Course
Input/Output and Streams in Java
- IntermediateSkill Level
- 4.8+
- 701
Advance your Java skills by learning to handle files, process data, and build clean, reusable code using real-world techniques.
Software Development
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Course
CI/CD for Machine Learning
- AdvancedSkill Level
- 4.8+
- 699
Elevate your Machine Learning Development with CI/CD using GitHub Actions and Data Version Control
Machine Learning
Course
Building Web Applications with Shiny in R
- IntermediateSkill Level
- 4.8+
- 695
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
Data Visualization in Google Sheets
- BasicSkill Level
- 4.8+
- 694
Learn the fundamentals of data visualization using Google Sheets.
Data Visualization
Course
Introduction to Optimization in Python
- IntermediateSkill Level
- 4.8+
- 694
Learn to solve real-world optimization problems using Pythons SciPy and PuLP, covering everything from basic to constrained and complex optimization.
Software Development
Course
User-Oriented Design in Power BI
- IntermediateSkill Level
- 4.7+
- 694
Learn how to design Power BI visualizations and reports with users in mind.
Data Visualization
Course
Intermediate Google Sheets
- BasicSkill Level
- 4.8+
- 691
Expand your Google Sheets vocabulary by diving deeper into data types, including numeric data, logical data, and missing data.
Data Preparation
Course
Credit Risk Modeling in Python
- IntermediateSkill Level
- 4.8+
- 690
Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.
Applied Finance
Course
Introduction to Financial Concepts in Python
- BasicSkill Level
- 4.9+
- 686
Using Python and NumPy, learn the most fundamental financial concepts.
Applied Finance
Course
Statistical Thinking in Python (Part 1)
- IntermediateSkill Level
- 4.9+
- 686
Build the foundation you need to think statistically and to speak the language of your data.
Probability & Statistics
Course
Intermediate Regression with statsmodels in Python
- IntermediateSkill Level
- 4.8+
- 686
Learn to perform linear and logistic regression with multiple explanatory variables.
Probability & Statistics
Course
Understanding GDPR
- BasicSkill Level
- 4.8+
- 686
Gain a clear understanding of GDPR principles and how to set up GDPR-compliant processes in this comprehensive course.
Data Literacy
Course
Case Study: Building E-Commerce Data Models with dbt
- AdvancedSkill Level
- 4.8+
- 685
Learn how to transform raw data into clean, reliable models with dbt through hands-on, real-world exercises.
Data Engineering
Course
Introduction to Portfolio Risk Management in Python
- IntermediateSkill Level
- 4.8+
- 682
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
Biomedical Image Analysis in Python
- IntermediateSkill Level
- 4.8+
- 679
Learn the fundamentals of exploring, manipulating, and measuring biomedical image data.
Data Manipulation
Course
Working with Geospatial Data in Python
- IntermediateSkill Level
- 4.8+
- 669
This course will show you how to integrate spatial data into your Python Data Science workflow.
Data Manipulation
Course
Data Visualization in Databricks
- BasicSkill Level
- 4.8+
- 666
Create visualizations and dynamic dashboards with Databricks, turning raw data into clear and actionable insights.
Data Visualization
Course
Foundations of Probability in R
- BasicSkill Level
- 4.8+
- 665
In this course, youll learn about the concepts of random variables, distributions, and conditioning.
Probability & Statistics
Course
Generate a Study Guide
- BasicSkill Level
- 4.8+
- 656
Use a chatbot to create a study guide tailored to your goals and schedule. Build skills with simple, effective prompts.
Artificial Intelligence
Course
Introduction to Text Analysis in R
- IntermediateSkill Level
- 4.8+
- 645
Analyze text data in R using the tidy framework.
Data Manipulation
Course
Linear Algebra for Data Science in R
- IntermediateSkill Level
- 4.7+
- 644
This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.
Probability & Statistics
Course
Introduction to Google Workspace with Gemini
- BasicSkill Level
- 4.8+
- 642
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
Sentiment Analysis in Python
- IntermediateSkill Level
- 4.8+
- 629
Are customers thrilled with your products or is your service lacking? Learn how to perform an end-to-end sentiment analysis task.
Machine Learning
Course
Intermediate SQL Querying with AI
- BasicSkill Level
- 4.9+
- 627
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+
- 627
Sharpen your skills in Oracle SQL including SQL basics, aggregating, combining, and customizing data.
Data Manipulation
Course
Data Types and Functions in Snowflake
- IntermediateSkill Level
- 4.9+
- 619
Learn Snowflake data types and functions to manipulate text, numbers, and dates while building custom functions and pivot tables.
Data Manipulation
Course
Window Functions in Snowflake
- IntermediateSkill Level
- 4.9+
- 612
Discover Snowflake window functions to solve complex data problems with rankings, partitions, and rolling calculations.
Data Manipulation
Course
Building a Go-To-Market Strategy
- BasicSkill Level
- 4.8+
- 610
Create a go-to-market strategy with generative AI: target industries, generate leads, and optimize website keywords.
Artificial Intelligence
Course
Natural Language Processing with spaCy
- IntermediateSkill Level
- 4.8+
- 608
Master the core operations of spaCy and train models for natural language processing. Extract information from unstructured data and match patterns.
Machine Learning
Course
Financial Trading in Python
- IntermediateSkill Level
- 4.8+
- 603
Learn to implement custom trading strategies in Python, backtest them, and evaluate their performance!
Applied Finance
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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