Course
Visualizations in Sigma
- BasicSkill Level
- 4.8+
- 439
Learn to build and customize Sigma charts to tell clear, compelling data stories—no coding required.
Data Visualization
Data, AI, and Cloud Courses
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Course
Monitoring Machine Learning Concepts
- IntermediateSkill Level
- 4.7+
- 438
Learn about the challenges of monitoring machine learning models in production, including data and concept drift, and methods to address model degradation.
Machine Learning
Course
Financial Analytics in Google Sheets
- BasicSkill Level
- 4.0+
- 438
Learn how to build a graphical dashboard with Google Sheets to track the performance of financial securities.
Applied Finance
Course
Streaming Data with AWS Kinesis and Lambda
- AdvancedSkill Level
- 4.4+
- 437
Learn how to work with streaming data using serverless technologies on AWS.
Cloud
Course
Multi-Modal Systems with the OpenAI API
- IntermediateSkill Level
- 4.6+
- 432
Create multi-modal systems using OpenAIs text and audio models, including an end-to-end customer support chatbot!
Artificial Intelligence
Course
Reinforcement Learning from Human Feedback (RLHF)
- AdvancedSkill Level
- 4.3+
- 431
Learn how to make GenAI models truly reflect human values while gaining hands-on experience with advanced LLMs.
Artificial Intelligence
Course
AI for Human Resources
- BasicSkill Level
- 4.7+
- 429
Collaborate with AI to make recruiting, people ops, and policy engagement faster and fairer.
Artificial Intelligence
Course
Case Study: Analyzing Job Market Data in Tableau
- BasicSkill Level
- 4.5+
- 429
In this case study, you’ll use visualization techniques to find out what skills are most in-demand for data scientists, data analysts, and data engineers.
Data Visualization
Course
Introduction to Financial Statements in Power BI
- IntermediateSkill Level
- 4.7+
- 427
Discover how to use the income statement and balance sheet in Power BI
Applied Finance
Course
Corporate Finance Fundamentals
- BasicSkill Level
- 4.7+
- 427
Learn key financial concepts such as capital investment, WACC, and shareholder value.
Applied Finance
Course
Generalized Linear Models in Python
- AdvancedSkill Level
- 4.6+
- 427
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
Artificial Intelligence
Course
Recommending Skincare Products
- BasicSkill Level
- 4.4+
- 423
Test a chatbot that matches customers with ideal skincare products using your prompting skills for personalized results.
Artificial Intelligence
Course
Introduction to Data Quality with Great Expectations
- IntermediateSkill Level
- 4.5+
- 418
Ensure high data quality in data science and data engineering workflows with Pythons Great Expectations library.
Data Engineering
Course
Financial Modeling in Google Sheets
- IntermediateSkill Level
- 4.3+
- 414
Learn basic business modeling including cash flows, investments, annuities, loan amortization, and more using Google Sheets.
Applied Finance
Course
Cluster Analysis in R
- IntermediateSkill Level
- 4.9+
- 411
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
Databricks with the Python SDK
- AdvancedSkill Level
- 4.4+
- 406
Master Databricks with Python: learn to authenticate, manage clusters, automate jobs, and query AI models programmatically.
Artificial Intelligence
Course
Intermediate Data Visualization with Seaborn
- IntermediateSkill Level
- 4.6+
- 405
Use Seaborns sophisticated visualization tools to make beautiful, informative visualizations with ease.
Data Visualization
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
Dealing With Missing Data in R
- BasicSkill Level
- 4.4+
- 403
Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.
Data Preparation
Course
Visualizing Time Series Data in R
- IntermediateSkill Level
- 4.7+
- 402
Learn how to visualize time series in R, then practice with a stock-picking case study.
Data Visualization
Course
Hierarchical and Mixed Effects Models in R
- AdvancedSkill Level
- 4.4+
- 398
In this course you will learn to fit hierarchical models with random effects.
Probability & Statistics
Course
Transform and Analyze Data with Microsoft Fabric
- BasicSkill Level
- 4.5+
- 397
Learn how to transform and analyze data within your Microsoft Fabric account
Other
Course
Dealing with Missing Data in Python
- IntermediateSkill Level
- 4.5+
- 391
Learn how to identify, analyze, remove and impute missing data in Python.
Data Manipulation
Course
Visualizing Geospatial Data in Python
- IntermediateSkill Level
- 4.6+
- 389
Learn how to make attractive visualizations of geospatial data in Python using the geopandas package and folium maps.
Data Visualization
Course
Case Study: Mortgage Trading Analysis in Power BI
- IntermediateSkill Level
- 4.7+
- 388
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
Functions for Manipulating Data in SQL Server
- IntermediateSkill Level
- 4.8+
- 387
Learn the most important functions for manipulating, processing, and transforming data in SQL Server.
Data Manipulation
Course
Fully Automated MLOps
- IntermediateSkill Level
- 4.6+
- 387
Learn about MLOps architecture, CI/CD/CM/CT techniques, and automation patterns to deploy ML systems that can deliver value over time.
Machine Learning
Course
Monte Carlo Simulations in Python
- IntermediateSkill Level
- 4.6+
- 384
Learn to design and run your own Monte Carlo simulations using Python!
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.