this course, you'll learn how to manipulate DataFrames, as you extract, filter, and transform real-world datasets for analysis. Using pandas you’ll explore all the core data science concepts. Using real-world data, including Walmart sales figures and global temperature time series, you’ll learn how to import, clean, calculate statistics, and create visualizations—using pandas to add to the power of Python!

Use the world’s most popular Python data science package to manipulate data and calculate summary statistics.

Data Manipulation with pandas

Machine learning is the field that teaches machines and computers to learn from existing data to make predictions on new data: Will a tumor be benign or malignant? Which of your customers will take their business elsewhere? Is a particular email spam? In this course, you'll learn how to use Python to perform supervised learning, an essential component of machine learning. You'll learn how to build predictive models, tune their parameters, and determine how well they will perform with unseen data—all while using real world datasets. You'll be using scikit-learn, one of the most popular and user-friendly machine learning libraries for Python.

Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.

Supervised Learning with scikit-learn

Intermediate R is the next stop on your journey in mastering the R programming language. In this R training, you will learn about conditional statements, loops, and functions to power your own R scripts. Next, make your R code more efficient and readable using the apply functions. Finally, the utilities chapter gets you up to speed with regular expressions in R, data structure manipulations, and times and dates. This course will allow you to take the next step in advancing your overall knowledge and capabilities while programming in R.

Continue your journey to becoming an R ninja by learning about conditional statements, loops, and vector functions.

Discover conditional statements, loops, and functions to power your own R scripts, and learn to make your R code more efficient using the apply functions.

Intermediate R

It's time to push forward and develop your Python chops even further. There are tons of fantastic functions in Python and its library ecosystem. However, as a data scientist, you'll constantly need to write your own functions to solve problems that are dictated by your data. You will learn the art of function writing in this first Python Data Science Toolbox course. You'll come out of this course being able to write your very own custom functions, complete with multiple parameters and multiple return values, along with default arguments and variable-length arguments. You'll gain insight into scoping in Python and be able to write lambda functions and handle errors in your function writing practice. And you'll wrap up each chapter by using your new skills to write functions that analyze Twitter DataFrames.

Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.

Python Data Science Toolbox (Part 1)

Visualizing data in plots and figures exposes the underlying patterns in the data and provides insights. Good visualizations also help you communicate your data to others, and are useful to data analysts and other consumers of the data. In this course, you will learn how to use Matplotlib, a powerful Python data visualization library. Matplotlib provides the building blocks to create rich visualizations of many different kinds of datasets. You will learn how to create visualizations for different kinds of data and how to customize, automate, and share these visualizations.

Learn how to create, customize, and share data visualizations using Matplotlib.

Introduction to Data Visualization with Matplotlib

In this second Python Data Science Toolbox course, you'll continue to build your Python data science skills. First, you'll learn about iterators, objects you have already encountered in the context of for loops. You'll then learn about list comprehensions, which are extremely handy tools for all data scientists working in Python. You'll end the course by working through a case study in which you'll apply all the techniques you learned in both parts of this course.

Continue to build your modern Data Science skills by learning about iterators and list comprehensions.

Python Data Science Toolbox (Part 2)

As a data scientist, you will need to clean data, wrangle and munge it, visualize it, build predictive models, and interpret these models. Before you can do so, however, you will need to know how to get data into Python. In this course, you'll learn the many ways to import data into Python: from flat files such as .txt and .csv; from files native to other software such as Excel spreadsheets, Stata, SAS, and MATLAB files; and from relational databases such as SQLite and PostgreSQL.

Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.

You'll learn the many ways to read data into Python: from flat files such as CSVs to Excel spreadsheets and relational databases in SQLite & PostgreSQL.

Introduction to Importing Data in Python

Seaborn is a powerful Python library that makes it easy to create informative and attractive visualizations. This course provides an introduction to Seaborn and teaches you how to visualize your data using plots such as scatter plots, box plots, and bar plots. You’ll do this while exploring survey responses about student hobbies and the factors that are associated with academic success. You’ll also learn about some of Seaborn’s advantages as a statistical visualization tool, such as how it automatically calculates confidence intervals. By the end of the course, you will be able to use Seaborn in a variety of situations to explore your data and effectively communicate the results of your data analyses to others.

Learn how to create informative and attractive visualizations in Python using the Seaborn library.

Learn how to visualize in scatter plots, box plots, and bar plots, to create informative and attractive visualizations using the Python libary, Seaborn.

