Data science courses

Master the basics of data analysis with Python in just four hours. This online course will introduce the Python interface and explore popular packages.

Learn how to create and query relational databases using SQL in just two hours.

Master the basics of data analysis in R, including vectors, lists, and data frames, and practice R with real data sets.

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

Dive into data science using Python and learn how to effectively analyze and visualize your data. No coding experience or skills needed.

Accompanied at every step with hands-on practice queries, this course teaches you everything you need to know to analyze data using your own SQL code today!

An introduction to data science with no coding involved.

Master the Power BI basics and learn to use the data visualization software to build impactful reports.

Learn how to import and clean data, calculate statistics, and create visualizations with pandas.

An introduction to data visualization with no coding involved.

Level up your SQL knowledge and learn to join tables together, apply relational set theory, and work with subqueries.

Grow your machine learning skills with scikit-learn in Python. Use real-world datasets in this interactive course and learn how to make powerful predictions!

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

Build real-world Excel skills in just 4 hours. This course will show you time-saving shortcuts and essential functions.

Start your Tableau journey with our Introduction to Tableau course. Discover Tableau basics such as its features and dashboards.

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

Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.

Master the complex SQL queries necessary to answer a wide variety of data science questions and prepare robust data sets for analysis in PostgreSQL.

Discover how data engineers lay the groundwork that makes data science possible. No coding involved!

Get started on the path to exploring and visualizing your own data with the tidyverse, a powerful and popular collection of data science tools within R.

Learn to combine data from multiple tables by joining data together using pandas.

An introduction to machine learning with no coding involved.

Learn the fundamentals of statistics, including measures of center and spread, probability distributions, and hypothesis testing with no coding involved!

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

Learn how to explore what's available in a database: the tables, relationships between them, and data stored in them.

Learn to produce meaningful and beautiful data visualizations with ggplot2 by understanding the grammar of graphics.

Enhance your Power BI knowledge, by learning the fundamentals of Data Analysis Expressions (DAX) such as calculated columns, tables, and measures.

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

Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.

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

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

No one enjoys looking at spreadsheets! Bring your data to life. Improve your presentation and learn how to translate technical data into actionable insights.

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

Data is all around us, which makes data literacy an essential life skill.

Learn to write efficient code that executes quickly and allocates resources skillfully to avoid unnecessary overhead.

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

Delve further into the Tidyverse by learning to transform and manipulate data with dplyr.

Power BI is a powerful data visualization tool that can be used in reports and dashboards.

Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights!

Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0 in Python.

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

Dive in and learn how to create classes and leverage inheritance and polymorphism to reuse and optimize code.

Learn how to create queries for analytics and data engineering with window functions, the SQL secret weapon!

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

Master your skills in Numpy by learning how to create, sort, filter, and update arrays using NYC’s tree census.

Learn about the world of data engineering in this short course, covering tools and topics like ETL and cloud computing.

Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R.

Learn how to analyze data with spreadsheets using functions such as SUM(), AVERAGE(), and VLOOKUP().

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

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

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

Learn to implement distributed data management and machine learning in Spark using the PySpark package.

Learn to use SQL Server to perform common data manipulation tasks and master common data manipulation tasks using this database system.

Learn the most important PostgreSQL functions for manipulating, processing, and transforming data.

Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.

Learn to use best practices to write maintainable, reusable, complex functions with good documentation.

Expand your spreadsheets vocabulary by diving deeper into data types, including numeric data, logical data, and missing data.

Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.

Learn about data science for managers and businesses and how to use data to strengthen your organization.

Learn how to analyze a SQL table and report insights to management.

Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis with statsmodels in Python.

Learn the fundamentals of data visualization using spreadsheets.

Learn to combine data across multiple tables to answer more complex questions with dplyr.

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

Take your Tableau skills up a notch with advanced analytics and visualizations.

In this interactive Power BI course, you’ll learn how to use Power Query Editor to transform and shape your data to be ready for analysis.

Learn to design databases in SQL.

Learn the key concepts of data modeling on Power BI.

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

Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.

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

The Unix command line helps users combine existing programs in new ways, automate repetitive tasks, and run programs on clusters and clouds.

