Introduction to Python
Master the basics of data analysis in Python. Expand your skillset by learning scientific computing with NumPy.
Master the basics of data analysis in Python. Expand your skillset by learning scientific computing with NumPy.
Master the basics of querying tables in relational databases such as MySQL, SQL Server, and PostgreSQL.
Dive into data science using Python and learn how to effectively analyze and visualize your data. No coding experience or skills needed.
Master the basics of data analysis by manipulating common data structures such as vectors, matrices, and data frames.
An introduction to data science with no coding involved.
Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.
Join two or three tables together into one, combine tables using set theory, and work with subqueries in PostgreSQL.
Gain a 360° overview of how to explore and use Power BI to build impactful reports.
Use the world’s most popular Python data science package to manipulate data and calculate summary statistics.
An introduction to data visualization with no coding involved.
Master the complex SQL queries necessary to answer a wide variety of data science questions and prepare robust data sets for analysis in PostgreSQL.
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!
An introduction to machine learning with no coding involved.
Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.
Discover how data engineers lay the groundwork that makes data science possible. No coding involved!
Get started with Tableau, a widely used business intelligence (BI) and analytics software to explore, visualize, and securely share data.
Learn the fundamentals of statistics, including measures of center and spread, probability distributions, and hypothesis testing with no coding involved!
Learn to combine data from multiple tables by joining data together using pandas.
Continue your journey to becoming an R ninja by learning about conditional statements, loops, and vector functions.
Learn how to use NumPy arrays in Python to perform mathematical operations and wrangle data with the best of them!
Learn how to create, customize, and share data visualizations using Matplotlib.
Learn how to analyze data in Excel.
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.
Continue to build your modern Data Science skills by learning about iterators and list comprehensions.
Learn how to create informative and attractive visualizations in Python using the Seaborn library.
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.
Enhance your Power BI knowledge, by learning the fundamentals of Data Analysis Expressions (DAX) such as calculated columns, tables, and measures.
Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.
Learn to write efficient code that executes quickly and allocates resources skillfully to avoid unnecessary overhead.
Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights!
Learn how to explore what's available in a database: the tables, relationships between them, and data stored in them.
Learn how to explore, visualize, and extract insights from data.
Learn to use SQL Server to perform common data manipulation tasks and master common data manipulation tasks using this database system.
Learn to produce meaningful and beautiful data visualizations with ggplot2 by understanding the grammar of graphics.
Learn how to create queries for analytics and data engineering with window functions, the SQL secret weapon!
Learn to transform and manipulate your data using dplyr.
Harness the power of relational databases by learning how they are structured and writing simple SQL commands to start analyzing data.
Learn about the world of data engineering with an overview of all its relevant topics and tools!
Power BI is a powerful data visualization tool that can be used in reports and dashboards.
Learn to use best practices to write maintainable, reusable, complex functions with good documentation.
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.
Learn the most important PostgreSQL functions for manipulating, processing, and transforming data.
Learn how to analyze data with spreadsheets using functions such as SUM(), AVERAGE(), and VLOOKUP().
Learn to implement distributed data management and machine learning in Spark using the PySpark package.
Improve your Python data importing skills and learn to work with web and API data.
Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.
Learn how to create one of the most efficient ways of storing data - relational databases!
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in Python.
Dive in and learn how to create classes and leverage inheritance and polymorphism to reuse and optimize code.
This course is an introduction to version control with Git for data scientists.
Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0.
Use Seaborn's sophisticated visualization tools to make beautiful, informative visualizations with ease.
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 fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.
Learn how to work with dates and times in Python.
The Unix command line helps users combine existing programs in new ways, automate repetitive tasks, and run programs on clusters and clouds.
Learn to design databases in SQL.
Learn to combine data across multiple tables to answer more complex questions with dplyr.
In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
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.
A non-coding introduction to the world of cloud computing.