Introduction to Data Visualization with Seaborn

You’ve already used SQL to query data from databases. But did you know that there's a lot more you can do with databases? You can model different phenomena in your data, as well as the relationships between them. This gives your data structure and consistency, which results in better data quality. In this course, you'll experience this firsthand by working with a real-life dataset that was used to investigate questionable university affiliations. Column by column, table by table, you'll get to unlock and admire the full potential of databases. You'll learn how to create tables and specify their relationships, as well as how to enforce data integrity. You'll also discover other unique features of database systems, such as constraints.

Learn how to create one of the most efficient ways of storing data - relational databases! 

Learn how to create tables and specify their relationships in SQL, as well as how to enforce data integrity and other unique features of database systems.

Introduction to Relational Databases in SQL

After all of the hard work of acquiring data and getting them into a form you can work with, you ultimately want to make clear, succinct conclusions from them. This crucial last step of a data analysis pipeline hinges on the principles of statistical inference. In this course, you will start building the foundation you need to think statistically, speak the language of your data, and understand what your data is telling you. The foundations of statistical thinking took decades to build, but can be grasped much faster today with the help of computers. With the power of Python-based tools, you will rapidly get up-to-speed and begin thinking statistically by the end of this course.

Build the foundation you need to think statistically and to speak the language of your data.

Statistical Thinking in Python (Part 1)

As a data scientist, you will need to clean data, wrangle and munge it, visualize it, build predictive models and interpret these models. Before you can do so, however, you will need to know how to get data into Python. In the prequel to this course, you learned many ways to import data into Python: from flat files such as .txt and .csv; from files native to other software such as Excel spreadsheets, Stata, SAS, and MATLAB files; and from relational databases such as SQLite and PostgreSQL. In this course, you'll extend this knowledge base by learning to import data from the web and by pulling data from Application Programming Interfaces— APIs—such as the Twitter streaming API, which allows us to stream real-time tweets.

Improve your Python data importing skills and learn to work with web and API data.

Learn how to import data into Python from sources like the web and by pulling data from APIs, such as the Twitter streaming API to stream real-time tweets.

Intermediate Importing Data in Python

How do we get from data to answers?  Exploratory data analysis is a process for exploring datasets, answering questions, and visualizing results.  This course presents the tools you need to clean and validate data, to visualize distributions and relationships between variables, and to use regression models to predict and explain.  You'll explore data related to demographics and health, including the National Survey of Family Growth and the General Social Survey.  But the methods you learn apply to all areas of science, engineering, and business. You'll use Pandas, a powerful library for working with data, and other core Python libraries including NumPy and SciPy, StatsModels for regression, and Matplotlib for visualization.  With these tools and skills, you will be prepared to work with real data, make discoveries, and present compelling results.

Learn how to explore, visualize, and extract insights from data. 

Learn with real-world examples how to clean, explore, visualize, and extract insights from data and use regression models to make predictions in Python.

Exploratory Data Analysis in Python

Do you want to make beautiful, informative visualizations with ease? If so, then you must learn seaborn! Seaborn is a visualization library that is an essential part of the python data science toolkit. In this course, you will learn how to use seaborn's sophisticated visualization tools to analyze multiple real world datasets including the American Housing Survey, college tuition data, and guests from the popular television series, The Daily Show. Following this course, you will be able to use seaborn functions to visualize your data in several different formats and customize seaborn plots for your unique needs.

Use Seaborn's sophisticated visualization tools to make beautiful, informative visualizations with ease.

Learn how you can use Seaborn functions to visualize your data in several different formats and customize Seaborn plots for your unique needs in Python.

Intermediate Data Visualization with Seaborn

Say you have a collection of customers with a variety of characteristics such as age, location, and financial history, and you wish to discover patterns and sort them into clusters. Or perhaps you have a set of texts, such as Wikipedia pages, and you wish to segment them into categories based on their content. This is the world of unsupervised learning, called as such because you are not guiding, or supervising, the pattern discovery by some prediction task, but instead uncovering hidden structure from unlabeled data. Unsupervised learning encompasses a variety of techniques in machine learning, from clustering to dimension reduction to matrix factorization. In this course, you'll learn the fundamentals of unsupervised learning and implement the essential algorithms using scikit-learn and SciPy. You will learn how to cluster, transform, visualize, and extract insights from unlabeled datasets, and end the course by building a recommender system to recommend popular musical artists.

Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.

Unsupervised Learning in Python

Version control is one of the power tools of programming. It allows you to keep track of what you did when, undo any changes you decide you don't want, and collaborate at scale with other people. This course will introduce you to Git, a modern version control tool that is very popular with data scientists and software developers, and show you how to use it to get more done in less time and with less pain.

This course is an introduction to version control with Git for data scientists. 

Discover Git, a popular and modern version control tool for data scientists, and how you can use it work more efficiently, quickly, and with fewer errors.

Introduction to Git

The financial industry is increasingly adopting Python for general-purpose programming and quantitative analysis, ranging from understanding trading dynamics to risk management systems. This course focuses specifically on introducing Python for financial analysis. Using practical examples, you will learn the fundamentals of Python data structures such as lists and arrays and learn powerful ways to store and manipulate financial data to identify trends.

This course introduces Python for financial analysis.

Learn how to use lists, arrays, and visualization in Python for financial analysis. You'll also get chance to apply techniques to a real-world case study.

Introduction to Python for Finance

Decision trees are supervised learning models used for problems involving classification and regression. Tree models present a high flexibility that comes at a price: on one hand, trees are able to capture complex non-linear relationships; on the other hand, they are prone to memorizing the noise present in a dataset. By aggregating the predictions of trees that are trained differently, ensemble methods take advantage of the flexibility of trees while reducing their tendency to memorize noise. Ensemble methods are used across a variety of fields and have a proven track record of winning many machine learning competitions.
In this course, you'll learn how to use Python to train decision trees and tree-based models with the user-friendly scikit-learn machine learning library. You'll understand the advantages and shortcomings of trees and demonstrate how ensembling can alleviate these shortcomings, all while practicing on real-world datasets. Finally, you'll also understand how to tune the most influential hyperparameters in order to get the most out of your models.

In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn. 

Machine Learning with Tree-Based Models in Python

You'll probably never have a time machine, but how about a machine for analyzing time? As soon as time enters any analysis, things can get weird. It's easy to get tripped up on day and month boundaries, time zones, daylight saving time, and all sorts of other things that can confuse the unprepared. If you're going to do any kind of analysis involving time, you’ll want to use Python to sort it out. Working with data sets on hurricanes and bike trips, we’ll cover counting events, figuring out how much time has elapsed between events and plotting data over time. You'll work in both standard Python and in Pandas, and we'll touch on the dateutil library, the only timezone library endorsed by the official Python documentation. After this course, you'll confidently handle date and time data in any format like a champion.

Learn how to work with dates and times in Python.

Learn how to work with dates and times in Python using Pandas and dateutil library. You'll also have chance to practice with real-world data as you learn.

Working With Dates and Times in Python

Working with Dates and Times in Python

In this course, you'll learn natural language processing (NLP) basics, such as how to identify and separate words, how to extract topics in a text, and how to build your own fake news classifier. You'll also learn how to use basic libraries such as NLTK, alongside libraries which utilize deep learning to solve common NLP problems. This course will give you the foundation to process and parse text as you move forward in your Python learning.

Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.

Learn fundamental natural language processing (NLP) techniques using Python and how to apply them to extract insights from real-world text data in Python.

Introduction to Natural Language Processing in Python

Now that you have learned the foundations of pandas, this course will give you the chance to apply that knowledge by answering interesting questions about a real dataset! You will explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior. During the course, you will gain more practice cleaning messy data, creating visualizations, combining and reshaping datasets, and manipulating time series data. Analyzing Police Activity with pandas will give you valuable experience analyzing a dataset from start to finish, preparing you for your data science career!

Explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior using pandas.

Analyzing Police Activity with pandas

A vital component of data science involves acquiring raw data and getting it into a form ready for analysis. It is commonly said that data scientists spend 80% of their time cleaning and manipulating data, and only 20% of their time actually analyzing it. This course will equip you with all the skills you need to clean your data in Python, from learning how to diagnose problems in your data, to dealing with missing values and outliers. At the end of the course, you'll apply all of the techniques you've learned to a case study to clean a real-world Gapminder dataset.

This course will equip you with all the skills you need to clean your data in Python.

Data scientists spend 80% of their time cleaning and manipulating data. This course will equip you with all the skills you need to clean your data in Python.

