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