Content Roadmap
Discover what we're working on, and what's up next.
BI Tools
Skills to interpret data and communicate insights
New Course: Exploring and Analyzing Data in Tableau
Your team needs to be able to manipulate and visualize data quickly to gain meaningful business insights. As a follow-up to our popular Introduction to Tableau course, Exploring and Analyzing Data in Tableau teaches introductory exploratory data analysis skills for BI analysts, data scientists, and machine learning scientists.
- Aligned with the Tableau Desktop Specialist certification curriculum
- Create and interpret common data visualization types
- Understand data grouping and hierarchies
- Apply analytics to Tableau Worksheets
Statistics
Statistics in Python for beginners
New Course: Introduction to Statistics in Python
In order to make use of powerful statistical models, run experiments, and make predictions with machine learning, data scientists and data analysts first need to understand a few basic statistical concepts. This new introductory course provides an entry point into DataCamp’s full Python statistics curriculum.
- Understand and calculate descriptive statistics from mean and variance to correlation coefficients
- Work with random numbers, which form the basis of all simulations
- Get to know the normal, binomial, and Poisson distributions that underlie many predictive models
Exploring the improved statistics curriculum in R
New Course: Intermediate Regression in R
Learn how to fit sophisticated models with high predictive power. While Introduction to Regression in R considers variables in isolation—such as factors that lead to a churned customer—this course teaches learners how to make more accurate predictions by analyzing multiple variables in their model. This course follows Introduction to Regression in R, and unlocks the door to the rest of DataCamp's statistical modeling curriculum in R.
- Make the transition from simple linear regression to multiple linear regression
- Understand the impact of interactions between explanatory variables
- Understand the strengths and limitations of linear regression models
Cloud-based Tools
Increase your team’s confidence using cloud tools
New Course: Cloud Computing for Everyone
Working in the cloud has many advantages: you aren't constrained by hardware limitations; you can quickly share data and analyses across remote teams; and you gain access to data security, quality control, and disaster recovery tools.
Give everyone on your team the skills to navigate the cloud with more confidence. As part of our new Data Literacy skill track, Cloud Computing for Everyone is appropriate for anyone in a non-technical role.
- Discover the range of tools provided by major cloud providers such as Amazon Web Services, Microsoft Azure, and Google Cloud
- Learn how using cloud tools can increase productivity and save money
- Gain the confidence to ask questions about how to optimize your use of cloud tools
Automate real-time data analysis in the cloud
New Course: Streaming Data with Amazon Kinesis and AWS Lambda
In a fast-paced business environment, you don't always have the luxury of time to analyze data. But there is an increasing number of tasks that need to be performed in real-time—automated trading, dynamic pricing, social media analysis, website clickstream analysis, warehouse management, fleet management, and more. This course is designed for intermediate to advanced data engineers who need to automate real-time data analysis to support critical business insights.
- Understand the use cases for batch processing vs. stream processing
- Learn how to create and process data streams using AWS Kinesis
- Set up serverless processing of streams using AWS Lambda
- Learn to analyze and visualize streams
Please note these may change due to market demands, experiment results, company strategy, capacity, and other unforeseen circumstances.