Course
Reinforcement Learning from Human Feedback (RLHF)
- AdvancedSkill Level
- 4.3+
- 427
Learn how to make GenAI models truly reflect human values while gaining hands-on experience with advanced LLMs.
Artificial Intelligence
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670 Courses
Course
Introduction to Financial Statements in Power BI
- IntermediateSkill Level
- 4.7+
- 426
Discover how to use the income statement and balance sheet in Power BI
Applied Finance
Course
Deep Reinforcement Learning in Python
- AdvancedSkill Level
- 4.4+
- 422
Learn and use powerful Deep Reinforcement Learning algorithms, including refinement and optimization techniques.
Artificial Intelligence
Course
Recommending Skincare Products
- BasicSkill Level
- 4.3+
- 418
Test a chatbot that matches customers with ideal skincare products using your prompting skills for personalized results.
Artificial Intelligence
Course
Conquering Data Bias
- BasicSkill Level
- 4.7+
- 417
Unlock your datas potential by learning to detect and mitigate bias for precise analysis and reliable models.
Data Management
Course
Generalized Linear Models in Python
- AdvancedSkill Level
- 4.6+
- 416
Extend your regression toolbox with the logistic and Poisson models and learn to train, understand, and validate them, as well as to make predictions.
Probability & Statistics
Course
Fundamentals of Bayesian Data Analysis in R
- IntermediateSkill Level
- 4.7+
- 414
Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.
Probability & Statistics
Course
Transform and Analyze Data with Microsoft Fabric
- BasicSkill Level
- 4.4+
- 412
Learn how to transform and analyze data within your Microsoft Fabric account
Other
Course
Financial Analytics in Google Sheets
- BasicSkill Level
- 4.0+
- 412
Learn how to build a graphical dashboard with Google Sheets to track the performance of financial securities.
Applied Finance
Course
Visualizations in Sigma
- BasicSkill Level
- 4.8+
- 411
Learn to build and customize Sigma charts to tell clear, compelling data stories—no coding required.
Data Visualization
Course
Databricks with the Python SDK
- AdvancedSkill Level
- 4.3+
- 411
Master Databricks with Python: learn to authenticate, manage clusters, automate jobs, and query AI models programmatically.
Artificial Intelligence
Course
Cluster Analysis in R
- IntermediateSkill Level
- 4.8+
- 409
Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.
Machine Learning
Course
Working with Dates and Times in R
- IntermediateSkill Level
- 4.7+
- 408
Learn the essentials of parsing, manipulating and computing with dates and times in R.
Software Development
Course
Feature Engineering with PySpark
- AdvancedSkill Level
- 4.6+
- 408
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 Google Workspace with Gemini
- BasicSkill Level
- 4.5+
- 407
You learn about the key features of Gemini and how they can be used to improve productivity and efficiency in Google Workspace.
Artificial Intelligence
Course
Anomaly Detection in Python
- IntermediateSkill Level
- 4.7+
- 404
Detect anomalies in your data analysis and expand your Python statistical toolkit in this four-hour course.
Probability & Statistics
Course
Winning a Kaggle Competition in Python
- AdvancedSkill Level
- 4.7+
- 402
Learn how to approach and win competitions on Kaggle.
Machine Learning
Course
Object-Oriented Programming with S3 and R6 in R
- AdvancedSkill Level
- 4.5+
- 400
Manage the complexity in your code using object-oriented programming with the S3 and R6 systems.
Software Development
Course
Categorical Data in the Tidyverse
- BasicSkill Level
- 4.3+
- 399
Get ready to categorize! In this course, you will work with non-numerical data, such as job titles or survey responses, using the Tidyverse landscape.
Data Manipulation
Course
Software Development with Claude Code
- BasicSkill Level
- 4.6+
- 396
Claude Code brings AI assistance to your terminal. Learn the workflows that turn it into a reliable tool for real software development.
Artificial Intelligence
Course
Introduction to Model Context Protocol (MCP)
- IntermediateSkill Level
- 4.6+
- 395
Integrate AI/LLM applications with APIs, databases, and filesystems easier than ever before with the Model Context Protocol (MCP).
Artificial Intelligence
Course
Financial Modeling in Google Sheets
- IntermediateSkill Level
- 4.3+
- 394
Learn basic business modeling including cash flows, investments, annuities, loan amortization, and more using Google Sheets.
Applied Finance
Course
Monte Carlo Simulations in Python
- IntermediateSkill Level
- 4.5+
- 393
Learn to design and run your own Monte Carlo simulations using Python!
Probability & Statistics
Course
Marketing Analytics in Google Sheets
- IntermediateSkill Level
- 4.5+
- 391
Learn how to ensure clean data entry and build dynamic dashboards to display your marketing data.
Reporting
Course
Fine-Tuning with Llama 3
- IntermediateSkill Level
- 4.6+
- 390
Fine-tune Llama for custom tasks using TorchTune, and learn techniques for efficient fine-tuning such as quantization.
Artificial Intelligence
Course
Streaming Data with AWS Kinesis and Lambda
- AdvancedSkill Level
- 4.5+
- 387
Learn how to work with streaming data using serverless technologies on AWS.
Cloud
Course
Intermediate R for Finance
- BasicSkill Level
- 4.4+
- 387
Learn about how dates work in R, and explore the world of if statements, loops, and functions using financial examples.
Applied Finance
Course
Case Study: Mortgage Trading Analysis in Power BI
- IntermediateSkill Level
- 4.7+
- 383
In this Power BI case study you’ll play the role of a junior trader, analyzing mortgage trading and enhancing your data modeling and financial analysis skills.
Applied Finance
Course
Dealing with Missing Data in Python
- IntermediateSkill Level
- 4.4+
- 383
Learn how to identify, analyze, remove and impute missing data in Python.
Data Manipulation
Course
Hierarchical and Mixed Effects Models in R
- AdvancedSkill Level
- 4.4+
- 382
In this course you will learn to fit hierarchical models with random effects.
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.