Expand your spreadsheets vocabulary by diving deeper into data types, including numeric data, logical data, and missing data.
This course introduces Python for financial analysis.
No one enjoys looking at spreadsheets! Bring your data to life. Improve your presentation and learn how to translate technical data into actionable insights.
In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.
Build the foundation you need to think statistically and to speak the language of your data.
Explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior using pandas.
Learn the key concepts of data modeling on Power BI.
Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.
Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.
In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.
Learn to retrieve and parse information from the internet using the Python library scrapy.
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R.
Learn to acquire data from common file formats and systems such as CSV files, spreadsheets, JSON, SQL databases, and APIs.
Learn to use facets, coordinate systems and statistics in ggplot2 to create meaningful explanatory plots.
Learn about data science and how can you use it to strengthen your organization.
Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.
Develop the skills you need to go from raw data to awesome insights as quickly and accurately as possible.
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.
You will investigate a dataset from a fictitious company called Databel in Power BI, and need to figure out why customers are churning.
Learn how to analyze a SQL table and report insights to management.
In this course you will learn the basics of machine learning for classification.
Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.
You’ll learn how to (un)pivot, transpose, append and join tables. Gain power with custom columns, M language, and the Advanced Editor.
Data Analysis Expressions (DAX) allow you to take your Power BI skills to the next level by writing custom functions.
Learn how to implement and schedule data engineering workflows.
In this course you'll learn the basics of working with time series data.
Learn the basics of spreadsheets by working with rows, columns, addresses, and ranges.
Dashboards are a must-have in a data-driven world. Increase your impact on business performance with Tableau dashboards.
Learn to process, transform, and manipulate images at your will.
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.
In this course you'll learn how to get your cleaned data ready for modeling.
Learn how to leverage statistical techniques using spreadsheets to more effectively work with and extract insights from your 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 to start developing deep learning models with Keras.
Learn how to write unit tests for your Data Science projects in Python using pytest.
In this course, you will use T-SQL, the flavor of SQL used in Microsoft's SQL Server for data analysis.
In this course you will learn the details of linear classifiers like logistic regression and SVM.
Learn to connect Tableau to different data sources and prepare the data for a smooth analysis.
Learn the fundamentals of AI. No programming experience required!
Consolidate and extend your knowledge of Python data types such as lists, dictionaries, and tuples, leveraging them to solve Data Science problems.
Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.
Discover how to make better business decisions by applying practical data frameworks—no coding required.
Learn to write SQL queries to calculate key metrics that businesses use to measure performance.
Use data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
Learn about modularity, documentation, and automated testing to help you solve data science problems more quickly and reliably.
Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.
Bash scripting allows you to build analytics pipelines in the cloud and work with data stored across multiple files.
Master data modeling in Power BI.
Learn about string manipulation and become a master at using regular expressions.
Learn the fundamentals of data visualization using spreadsheets.
This course focuses on feature engineering and machine learning for time series data.
R Markdown is an easy-to-use formatting language for authoring dynamic reports from R code.
Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests.
Learn to create deep learning models with the PyTorch library.
Learn how to build your very own dashboard by applying all the SQL concepts and functions you have learned in previous courses.
Parse data in any format. Whether it's flat files, statistical software, databases, or data right from the web.
Understand the fundamentals of Machine Learning and how it's applied in the business world.
In this course you'll learn the basics of analyzing time series data.
Build multiple-input and multiple-output deep learning models using Keras.
Discover a wide range of DAX calculations and learn how to use them in in Microsoft Power BI.
Explore ways to work with date and time data in SQL Server for time series analysis
Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.
Create new features to improve the performance of your Machine Learning models.
Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.
You will investigate a dataset from a fictitious company called Databel in Tableau, and need to figure out why customers are churning.
Learn to perform linear and logistic regression with multiple explanatory variables.
Learn techniques to extract useful information from text and process them into a format suitable for machine learning.
Learn powerful techniques for image analysis in Python using deep learning and convolutional neural networks in Keras.
