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!
Build real-world Excel skills in just 4 hours. This course will show you time-saving shortcuts and essential functions.
Learn about the world of data engineering in this short course, covering tools and topics like ETL and cloud computing.
Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.
Take your Julia skills to the next level with our intermediate Julia course. Learn about loops, advanced data structures, timing, and more.
Data visualization is one of the most desired skills for data analysts. This course allows you to present your findings better using Tableau.
Master the basics of data analysis with Python in just four hours. This online course will introduce the Python interface and explore popular packages.
Get hands-on experience making sound conclusions based on data in this four-hour course on statistical inference in Python.
Detect anomalies in your data analysis and expand your Python statistical toolkit in this four-hour course.
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!
Gain a clear understanding of GDPR principles and how to set up GDPR-compliant processes in this comprehensive course.
Learn how to analyze data with spreadsheets using functions such as SUM(), AVERAGE(), and VLOOKUP().
Learn the most important PostgreSQL functions for manipulating, processing, and transforming data.
Familiarize yourself with Git for version control. Explore how to track, compare, modify, and revert files, as well as collaborate with colleagues using Git.
Expand your spreadsheets vocabulary by diving deeper into data types, including numeric data, logical data, and missing data.
Understand the fundamentals of Machine Learning and how it's applied in the business world.
Discover a wide range of DAX calculations and learn how to use them in Microsoft Power BI.
Learn how to perform financial analysis in Power BI or apply any existing financial skills using Power BI data visualizations.
Gain an introduction to data governance, exploring its meaning, purpose, and how to implement a data governance framework.
Build multiple-input and multiple-output deep learning models using Keras.
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 use PostgreSQL to handle time series analysis effectively and apply these techniques to real-world data.
Learn to effectively convey your data with an overview of common charts, alternative visualization types, and perception-driven style enhancements.
Leverage the tools in the tidyverse to generate, explore and evaluate machine learning models.
Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users.
Advance you R finance skills to backtest, analyze, and optimize financial portfolios.
Learn to implement distributed data management and machine learning in Spark using the PySpark package.
Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.
Learn to use facets, coordinate systems and statistics in ggplot2 to create meaningful explanatory plots.
Learn how to analyze a SQL table and report insights to management.
You’ll learn how to (un)pivot, transpose, append and join tables. Gain power with custom columns, M language, and the Advanced Editor.
Master data modeling in Power BI.
Learn how to design Power BI visualizations and reports with users in mind.
Learn techniques to extract useful information from text and process them into a format suitable for machine learning.
Enhance your reports with trend analysis techniques such as time series, decomposition trees, and key influencers.
Parse data in any format. Whether it's flat files, statistical software, databases, or data right from the web.
Learn the practical uses of A/B testing in Python to run and analyze experiments. Master p-values, sanity checks, and analysis to guide business decisions.
Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.
Learn the most important functions for manipulating, processing, and transforming data in SQL Server.
Explore Linear Regression in a tidy framework.
Create and share your own R Packages!
Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data.
Learn A/B testing: including hypothesis testing, experimental design, and confounding variables.
Learn to use R to develop models to evaluate and analyze bonds as well as protect them from interest rate changes.
Learn to predict labels of nodes in networks using network learning and by extracting descriptive features from the network
Learn the principles of feature engineering for machine learning models and how to implement them using the R tidymodels framework.
Take your Power BI visualizations up a level with the skills you already have. Learn alternative data storytelling techniques to simply building dashboards.
Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.
Discover how Marketing Analysts use data to understand customers and drive business growth.
Gain an overview of all the skills and tools needed to excel in Natural Language Processing in R.