Discover a wide range of DAX calculations and learn how to use them in Microsoft Power BI.
Learn to connect Tableau to different data sources and prepare the data for a smooth analysis.
In this four-hour course, you’ll learn the basics of analyzing time series data in Python.
Learn to write SQL queries to calculate key metrics that businesses use to measure performance.
Learn about string manipulation and become a master at using regular expressions.
Learn to use facets, coordinate systems and statistics in ggplot2 to create meaningful explanatory plots.
Master data modeling in Power BI.
Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.
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 fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.
Elevate your data storytelling skills and discover how to tell great stories that drive change with your audience.
This introductory and conceptual course will help you understand the fundamentals of data warehousing.
Reshape DataFrames from a wide to long format, stack and unstack rows and columns, and wrangle multi-index DataFrames.
Learn how to implement and schedule data engineering workflows.
Learn to process, transform, and manipulate images at your will.
Expand your spreadsheets vocabulary by diving deeper into data types, including numeric data, logical data, and missing data.
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.
In this course you'll learn how to get your cleaned data ready for modeling.
Discover how Marketing Analysts use data to understand customers and drive business growth.
Learn to create deep learning models with the PyTorch library.
Analyze text data in R using the tidy framework.
Learn to perform linear and logistic regression with multiple explanatory variables.
Apply your skills to import, analyze and visualize Human Resources (HR) data using Power BI.
Learn to start developing deep learning models with Keras.
In this course you will learn the basics of machine learning for classification.
Discover how MLOps can take machine learning models from local notebooks to functioning models in production that generate real business value.
Learn how to build and test data engineering pipelines in Python using PySpark and Apache Airflow.
In this course, you will use T-SQL, the flavor of SQL used in Microsoft's SQL Server for data analysis.
Explore data structures such as linked lists, stacks, queues, hash tables, and graphs; and search and sort algorithms!
Learn how to use GitHub's various features, navigate the interface and perform everyday collaborative tasks.
Gain an introduction to data governance, exploring its meaning, purpose, and how to implement a data governance framework.
Learn to perform linear and logistic regression with multiple explanatory variables.
Learn how to design Power BI visualizations and reports with users in mind.
Build on top of your Python skills for Finance, by learning how to use datetime, if-statements, DataFrames, and more.
In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.
Take your Power BI visualizations up a level with the skills you already have. Learn alternative data storytelling techniques to simply building dashboards.
Learn the fundamentals of data visualization using spreadsheets.
Learn how to use RNNs to classify text sentiment, generate sentences, and translate text between languages.
R Markdown is an easy-to-use formatting language for authoring dynamic reports from R code.
Enhance your reports with trend analysis techniques such as time series, decomposition trees, and key influencers.
Leverage your Python and SQL knowledge to create an ETL pipeline to ingest, transform, and load data into a database.
Take your R skills up a notch by learning to write efficient, reusable functions.
Learn to acquire data from common file formats and systems such as CSV files, spreadsheets, JSON, SQL databases, and APIs.
Discover the different ways you can enhance your Power BI data importing skills.
This course focuses on feature engineering and machine learning for time series data.
You will investigate a dataset from a fictitious company called Databel in Tableau, and need to figure out why customers are churning.
Learn how to explore, visualize, and extract insights from data.
Learn the power of deep learning in PyTorch. Build your first neural network, adjust hyperparameters, and tackle classification and regression problems.
Learn to create your own Python packages to make your code easier to use and share with others.
Write functions to forecast time series of food prices in Rwanda.
Check what passwords fail to conform to the National Institute of Standards and Technology password guidelines.
Use a variety of data manipulation techniques to explore different aspects of Lego's history!