Learn how to build a model to automatically classify items in a school budget.
In this course, you'll learn the basics of relational databases and how to interact with them.
In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.
Learn how to clean data with Apache Spark in Python.
Learn how to design Power BI visualizations and reports with users in mind.
Build up your pandas skills and answer marketing questions by merging, slicing, visualizing, and more!
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.
Learn how to make predictions with Apache Spark.
Learn powerful command-line skills to download, process, and transform data, including machine learning pipeline.
Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.
Learn how to build data engineering pipelines in Python.
Learn how to approach and win competitions on Kaggle.
Learn to tune hyperparameters in Python.
Find tables, store and manage new tables and views, and write maintainable SQL code to answer business questions.
Learn the essentials of parsing, manipulating and computing with dates and times in R.
Take your Power BI visualizations up a level with the skills you already have. Learn alternative data storytelling techniques to simply building dashboards.
Discover how Marketing Analysts use data to understand customers and drive business growth.
Learn how to identify, analyze, remove and impute missing data in Python.
Reshape DataFrames from a wide to long format, stack and unstack rows and columns, and wrangle multi-index DataFrames.
Master SQL Server programming by learning to create, update, and execute functions and stored procedures.
Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.
Discover the different ways you can enhance your Power BI data importing skills.
Enhance your reports with trend analysis techniques such as time series, decomposition trees, and key influencers.
Begin your journey with Scala, a popular language for scalable applications and data engineering infrastructure.
Learn the fundamentals of cloud computing with AWS.
In this course, students will learn to write queries that are both efficient and easy to read and understand.
Learn how to use Python to create, run, and analyze A/B tests to make proactive business decisions.
Leverage your Python and SQL knowledge to create a pipeline ingesting, transforming and loading data into a database.
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
This course provides a comprehensive introduction to working with base graphics in R.
Learn how to describe relationships between two numerical quantities and characterize these relationships graphically.
Are customers thrilled with your products or is your service lacking? Learn how to perform an end-to-end sentiment analysis task.
Learn about ARIMA models in Python and become an expert in time series analysis.
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 efficiently collect and download data from any website using R.
Continue your data visualization journey where you'll learn practical techniques for incorporating DAX measures and progressive disclosure in your reports.
In this conceptual course (no coding required), you will learn about the four major NoSQL databases and popular engines.
Learn to create your own Python packages to make your code easier to use and share with others.
Learn how to build a graphical dashboard with spreadsheets to track the performance of financial securities.
Learn efficient techniques in pandas to optimize your Python code.
Learn the most important functions for manipulating, processing, and transforming data in SQL Server.
In this course, you'll learn how to import and manage financial data in Python using various tools and sources.
Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.
Create interactive data visualizations in Python using Plotly.
Learn how to build interactive and insight-rich dashboards with Dash and Plotly.
Learn to perform linear and logistic regression with multiple explanatory variables.
Learn the core techniques necessary to extract meaningful insights from time series data.
The xts and zoo packages make the task of managing and manipulating ordered observations fast and mistake free.
In this course you'll learn how to leverage statistical techniques for working with categorical data.
Learn about AWS Boto and harnessing cloud technology to optimize your data workflow.
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
In this course, you'll learn about the concepts of random variables, distributions, and conditioning.
Learn how to make predictions about the future using time series forecasting in R.
Build on top of your Python skills for Finance, by learning how to use datetime, if-statements, DataFrames, and more.
This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.
Learn how to design and implement triggers in SQL Server using real-world examples.
Analyze text data in R using the tidy framework.
Prepare for your next coding interviews in Python.
Learn to write scripts that will catch and handle errors and control for multiple operations happening at once.
Learn the fundamentals of how to build conversational bots using rule-based systems as well as machine learning.
Evaluate portfolio risk and returns, construct market-cap weighted equity portfolios and learn how to forecast and hedge market risk via scenario generation.