Cleaning Data in Python

After completing Statistical Thinking in Python (Part 1), you have the probabilistic mindset and foundational hacker stats skills to dive into data sets and extract useful information from them. In this course, you will do just that, expanding and honing your hacker stats toolbox to perform the two key tasks in statistical inference, parameter estimation and hypothesis testing. You will work with real data sets as you learn, culminating with analysis of measurements of the beaks of the Darwin's famous finches. You will emerge from this course with new knowledge and lots of practice under your belt, ready to attack your own inference problems out in the world.

Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.

Statistical Thinking in Python (Part 2)

You have probably come across Google News, which automatically groups similar news articles under a topic. Have you ever wondered what process runs in the background to arrive at these groups? In this course, you will be introduced to unsupervised learning through clustering using the SciPy library in Python. This course covers pre-processing of data and application of hierarchical and k-means clustering. Through the course, you will explore player statistics from a popular football video game, FIFA 18. After completing the course, you will be able to quickly apply various clustering algorithms on data, visualize the clusters formed and analyze results.

In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library. 

Cluster Analysis in Python

Data science isn't just for predicting ad-clicks-it's also useful for social impact! This course is a case study from a machine learning competition on DrivenData. You'll explore a problem related to school district budgeting. By building a model to automatically classify items in a school's budget, it makes it easier and faster for schools to compare their spending with other schools. In this course, you'll begin by building a baseline model that is a simple, first-pass approach. In particular, you'll do some natural language processing to prepare the budgets for modeling. Next, you'll have the opportunity to try your own techniques and see how they compare to participants from the competition. Finally, you'll see how the winner was able to combine a number of expert techniques to build the most accurate model.

Learn how to build a model to automatically classify items in a school budget.

This course is a case study from a machine learning competition on DrivenData. Learn how to build a model to automatically classify items in a school budget.

Case Study: School Budgeting with Machine Learning in Python

In this course you'll learn all about using linear classifiers, specifically logistic regression and support vector machines, with scikit-learn. Once you've learned how to apply these methods, you'll dive into the ideas behind them and find out what really makes them tick. At the end of this course you'll know how to train, test, and tune these linear classifiers in Python. You'll also have a conceptual foundation for understanding many other machine learning algorithms.

In this course you will learn the details of linear classifiers like logistic regression and SVM.

Linear Classifiers in Python

Do you know the basics of supervised learning and want to use state-of-the-art models on real-world datasets? Gradient boosting is currently one of the most popular techniques for efficient modeling of tabular datasets of all sizes. XGboost is a very fast, scalable implementation of gradient boosting, with models using XGBoost regularly winning online data science competitions and being used at scale across different industries. In this course, you'll learn how to use this powerful library alongside pandas and scikit-learn to build and tune supervised learning models. You'll work with real-world datasets to solve classification and regression problems.

Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.

Explore the fundamentals of gradient boosting, with a focus on Regression with XGBoost, using XGBoost in pipelines and how to fine-tune your XGBoost model.

Extreme Gradient Boosting with XGBoost

As a data scientist, you'll often find that the data you need is not in a single file. It may be spread across a number of text files, spreadsheets, or databases. You’ll want to be able to import the data you’re interested in as a collection of DataFrames and combine them to answer your central questions. This course is all about the act of combining—or merging—DataFrames, an essential part of any data scientist's toolbox. You'll hone your pandas skills by learning how to organize, reshape, and aggregate multiple datasets to answer your specific questions.

This course is all about the act of combining, or merging, DataFrames, an essential part your Data Scientist's toolbox. 

Hone your pandas skills by learning how to organize, reshape, and aggregate multiple data sets to answer your specific questions.

Merging DataFrames with pandas

Are you a financial or business analyst, or simply looking for an easier way to manage your stock portfolio? If so learning Python can automate financial tasks such as calculating risk, mapping market health, and visualizing a stock's price trends, saving you time and money.

In this course, you’ll learn how to use Python data structures, execution control statements, and DataFrames to manipulate financial data. You will then work with pandas, using data from the Federal Reserve Bank, to explore national economic trends—an essential part of understanding investment strategies. You will also calculate risk based on stock price data, and display this data in easy to read plots. By the end of this course, you’ll be the new Python of Wall Street.

Build on top of your Python skills for Finance, by learning how to use datetime, if-statements, DataFrames, and more.

