Data Analytics
Showing 697–708 of 712 results
Visualizing Data via Snowflake
This course will teach you how to connect Snowflake to BI tools such as Tableau and Power BI. You'll also learn how to visualize and create dashboards within each of the tools.
Web Crawling and Scraping Using Rcrawler
Data is often available on web pages, requiring extra effort and caution to retrieve it. This course is about the Rcrawler package which is a web crawler and scraper that you can use in your R projects.
Web Scraping in Python
Learn to retrieve and parse information from the internet using the Python library scrapy.
Web Scraping in R
Learn how to efficiently collect and download data from any website using R.
What I’ve Learned from Advising 400+ Data Science Projects
During this session, Alice Zhao will share anecdotes that have led to some surprising findings.
What’s for Dinner? Meaningful Choice through Data Science
In this session, we will discuss how recipe kit delivery company Gousto is using Data Science to offer meaningful choice to our customers.
Who is Afraid of the Big Bad Data Wolf?
This session will explore fears around the use of data and how strategic data governance can give people the confidence to be curious and explore data.
Work with Delta Lake tables in Microsoft Fabric
Tables in a Microsoft Fabric lakehouse are based on the Delta Lake storage format commonly used in Apache Spark. By using the enhanced capabilities of delta tables, you can create advanced analytics solutions.
Work with semantic models in Microsoft Fabric
Designing reports for enterprise scale requires more than just connecting to data. Understanding semantic models and strategies for scalability and optimization are key to a successful enterprise implementation. This learning path helps you prepare for the Fabric Analytics Engineer Certification.
Working with Categorical Data in Python
Learn how to manipulate and visualize categorical data using pandas and seaborn.
Working with Data
This course introduces key concepts to proficiently work with data - data cleaning, preprocessing, manipulation, transformation, and core concepts of data visualization and interpretation. You'll also work through examples with a provided dataset.
Working with Dates and Times in R
Learn the essentials of parsing, manipulating and computing with dates and times in R.