In this course you'll learn to add multiple variables to linear models and to use logistic regression for classification.
Learn how to pull character strings apart, put them back together and use the stringr package.
Learn to manipulate and analyze flexibly structured data with MongoDB.
Learn about how dates work in R, and explore the world of if statements, loops, and functions using financial examples.
Explore the concepts and applications of linear models with python and build models to describe, predict, and extract insight from data patterns.
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.
Master sampling to get more accurate statistics with less data.
Learn how to segment customers in Python.
Use RNNs to classify text sentiment, generate sentences, and translate text between languages.
Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.
In this interactive course, you’ll learn how to use functions for your Tableau calculations and when you should use them!
Explore association rules in market basket analysis with Python by bookstore data and creating movie recommendations.
Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.
In this course you will learn to fit hierarchical models with random effects.
Transform almost any dataset into a tidy format to make analysis easier.
In this course, you'll learn how to collect Twitter data and analyze Twitter text, networks, and geographical origin.
Learn to effectively convey your data with an overview of common charts, alternative visualization types, and perception-driven style enhancements.
Learn about risk management, value at risk and more applied to the 2008 financial crisis using Python.
Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.
Explore Linear Regression in a tidy framework.
Learn to analyze and model customer choice data in R.
Learn to work with data using tools from the tidyverse, and master the important skills of taming and tidying your data.
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!
This course teaches you the skills and knowledge necessary to create and manage your own PostgreSQL databases.
Learn to construct compelling and attractive visualizations that help communicate results efficiently and effectively.
Using Python and NumPy, learn the most fundamental financial concepts.
Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.
In this course you'll learn techniques for performing statistical inference on numerical data.
Learn to implement custom trading strategies in Python, backtest them, and evaluate their performance!
Learn to read, explore, and manipulate spatial data then use your skills to create informative maps using R.
Manage the complexity in your code using object-oriented programming with the S3 and R6 systems.
Learn basic business modeling including cash flows, investments, annuities, loan amortization, and more using Sheets.
Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.
Learn survey design using common design structures followed by visualizing and analyzing survey results.
Learn how to manipulate data and create machine learning feature sets in Spark using SQL in Python.
Learn the fundamentals of exploring, manipulating, and measuring biomedical image data.
In this course you'll learn how to perform inference using linear models.
Learn how to make attractive visualizations of geospatial data in Python using the geopandas package and folium maps.
Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!
Learn how to use conditional formatting with your data through built-in options and by creating custom formulas.
Learn how to import and manipulate data with Oracle SQL.
Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.
Learn how to ensure clean data entry and build dynamic dashboards to display your marketing data.
Learn how to detect fraud using Python.
Learn how to use Python to analyze customer churn and build a model to predict it.
Leverage the tools in the tidyverse to generate, explore and evaluate machine learning models.
Step into the role of CFO and learn how to advise a board of directors on key metrics while building a financial forecast.
In this course you'll learn to use and present logistic regression models for making predictions.
In this course you'll learn to build dashboards using the shinydashboard package.
Visualize seasonality, trends and other patterns in your time series data.
Learn about experimental design, and how to explore your data to ask and answer meaningful questions.
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.
Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.
Learn the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.
Level up your SQL knowledge and learn to join tables together, apply relational set theory, and work with subqueries.
Leverage the power of tidyverse tools to create publication-quality graphics and custom-styled reports that communicate your results.
Develop the skills you need to clean raw data and transform it into accurate insights.
Learn to distinguish real differences from random noise, and explore psychological crutches we use that interfere with our rational decision making.
Learn to use essential bioconductor packages using datasets from virus, fungus, human and plants!
Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.
Learn the language of data, study types, sampling strategies, and experimental design.
Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.
In this course you'll learn to analyze and visualize network data with the igraph package.
Learn to build recommendation engines in Python using machine learning techniques.
Learn how to structure your PostgreSQL queries to run in a fraction of the time.
Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.