In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.

Learn to retrieve and parse information from the internet using the Python library scrapy.

Explore the world of Pivot Tables within Google Sheets, and learn how to quickly organize thousands of data points with just a few clicks of the mouse.

A non-coding introduction to cloud computing, covering key concepts, terminology, and tools.

In this course you'll learn the basics of working with time series data.

Dashboards are a must-have in a data-driven world. Increase your impact on business performance with Tableau dashboards.

Learn how to work with dates and times in Python.

Learn to use facets, coordinate systems and statistics in ggplot2 to create meaningful explanatory plots.

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

Learn to use Python for financial analysis using basic skills, including lists, data visualization, and arrays.

In this course you will learn the basics of machine learning for classification.

Julia is a new programming language designed to be the ideal language for scientific computing, machine learning, and data mining.

Discover how to make better business decisions by applying practical data frameworks—no coding required.

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

Learn how and when to use hypothesis testing in R, including t-tests, proportion tests, and chi-square tests.

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

Learn to perform linear and logistic regression with multiple explanatory variables.

You will investigate a dataset from a fictitious company called Databel in Power BI, and need to figure out why customers are churning.

You’ll learn how to (un)pivot, transpose, append and join tables. Gain power with custom columns, M language, and the Advanced Editor.

Learn to clean data as quickly and accurately as possible to help your business move from raw data to awesome insights.

Learn how to implement and schedule data engineering workflows.

Learn how to write unit tests for your Data Science projects in Python using pytest.

Learn to connect Tableau to different data sources and prepare the data for a smooth analysis.

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

Learn the fundamentals of working with big data with PySpark.

Take your R skills up a notch by learning to write efficient, reusable functions.

Learn how to leverage statistical techniques using spreadsheets to more effectively work with and extract insights from your data.

Bash scripting allows you to build analytics pipelines in the cloud and work with data stored across multiple files.

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

Data Analysis Expressions (DAX) allow you to take your Power BI skills to the next level by writing custom functions.

Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.

R Markdown is an easy-to-use formatting language for authoring dynamic reports from R code.

Learn to acquire data from common file formats and systems such as CSV files, spreadsheets, JSON, SQL databases, and APIs.

Consolidate and extend your knowledge of Python data types such as lists, dictionaries, and tuples, leveraging them to solve Data Science problems.

You will investigate a dataset from a fictitious company called Databel in Tableau, and need to figure out why customers are churning.

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

This course focuses on feature engineering and machine learning for time series data.

Find tables, store and manage new tables and views, and write maintainable SQL code to answer business questions.

In this four-hour course, you’ll learn the basics of analyzing time series data in Python.

This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.

In this course you'll learn about basic experimental design, a crucial part of any data analysis.

Learn about modularity, documentation, and automated testing to help you solve data science problems more quickly and reliably.

Learn how to use GitHub's various features, navigate the interface and perform everyday collaborative tasks.

Understand the fundamentals of Machine Learning and how it's applied in the business world.

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

Learn to start developing deep learning models with Keras.

Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.

Help a fictional company in this interactive Power BI case study. You’ll use Power Query, DAX, and dashboards to identify the most in-demand data jobs!

Learn the essentials of parsing, manipulating and computing with dates and times in R.

Learn the basics of spreadsheets by working with rows, columns, addresses, and ranges.

Parse data in any format. Whether it's flat files, statistical software, databases, or data right from the web.

Learn about string manipulation and become a master at using regular expressions.

Master data modeling in Power BI.

Transform almost any dataset into a tidy format to make analysis easier.

Learn how to identify, analyze, remove and impute missing data in Python.

Learn to create deep learning models with the PyTorch library.

Master sampling to get more accurate statistics with less data.

In this course you'll learn how to get your cleaned data ready for modeling.

Shiny is an R package that makes it easy to build interactive web apps directly in R, allowing your team to explore your data as dashboards or visualizations.

Learn how to clean data with Apache Spark in Python.

Learn to write SQL queries to calculate key metrics that businesses use to measure performance.

