Course
Streaming Concepts
- BasicSkill Level
- 4.8+
- 430
Learn about the difference between batching and streaming, scaling streaming systems, and real-world applications.
Data Engineering
Data, AI, and Cloud Courses
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Course
Visualizing Time Series Data in Python
- IntermediateSkill Level
- 4.8+
- 430
Visualize seasonality, trends and other patterns in your time series data.
Data Visualization
Course
Create Engaging Video with Google Vids
- BasicSkill Level
- 4.9+
- 429
Create and refine videos faster with Gemini in Google Vids. Use AI-powered storyboarding and content generation to produce polished videos with ease.
Cloud
Course
Introduction to Julia
- BasicSkill Level
- 4.9+
- 424
Julia is a new programming language designed to be the ideal language for scientific computing, machine learning, and data mining.
Software Development
Course
Fraud Detection in Python
- IntermediateSkill Level
- 4.8+
- 423
Learn how to detect fraud using Python.
Machine Learning
Course
Corporate Finance Fundamentals
- BasicSkill Level
- 4.8+
- 417
Learn key financial concepts such as capital investment, WACC, and shareholder value.
Applied Finance
Course
Dealing with Missing Data in Python
- IntermediateSkill Level
- 4.8+
- 414
Learn how to identify, analyze, remove and impute missing data in Python.
Data Manipulation
Course
Understanding Digital Transformation
- BasicSkill Level
- 4.8+
- 412
Dive into the world of digital transformation and equip yourself to be an agent of change in a rapidly evolving digital landscape.
Data Literacy
Course
Case Study: Analyzing Job Market Data in Tableau
- BasicSkill Level
- 4.8+
- 409
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
Improving Query Performance in PostgreSQL
- IntermediateSkill Level
- 4.8+
- 407
Learn how to structure your PostgreSQL queries to run in a fraction of the time.
Software Development
Course
Bayesian Data Analysis in Python
- IntermediateSkill Level
- 4.8+
- 404
Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!
Probability & Statistics
Course
Reinforcement Learning from Human Feedback (RLHF)
- AdvancedSkill Level
- 4.8+
- 403
Learn how to make GenAI models truly reflect human values while gaining hands-on experience with advanced LLMs.
Artificial Intelligence
Course
Foundations of Probability in Python
- IntermediateSkill Level
- 4.8+
- 402
Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.
Probability & Statistics
Course
Visualization Best Practices in R
- BasicSkill Level
- 4.9+
- 401
Learn to effectively convey your data with an overview of common charts, alternative visualization types, and perception-driven style enhancements.
Data Visualization
Course
Transform and Analyze Data with Microsoft Fabric
- BasicSkill Level
- 4.8+
- 400
Learn how to transform and analyze data within your Microsoft Fabric account
Other
Course
Spoken Language Processing in Python
- AdvancedSkill Level
- 4.8+
- 385
Learn how to load, transform, and transcribe speech from raw audio files in Python.
Data Manipulation
Course
Streaming Data with AWS Kinesis and Lambda
- AdvancedSkill Level
- 4.8+
- 382
Learn how to work with streaming data using serverless technologies on AWS.
Cloud
Course
Recommending Skincare Products
- BasicSkill Level
- 4.8+
- 375
Test a chatbot that matches customers with ideal skincare products using your prompting skills for personalized results.
Artificial Intelligence
Course
Case Study: Mortgage Trading Analysis in Power BI
- IntermediateSkill Level
- 4.9+
- 373
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
Improving Your Data Visualizations in Python
- IntermediateSkill Level
- 4.8+
- 366
Learn to construct compelling and attractive visualizations that help communicate results efficiently and effectively.
Data Visualization
Course
Intermediate R for Finance
- BasicSkill Level
- 4.8+
- 361
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: Analyzing Sales Data in Alteryx
- BasicSkill Level
- 4.8+
- 360
Explore Alteryx Designer in a retail data case study to boost sales analysis and strategic decision-making.
Data Preparation
Course
Factor Analysis in R
- AdvancedSkill Level
- 4.8+
- 354
Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.
Probability & Statistics
Course
Building Marketing Workflows with n8n
- BasicSkill Level
- 4.9+
- 349
Build marketing workflows in n8n using AI agents. Automate campaign strategy, conversion optimization, and lead generation from scratch.
Artificial Intelligence
Course
Anomaly Detection in Python
- IntermediateSkill Level
- 4.8+
- 347
Detect anomalies in your data analysis and expand your Python statistical toolkit in this four-hour course.
Probability & Statistics
Course
Financial Modeling in Google Sheets
- IntermediateSkill Level
- 4.8+
- 340
Learn basic business modeling including cash flows, investments, annuities, loan amortization, and more using Google Sheets.
Applied Finance
Course
Communicating with Data in the Tidyverse
- BasicSkill Level
- 4.8+
- 339
Leverage the power of tidyverse tools to create publication-quality graphics and custom-styled reports that communicate your results.
Data Visualization
Course
Monitoring Machine Learning in Python
- AdvancedSkill Level
- 4.8+
- 338
This course covers everything you need to know to build a basic machine learning monitoring system in Python
Machine Learning
Course
Building and Optimizing Triggers in SQL Server
- IntermediateSkill Level
- 4.8+
- 329
Learn how to design and implement triggers in SQL Server using real-world examples.
Software Development
Course
Data Fluency
- BasicSkill Level
- 4.8+
- 324
Master data fluency! Learn skills for individuals and organizations, understand behaviors, and build a data-fluent culture.
Data Literacy
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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