Course
Introduction to DataLab
- BasicSkill Level
- 4.9+
- 300
Learn the fundamentals of using DataLab, an AI-powered data notebook for data analysis and exploration.
Reporting
Data, AI, and Cloud Courses
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Course
Transactions and Error Handling in PostgreSQL
- IntermediateSkill Level
- 4.8+
- 300
Ensure data consistency by learning how to use transactions and handle errors in concurrent environments.
Software Development
Course
Case Study: Competitor Sales Analysis in Power BI
- IntermediateSkill Level
- 4.8+
- 299
This Power BI case study follows a real-world business use case where you will apply the concepts of ETL and visualization.
Data Visualization
Course
Plan and Implement a Data Analytics Environment with Microsoft Fabric
- BasicSkill Level
- 4.8+
- 297
Learn how to set up and manage your Microsoft Fabric infrastructure.
Other
Course
Case Study: Data Analysis in Databricks
- AdvancedSkill Level
- 4.6+
- 296
Learn to analyze Airbnb data using SQL in Databricks, create dashboards, and derive actionable insights.
Importing & Cleaning Data
Course
Inference for Categorical Data in R
- AdvancedSkill Level
- 4.8+
- 295
In this course youll learn how to leverage statistical techniques for working with categorical data.
Probability & Statistics
Course
Data Transformation with Spark SQL in Databricks
- IntermediateSkill Level
- 4.9+
- 291
Build end-to-end data pipelines - from cleaning and aggregation to streaming and orchestration.
Data Engineering
Course
Practicing Machine Learning Interview Questions in Python
- AdvancedSkill Level
- 4.9+
- 291
Sharpen your knowledge and prepare for your next interview by practicing Python machine learning interview questions.
Machine Learning
Course
Marketing Analytics: Predicting Customer Churn in Python
- IntermediateSkill Level
- 4.9+
- 290
Learn how to use Python to analyze customer churn and build a model to predict it.
Exploratory Data Analysis
Course
A/B Testing in R
- IntermediateSkill Level
- 4.8+
- 290
Learn the basics of A/B testing in R, including how to design experiments, analyze data, predict outcomes, and present results through visualizations.
Probability & Statistics
Course
Cleaning Data in SQL Server Databases
- IntermediateSkill Level
- 4.8+
- 289
Develop the skills you need to clean raw data and transform it into accurate insights.
Data Preparation
Course
Case Study: Building Software in Python
- AdvancedSkill Level
- 4.8+
- 288
Build real-world applications with Python—practice using OOP and software engineering principles to write clean and maintainable code.
Software Development
Course
Practicing Statistics Interview Questions in Python
- AdvancedSkill Level
- 4.7+
- 286
Prepare for your next statistics interview by reviewing concepts like conditional probabilities, A/B testing, the bias-variance tradeoff, and more.
Probability & Statistics
Course
Building Marketing Workflows with n8n
- BasicSkill Level
- 4.9+
- 285
Build marketing workflows in n8n using AI agents. Automate campaign strategy, conversion optimization, and lead generation from scratch.
Artificial Intelligence
Course
Analyzing Survey Data in R
- IntermediateSkill Level
- 4.8+
- 284
Learn survey design using common design structures followed by visualizing and analyzing survey results.
Probability & Statistics
Course
Factor Analysis in R
- AdvancedSkill Level
- 4.8+
- 284
Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.
Probability & Statistics
Course
Introduction to Redshift
- IntermediateSkill Level
- 4.9+
- 283
Master Amazon Redshifts SQL, data management, optimization, and security.
Data Engineering
Course
Case Study: Exploratory Data Analysis in R
- BasicSkill Level
- 4.9+
- 282
Use data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.
Exploratory Data Analysis
Course
Modeling with tidymodels in R
- IntermediateSkill Level
- 4.8+
- 282
Learn to streamline your machine learning workflows with tidymodels.
Machine Learning
Course
Building Recommendation Engines with PySpark
- AdvancedSkill Level
- 4.8+
- 280
Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users.
Machine Learning
Course
Introduction to Amazon Bedrock
- IntermediateSkill Level
- 4.8+
- 276
Learn to use Amazon Bedrock to access foundation AI models and build with AI - without managing complex infrastructure.
Artificial Intelligence
Course
Introduction to Scala
- IntermediateSkill Level
- 4.8+
- 272
Begin your journey with Scala, a popular language for scalable applications and data engineering infrastructure.
Software Development
Course
Introduction to Testing in Java
- AdvancedSkill Level
- 4.9+
- 260
Learn how to write effective tests in Java using JUnit and Mockito to build robust, reliable applications with confidence.
Software Development
Course
Exploring Data Transformation with Google Cloud
- BasicSkill Level
- 4.9+
- 259
Exploring Data Transformation with Google Cloud
Cloud
Course
Gen AI Apps: Transform Your Work
- BasicSkill Level
- 4.8+
- 259
This course introduces Google’s gen AI applications, such as Google Workspace with Gemini and NotebookLM.
Cloud
Course
Statistical Thinking in Python (Part 2)
- IntermediateSkill Level
- 4.8+
- 259
Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.
Probability & Statistics
Course
Marketing Analytics in Tableau
- IntermediateSkill Level
- 4.8+
- 257
Master marketing analytics using Tableau. Analyze performance, benchmark metrics, and optimize strategies across channels.
Data Preparation
Course
Data Transformation in KNIME
- BasicSkill Level
- 4.8+
- 257
Enhance your KNIME skills with our course on data transformation, column operations, and workflow optimization.
Data Preparation
Course
Azure API Management
- IntermediateSkill Level
- 4.8+
- 254
Learn to create, secure, and manage APIs with Azure API Management through hands-on practice.
Cloud
Course
Customer Analytics and A/B Testing in Python
- IntermediateSkill Level
- 4.8+
- 254
Learn how to use Python to create, run, and analyze A/B tests to make proactive business decisions.
Probability & Statistics
FAQs
What is data science?
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.
How can I learn data science?
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.
What skills are required for data science?
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.
What can I use data science for?
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.
Is data science a good career?
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.
Is it difficult to become a data scientist?
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.
Does data science require coding?
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.
How long does it take to become a data scientist?
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.
What topics can I study within data science?
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.
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