Course
Case Study: Analyzing Customer Churn in Tableau
- BasicSkill Level
- 4.8+
- 1,050 reviews
You will investigate a dataset from a fictitious company called Databel in Tableau, and need to figure out why customers are churning.
Data Visualization
Follow short videos led by expert instructors and then practice what you’ve learned with interactive exercises in your browser.
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Course
You will investigate a dataset from a fictitious company called Databel in Tableau, and need to figure out why customers are churning.
Data Visualization
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Practice data storytelling using real-world examples! Communicate complex insights effectively with a dataset of certified green businesses.
Data Literacy
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Conquer NoSQL and supercharge data workflows. Learn Snowflake to work with big data, Postgres JSON for handling document data, and Redis for key-value data.
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This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.
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Learn Snowflake data types and functions to manipulate text, numbers, and dates while building custom functions and pivot tables.
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Learn how to structure your PostgreSQL queries to run in a fraction of the time.
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Learn to use the KNIME Analytics Platform for data access, cleaning, and analysis with a no-code/low-code approach.
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Cloud
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Learn how to transform raw data into clean, reliable models with dbt through hands-on, real-world exercises.
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Learn how to write effective tests in Java using JUnit and Mockito to build robust, reliable applications with confidence.
Software Development
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In this course, youll learn the basics of relational databases and how to interact with them.
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Learn essential finance math skills with practical Excel exercises and real-world examples.
Applied Finance
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Build SQL skills by writing AI prompts that generate queries for sorting, grouping, filtering, and categorizing data.
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Learn to write scripts that will catch and handle errors and control for multiple operations happening at once.
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Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.
Machine Learning
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Dive into the world of digital transformation and equip yourself to be an agent of change in a rapidly evolving digital landscape.
Data Literacy
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Learn about the challenges of monitoring machine learning models in production, including data and concept drift, and methods to address model degradation.
Machine Learning
Course
Explore multi-agent system architecture and deployment using Googles ADK and Google Cloud infrastructure for production-grade agent applications.
Cloud
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Artificial Intelligence
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Use Seaborns sophisticated visualization tools to make beautiful, informative visualizations with ease.
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Artificial Intelligence
Data science is an area of expertise focused on gaining information from data. Using programming skills, scientific methods, algorithms, and more, data scientists analyze data to form actionable insights.
You’ll need to learn a programming language such as Python or R and master the principles of math and statistics. Knowledge of data analysis methods and data science tools is also essential. There are many ways to learn data science. As well as formal means of education, such as a degree or university study, there are plenty of other resources to help you learn at your own pace. As well as online courses and tutorials, there are books, videos, and more.
As well as knowledge of mathematics and statistics, data scientists need programming skills in languages such as Python, R, and SQL. Additionally, data science requires the ability to work with large data sets, knowledge of data visualization, data wrangling, and database management. Skills in machine learning and deep learning can also be useful.
In a professional capacity, almost every industry can use data science to some degree. Healthcare organizations use data science to detect and cure diseases, while finance companies use it to detect and prevent fraud. All kinds of industries use data science for marketing, such as building recommendation systems and analyzing customer churn.
Yes, data science is among the fastest-growing sectors in the US and worldwide. It’s also one of the best-paid careers out there. According to data from Payscale, experience data scientists earn an average of $97,609 and have a satisfaction rating of four stars out of five in the US.
There are a few things to consider here. First, data science degrees can be competitive to get onto, often requiring consistently high grades. Similarly, many of the skills required for data science require a lot of study and patience. It can take several months to master all of the necessary basics, as well as a lot of practical experience to secure an entry-level position.
Yes, you’ll need some coding experience in languages such as Python, R, SQL, Java, and C/C++. However, due to its relatively simple syntax, Python programming language is often the preferred choice among newcomers.
For a person with no prior coding experience and/or mathematical background, it can typically take 7 to 12 months of intensive studies to be at the level of an entry-level data scientist. However, it is important to remember that learning only the theoretical basis of data science may not make you a real data scientist.
Once you’ve mastered the foundations of data science, you can then specialize in a variety of areas, including machine learning, artificial intelligence, big data analysis, business analytics and intelligence, data mining, and more.
Make progress on the go with our mobile courses and daily 5-minute coding challenges.