Enhance your reports with Power BI's Exploratory Data Analysis (EDA). Learn what EDA is for Power BI and how it can help you extract insights from your data.

Discover how Marketing Analysts use data to understand customers and drive business growth.

Apply your skills to import, analyze and visualize Human Resources (HR) data using Power BI.

Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.

Learn the fundamentals of cloud computing with AWS.

In this course, you'll learn the basics of relational databases and how to interact with them.

In this course, you will use T-SQL, the flavor of SQL used in Microsoft's SQL Server for data analysis.

This course teaches the big ideas in machine learning like how to build and evaluate predictive models.

Discover a wide range of DAX calculations and learn how to use them in Microsoft Power BI.

Use data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.

Learn how to build your own SQL reports and dashboards, plus hone your data exploration, cleaning, and validation skills.

Learn the fundamentals of AI. No programming experience required!

Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.

Take your Power BI visualizations up a level with the skills you already have. Learn alternative data storytelling techniques to simply building dashboards.

Create new features to improve the performance of your Machine Learning models.

Learn how to build and test data engineering pipelines in Python using PySpark and Apache Airflow.

In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.

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

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

Master SQL Server programming by learning to create, update, and execute functions and stored procedures.

Reshape DataFrames from a wide to long format, stack and unstack rows and columns, and wrangle multi-index DataFrames.

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

Build up your pandas skills and answer marketing questions by merging, slicing, visualizing, and more!

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

Analyze text data in R using the tidy framework.

Familiarize yourself with Git for version control. Explore how to track, compare, modify, and revert files, as well as collaborate with colleagues using Git.

Manage the complexity in your code using object-oriented programming with the S3 and R6 systems.

Begin your journey with Scala, a popular language for scalable applications and data engineering infrastructure.

Build multiple-input and multiple-output deep learning models using Keras.

Enhance your reports with trend analysis techniques such as time series, decomposition trees, and key influencers.

Learn powerful command-line skills to download, process, and transform data, including machine learning pipeline.

Learn how to design Power BI visualizations and reports with users in mind.

Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.

In this course you'll learn techniques for performing statistical inference on numerical data.

Learn the core techniques necessary to extract meaningful insights from time series data.

Discover the different ways you can enhance your Power BI data importing skills.

Leverage your Python and SQL knowledge to create an ETL pipeline to ingest, transform, and load data into a database.

In this conceptual course (no coding required), you will learn about the four major NoSQL databases and popular engines.

Learn how to make predictions about the future using time series forecasting in R including ARIMA models and exponential smoothing methods.

Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.

Learn to implement custom trading strategies in Python, backtest them, and evaluate their performance!

Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.

Learn efficient techniques in pandas to optimize your Python code.

In this course you'll learn how to perform inference using linear models.

Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.

Explore ways to work with date and time data in SQL Server for time series analysis

Learn to conduct image analysis using Keras with Python by constructing, training, and evaluating convolutional neural networks.

Learn how to use Python to create, run, and analyze A/B tests to make proactive business decisions.

Learn how to create interactive data visualizations, including building and connecting widgets using Bokeh!

Learn how to deploy and maintain assets in Power BI. You’ll get to grips with the Power BI Service interface and key elements in it like workspaces.

Learn how to build a graphical dashboard with spreadsheets to track the performance of financial securities.

Continue your data visualization journey where you'll learn practical techniques for incorporating DAX measures and progressive disclosure in your reports.

Learn about ARIMA models in Python and become an expert in time series analysis.

Learn about how dates work in R, and explore the world of if statements, loops, and functions using financial examples.

Explore Linear Regression in a tidy framework.

Data visualization is one of the most desired skills for data analysts. This course allows you to present your findings better using Tableau.

Evaluate portfolio risk and returns, construct market-cap weighted equity portfolios and learn how to forecast and hedge market risk via scenario generation.

Learn how to calculate meaningful measures of risk and performance, and how to compile an optimal portfolio for the desired risk and return trade-off.

Learn techniques to extract useful information from text and process them into a format suitable for machine learning.

Learn to manipulate and analyze flexibly structured data with MongoDB.

