Course
Data Visualization in Databricks
- BasicSkill Level
- 4.8+
- 611
Create visualizations and dynamic dashboards with Databricks, turning raw data into clear and actionable insights.
Data Visualization
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Course
Market Basket Analysis in Python
- IntermediateSkill Level
- 4.9+
- 574
Explore association rules in market basket analysis with Python by bookstore data and creating movie recommendations.
Machine Learning
Course
Data Visualization in Google Sheets
- BasicSkill Level
- 4.8+
- 558
Learn the fundamentals of data visualization using Google Sheets.
Data Visualization
Course
Foundations of Probability in R
- BasicSkill Level
- 4.8+
- 552
In this course, youll learn about the concepts of random variables, distributions, and conditioning.
Probability & Statistics
Course
Data Ingestion and Semantic Models with Microsoft Fabric
- BasicSkill Level
- 4.8+
- 549
Learn to bring data into Microsoft Fabric, covering Pipelines, Dataflows, Shortcuts, Semantic Models, security, and model refresh.
Other
Course
Building a Go-To-Market Strategy
- BasicSkill Level
- 4.8+
- 547
Create a go-to-market strategy with generative AI: target industries, generate leads, and optimize website keywords.
Artificial Intelligence
Course
Machine Learning with Tree-Based Models in R
- BasicSkill Level
- 4.9+
- 538
Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.
Machine Learning
Course
Introduction to Bioconductor in R
- IntermediateSkill Level
- 4.8+
- 535
Learn to use essential Bioconductor packages for bioinformatics using datasets from viruses, fungi, humans, and plants!
Probability & Statistics
Course
Introduction to Predictive Analytics in Python
- BasicSkill Level
- 4.8+
- 519
In this course youll learn to use and present logistic regression models for making predictions.
Machine Learning
Course
Quantitative Risk Management in Python
- AdvancedSkill Level
- 4.8+
- 519
Learn about risk management, value at risk and more applied to the 2008 financial crisis using Python.
Applied Finance
Course
Financial Trading in Python
- IntermediateSkill Level
- 4.8+
- 513
Learn to implement custom trading strategies in Python, backtest them, and evaluate their performance!
Applied Finance
Course
Generalized Linear Models in R
- IntermediateSkill Level
- 4.8+
- 504
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.
Probability & Statistics
Course
Practicing Coding Interview Questions in Python
- AdvancedSkill Level
- 4.8+
- 501
Prepare for your next coding interviews in Python.
Software Development
Course
Statistical Techniques in Tableau
- IntermediateSkill Level
- 4.8+
- 488
Take your reporting skills to the next level with Tableau’s built-in statistical functions.
Probability & Statistics
Course
Gemini in Google Docs
- BasicSkill Level
- 4.9+
- 480
Write and edit faster with Gemini in Google Docs. Get AI-powered drafting, rewriting, and content suggestions to create clear, polished documents effortlessly.
Artificial Intelligence
Course
NoSQL Concepts
- IntermediateSkill Level
- 4.8+
- 471
In this conceptual course (no coding required), you will learn about the four major NoSQL databases and popular engines.
Data Engineering
Course
Cleaning Data in PostgreSQL Databases
- IntermediateSkill Level
- 4.8+
- 469
Learn to tame your raw, messy data stored in a PostgreSQL database to extract accurate insights.
Data Preparation
Course
Case Study: Net Revenue Management in Excel
- IntermediateSkill Level
- 4.8+
- 465
You will use Net Revenue Management techniques in Excel for a Fast Moving Consumer Goods company.
Applied Finance
Course
Gemini in Google Sheets
- BasicSkill Level
- 4.9+
- 461
Analyze data smarter with Gemini in Google Sheets. Use AI-powered insights, formula suggestions, and automation to simplify spreadsheets and boost productivity.
Artificial Intelligence
Course
Ensemble Methods in Python
- AdvancedSkill Level
- 4.9+
- 460
Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.
Machine Learning
Course
Math for Finance Professionals
- BasicSkill Level
- 4.8+
- 453
Learn essential finance math skills with practical Excel exercises and real-world examples.
Applied Finance
Course
RNA-Seq with Bioconductor in R
- IntermediateSkill Level
- 4.7+
- 453
Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.
Probability & Statistics
Course
Foundations of PySpark
- IntermediateSkill Level
- 4.7+
- 453
Learn to implement distributed data management and machine learning in Spark using the PySpark package.
Data Engineering
Course
Experimental Design in R
- IntermediateSkill Level
- 4.7+
- 447
In this course youll learn about basic experimental design, a crucial part of any data analysis.
Probability & Statistics
Course
Data Manipulation with data.table in R
- BasicSkill Level
- 4.7+
- 445
Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.
Data Manipulation
Course
Hierarchical and Mixed Effects Models in R
- AdvancedSkill Level
- 4.7+
- 444
In this course you will learn to fit hierarchical models with random effects.
Probability & Statistics
Course
Case Study: Supply Chain Analytics in Power BI
- BasicSkill Level
- 4.8+
- 440
Learn how to use Power BI for supply chain analytics in this case study. Create a make vs. buy analysis tool, calculate costs, and analyze production volumes.
Data Visualization
Course
Gemini in Google Meet
- BasicSkill Level
- 4.9+
- 435
Enhance virtual meetings with Gemini in Google Meet. Leverage AI-driven summaries, notes, and tools to make every meeting more efficient and actionable.
Artificial Intelligence
Course
Feature Engineering with PySpark
- AdvancedSkill Level
- 4.8+
- 435
Learn the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.
Data Manipulation
Course
Introduction to GCP
- BasicSkill Level
- 4.8+
- 432
Get to know the Google Cloud Platform (GCP) with this course on storage, data handling, and business modernization using GCP.
Cloud
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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