Intermediate Python for Finance

If you want to apply your new 'Python for Data Science' skills to real-world financial data, then this course will give you some very valuable tools.
First, you will learn how to get data out of Excel into pandas and back. Then, you will learn how to pull stock prices from various online APIs like
Google or Yahoo! Finance, macro data from the Federal Reserve, and exchange rates from OANDA. Finally, you will learn how to calculate returns for various time horizons,
analyze stock performance by sector for IPOs, and calculate and summarize correlations.


In this course, you'll learn how to import and manage financial data in Python using various tools and sources.

Learn how to pull stock prices from online APIs to calculate returns for time horizons, analyze stock performance by sector, and summarize correlations.

Importing and Managing Financial Data in Python

Gain the career-building Python skills you need to succeed as a data scientist. No prior coding experience required.

In this track, you'll learn how this versatile language allows you to import, clean, manipulate, and visualize data—all integral skills for any aspiring data professional or researcher. Through interactive exercises, you'll get hands-on with some of the most popular Python libraries, including pandas, NumPy, Matplotlib, and many more. You'll then work with real-world datasets to learn the statistical and machine learning techniques you need to train decision trees and use natural language processing (NLP). Start this track, grow your Python skills, and begin your journey to becoming a confident data scientist.

From data manipulation to machine learning, you'll gain the career-building Python skills you need to succeed in the exciting field of data science!

with Python

Data Scientist 

Time series data is all around us; some examples are the weather, human behavioral patterns as consumers and members of society, and financial data. In this course, you'll learn how to calculate technical indicators from historical stock data, and how to create features and targets out of the historical stock data. You'll understand how to prepare our features for linear models, xgboost models, and neural network models. We will then use linear models, decision trees, random forests, and neural networks to predict the future price of stocks in the US markets. You will also learn how to evaluate the performance of the various models we train in order to optimize them, so our predictions have enough accuracy to make a stock trading strategy profitable.

Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.

Learn to model & predict stock data values using linear models, decision trees, random forests, and neural networks.

Machine Learning for Finance in Python

High-dimensional datasets can be overwhelming and leave you not knowing where to start. Typically, you’d visually explore a new dataset first, but when you have too many dimensions the classical approaches will seem insufficient. Fortunately, there are visualization techniques designed specifically for high dimensional data and you’ll be introduced to these in this course. After exploring the data, you’ll often find that many features hold little information because they don’t show any variance or because they are duplicates of other features. You’ll learn how to detect these features and drop them from the dataset so that you can focus on the informative ones. In a next step, you might want to build a model on these features, and it may turn out that some don’t have any effect on the thing you’re trying to predict. You’ll learn how to detect and drop these irrelevant features too, in order to reduce dimensionality and thus complexity. Finally, you’ll learn how feature extraction techniques can reduce dimensionality for you through the calculation of uncorrelated principal components.

Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.

Learn to reduce dimensionality in Python by detecting these features and dropping them from the dataset so that you can focus on the informative ones.

Dimensionality Reduction in Python

Images are everywhere! We live in a time where images contain lots of information, which is sometimes difficult to obtain. This is why image pre-processing has become a highly valuable skill, applicable in many use cases. In this course, you will learn to process, transform, and manipulate images at your will, even when they come in thousands. You will also learn to restore damaged images, perform noise reduction, smart-resize images, count the number of dots on a dice, apply facial detection, and much more, using scikit-image. After completing this course, you will be able to apply your knowledge to different domains such as machine learning and artificial intelligence, machine and robotic vision, space and medical image analysis, retailing, and many more. Take the step and dive into the wonderful world that is computer vision!

Learn to process, transform, and manipulate images at your will.

Learn how you can restore damaged images, perform noise reduction, smart-resize images, apply facial detection, and more, using scikit-image in Python.

Image Processing in Python

What do Amazon product recommendations and Netflix movie suggestions have in common? They both rely on Market Basket Analysis, which is a powerful tool for translating vast amounts of customer transaction and viewing data into simple rules for product promotion and recommendation. In this course, you’ll learn how to perform Market Basket Analysis using the Apriori algorithm, standard and custom metrics, association rules, aggregation and pruning, and visualization. You’ll then reinforce your new skills through interactive exercises, building recommendations for a small grocery store, a library, an e-book seller, a novelty gift retailer, and a movie streaming service. In the process, you’ll uncover hidden insights to improve recommendations for customers.

Explore association rules in market basket analysis with Python by bookstore data and creating movie recommendations.

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