Course
Introduction to R for Finance
- BasicSkill Level
- 4.8+
- 634
Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.
Applied Finance
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Course
AI for Human Resources
- BasicSkill Level
- 4.9+
- 631
Collaborate with AI to make recruiting, people ops, and policy engagement faster and fairer.
Artificial Intelligence
Course
Machine Learning for Finance in Python
- IntermediateSkill Level
- 4.8+
- 622
Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.
Machine Learning
Course
Understanding GDPR
- BasicSkill Level
- 4.8+
- 620
Gain a clear understanding of GDPR principles and how to set up GDPR-compliant processes in this comprehensive course.
Data Literacy
Course
A/B Testing in Python
- IntermediateSkill Level
- 4.8+
- 620
Learn the practical uses of A/B testing in Python to run and analyze experiments. Master p-values, sanity checks, and analysis to guide business decisions.
Probability & Statistics
Course
Building Web Applications with Shiny in R
- IntermediateSkill Level
- 4.8+
- 617
Shiny is an R package that makes it easy to build interactive web apps directly in R, allowing your team to explore your data as dashboards or visualizations.
Software Development
Course
Dimensionality Reduction in Python
- IntermediateSkill Level
- 4.8+
- 616
Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.
Machine Learning
Course
Introduction to Generative AI in Snowflake
- IntermediateSkill Level
- 4.8+
- 607
Learn to build AI applications using Snowflake Cortexs built-in LLM functions for text analysis, generation, and multi-step workflows.
Artificial Intelligence
Course
Natural Language Processing with spaCy
- IntermediateSkill Level
- 4.8+
- 595
Master the core operations of spaCy and train models for natural language processing. Extract information from unstructured data and match patterns.
Machine Learning
Course
Introduction to Portfolio Risk Management in Python
- IntermediateSkill Level
- 4.8+
- 594
Evaluate portfolio risk and returns, construct market-cap weighted equity portfolios and learn how to forecast and hedge market risk via scenario generation.
Applied Finance
Course
Intermediate Regression with statsmodels in Python
- IntermediateSkill Level
- 4.8+
- 594
Learn to perform linear and logistic regression with multiple explanatory variables.
Probability & Statistics
Course
User-Oriented Design in Power BI
- IntermediateSkill Level
- 4.7+
- 594
Learn how to design Power BI visualizations and reports with users in mind.
Data Visualization
Course
Intermediate Google Sheets
- BasicSkill Level
- 4.8+
- 593
Expand your Google Sheets vocabulary by diving deeper into data types, including numeric data, logical data, and missing data.
Data Preparation
Course
Generate a Study Guide
- BasicSkill Level
- 4.8+
- 593
Use a chatbot to create a study guide tailored to your goals and schedule. Build skills with simple, effective prompts.
Artificial Intelligence
Course
Case Study: Building E-Commerce Data Models with dbt
- AdvancedSkill Level
- 4.8+
- 592
Learn how to transform raw data into clean, reliable models with dbt through hands-on, real-world exercises.
Data Engineering
Course
Introduction to Optimization in Python
- IntermediateSkill Level
- 4.8+
- 592
Learn to solve real-world optimization problems using Pythons SciPy and PuLP, covering everything from basic to constrained and complex optimization.
Software Development
Course
Statistical Thinking in Python (Part 1)
- IntermediateSkill Level
- 4.9+
- 589
Build the foundation you need to think statistically and to speak the language of your data.
Probability & Statistics
Course
Feature Engineering for NLP in Python
- AdvancedSkill Level
- 4.8+
- 588
Learn techniques to extract useful information from text and process them into a format suitable for machine learning.
Machine Learning
Course
Reporting with R Markdown
- IntermediateSkill Level
- 4.8+
- 586
R Markdown is an easy-to-use formatting language for authoring dynamic reports from R code.
Reporting
Course
Introduction to Financial Concepts in Python
- BasicSkill Level
- 4.9+
- 584
Using Python and NumPy, learn the most fundamental financial concepts.
Applied Finance
Course
Working with Geospatial Data in Python
- IntermediateSkill Level
- 4.8+
- 584
This course will show you how to integrate spatial data into your Python Data Science workflow.
Data Manipulation
Course
CI/CD for Machine Learning
- AdvancedSkill Level
- 4.8+
- 583
Elevate your Machine Learning Development with CI/CD using GitHub Actions and Data Version Control
Machine Learning
Course
Introduction to Polars
- BasicSkill Level
- 4.8+
- 572
Learn how to efficiently transform, clean, and analyze data using Polars, a Python library for fast data manipulation.
Data Manipulation
Course
Data Visualization in Databricks
- BasicSkill Level
- 4.8+
- 569
Create visualizations and dynamic dashboards with Databricks, turning raw data into clear and actionable insights.
Data Visualization
Course
Market Basket Analysis in Python
- IntermediateSkill Level
- 4.9+
- 568
Explore association rules in market basket analysis with Python by bookstore data and creating movie recommendations.
Machine Learning
Course
Linear Algebra for Data Science in R
- IntermediateSkill Level
- 4.7+
- 564
This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.
Probability & Statistics
Course
Intermediate SQL Querying with AI
- BasicSkill Level
- 4.9+
- 543
Build SQL skills by writing AI prompts that generate queries for sorting, grouping, filtering, and categorizing data.
Data Manipulation
Course
Introduction to Oracle SQL
- BasicSkill Level
- 4.8+
- 535
Sharpen your skills in Oracle SQL including SQL basics, aggregating, combining, and customizing data.
Data Manipulation
Course
Data Ingestion and Semantic Models with Microsoft Fabric
- BasicSkill Level
- 4.8+
- 534
Learn to bring data into Microsoft Fabric, covering Pipelines, Dataflows, Shortcuts, Semantic Models, security, and model refresh.
Other
Course
Building Dashboards with Dash and Plotly
- IntermediateSkill Level
- 4.8+
- 532
Learn how to build interactive and insight-rich dashboards with Dash and Plotly.
Data Visualization
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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