Learn to tame your raw, messy data stored in a PostgreSQL database to extract accurate insights.

Learn how to analyze business processes in R and extract actionable insights from enormous sets of event data.

In this interactive course, you’ll learn how to use functions for your Tableau calculations and when you should use them!

Learn how to make attractive visualizations of geospatial data in Python using the geopandas package and folium maps.

Learn how to build interactive and insight-rich dashboards with Dash and Plotly.

Learn how to create a PostgreSQL database and explore the structure, data types, and how to normalize databases.

Are customers thrilled with your products or is your service lacking? Learn how to perform an end-to-end sentiment analysis task.

Using Python and NumPy, learn the most fundamental financial concepts.

Learn how to visualize time series in R, then practice with a stock-picking case study.

Learn to create your own Python packages to make your code easier to use and share with others.

Learn about risk management, value at risk and more applied to the 2008 financial crisis using Python.

Learn how to access financial data from local files as well as from internet sources.

Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.

Learn basic business modeling including cash flows, investments, annuities, loan amortization, and more using Sheets.

Learn about AWS Boto and harnessing cloud technology to optimize your data workflow.

Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.

Learn the fundamentals of how to build conversational bots using rule-based systems as well as machine learning.

The xts and zoo packages make the task of managing and manipulating ordered observations fast and mistake free.

Learn to read, explore, and manipulate spatial data then use your skills to create informative maps using R.

Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.

Take vital steps towards mastery as you apply your statistical thinking skills to real-world data sets and extract actionable insights from them.

Learn the most important functions for manipulating, processing, and transforming data in SQL Server.

Learn to streamline your machine learning workflows with tidymodels.

Prepare for your next coding interviews in Python.

This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.

Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!

In this course, students will learn to write queries that are both efficient and easy to read and understand.

Learn to tune hyperparameters in Python.

The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.

Visualize seasonality, trends and other patterns in your time series data.

Learn how to efficiently collect and download data from any website using R.

Learn how to structure your PostgreSQL queries to run in a fraction of the time.

Learn how to ensure clean data entry and build dynamic dashboards to display your marketing data.

Create interactive data visualizations in Python using Plotly.

Learn to construct compelling and attractive visualizations that help communicate results efficiently and effectively.

Learn how to segment customers in Python.

Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.

In this course, you'll learn how to collect Twitter data and analyze Twitter text, networks, and geographical origin.

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

Learn how to pull character strings apart, put them back together and use the stringr package.

Leverage the power of Python and PuLP to optimize supply chains.

Sharpen your skills in Oracle SQL including SQL basics, aggregating, combining, and customizing data.

In this course you will learn to fit hierarchical models with random effects.

Learn to effectively convey your data with an overview of common charts, alternative visualization types, and perception-driven style enhancements.

Learn to use essential bioconductor packages using datasets from virus, fungus, human and plants!

Data-driven organizations consistently rely on insights to inspire action and drive change.

Learn to perform linear and logistic regression with multiple explanatory variables.

Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.

Learn to build recommendation engines in Python using machine learning techniques.

Leverage the power of tidyverse tools to create publication-quality graphics and custom-styled reports that communicate your results.

Take your reporting skills to the next level with Tableau’s built-in statistical functions.

Learn how to approach and win competitions on Kaggle.

In this course you'll learn how to leverage statistical techniques for working with categorical data.

Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.

Learn how to manipulate data and create machine learning feature sets in Spark using SQL in Python.

Explore the concepts and applications of linear models with python and build models to describe, predict, and extract insight from data patterns.

In this course, you'll learn about the concepts of random variables, distributions, and conditioning.

In this course you'll learn to use and present logistic regression models for making predictions.

This course will show you how to integrate spatial data into your Python Data Science workflow.

Learn the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.

Learn the bag of words technique for text mining with R.

Learn the fundamentals of exploring, manipulating, and measuring biomedical image data.

Learn how to use conditional formatting with your data through built-in options and by creating custom formulas.

Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.

Learn how to use Python to analyze customer churn and build a model to predict it.

Learn how to detect fraud using Python.

Leverage the tools in the tidyverse to generate, explore and evaluate machine learning models.

Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.