Course
Recommending Skincare Products
- BasicSkill Level
- 4.8+
- 366
Test a chatbot that matches customers with ideal skincare products using your prompting skills for personalized results.
Artificial Intelligence
Data, AI, and Cloud Courses
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Course
Streaming Data with AWS Kinesis and Lambda
- AdvancedSkill Level
- 4.8+
- 359
Learn how to work with streaming data using serverless technologies on AWS.
Cloud
Course
Visualization Best Practices in R
- BasicSkill Level
- 4.9+
- 358
Learn to effectively convey your data with an overview of common charts, alternative visualization types, and perception-driven style enhancements.
Data Visualization
Course
Building Marketing Workflows with n8n
- BasicSkill Level
- 4.9+
- 357
Build marketing workflows in n8n using AI agents. Automate campaign strategy, conversion optimization, and lead generation from scratch.
Artificial Intelligence
Course
Calculations in Sigma
- BasicSkill Level
- 4.9+
- 356
Build dynamic Sigma calculations to explore data, automate logic, and uncover trends with practical business examples.
Data Manipulation
Course
Case Study: Mortgage Trading Analysis in Power BI
- IntermediateSkill Level
- 4.9+
- 356
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
Fully Automated MLOps
- IntermediateSkill Level
- 4.8+
- 356
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
Improving Your Data Visualizations in Python
- IntermediateSkill Level
- 4.8+
- 355
Learn to construct compelling and attractive visualizations that help communicate results efficiently and effectively.
Data Visualization
Course
Fundamentals of Bayesian Data Analysis in R
- IntermediateSkill Level
- 4.9+
- 354
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
Recurrent Neural Networks (RNNs) for Language Modeling with Keras
- AdvancedSkill Level
- 4.8+
- 352
Learn how to use RNNs to classify text sentiment, generate sentences, and translate text between languages.
Artificial Intelligence
Course
Introduction to Financial Statements in Power BI
- IntermediateSkill Level
- 4.8+
- 350
Discover how to use the income statement and balance sheet in Power BI
Applied Finance
Course
Case Study: Analyzing Sales Data in Alteryx
- BasicSkill Level
- 4.8+
- 348
Explore Alteryx Designer in a retail data case study to boost sales analysis and strategic decision-making.
Data Preparation
Course
Intermediate R for Finance
- BasicSkill Level
- 4.8+
- 347
Learn about how dates work in R, and explore the world of if statements, loops, and functions using financial examples.
Applied Finance
Course
Factor Analysis in R
- AdvancedSkill Level
- 4.8+
- 346
Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.
Probability & Statistics
Course
Monte Carlo Simulations in Python
- IntermediateSkill Level
- 4.8+
- 343
Learn to design and run your own Monte Carlo simulations using Python!
Probability & Statistics
Course
Bayesian Data Analysis in Python
- IntermediateSkill Level
- 4.8+
- 340
Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!
Probability & Statistics
Course
Anomaly Detection in Python
- IntermediateSkill Level
- 4.8+
- 338
Detect anomalies in your data analysis and expand your Python statistical toolkit in this four-hour course.
Probability & Statistics
Course
Introduction to Network Analysis in Python
- IntermediateSkill Level
- 4.8+
- 336
This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.
Probability & Statistics
Course
Time Series Analysis in SQL Server
- IntermediateSkill Level
- 4.8+
- 330
Explore ways to work with date and time data in SQL Server for time series analysis
Data Manipulation
Course
HR Analytics: Exploring Employee Data in R
- IntermediateSkill Level
- 4.8+
- 327
Learn how to manipulate, visualize, and perform statistical tests through a series of HR analytics case studies.
Exploratory Data Analysis
Course
Introduction to Linear Modeling in Python
- IntermediateSkill Level
- 4.7+
- 327
Explore the concepts and applications of linear models with python and build models to describe, predict, and extract insight from data patterns.
Probability & Statistics
Course
Communicating with Data in the Tidyverse
- BasicSkill Level
- 4.8+
- 326
Leverage the power of tidyverse tools to create publication-quality graphics and custom-styled reports that communicate your results.
Data Visualization
Course
Case Study: Analyzing City Time Series Data in R
- IntermediateSkill Level
- 4.9+
- 323
Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data.
Probability & Statistics
Course
Data Fluency
- BasicSkill Level
- 4.8+
- 319
Master data fluency! Learn skills for individuals and organizations, understand behaviors, and build a data-fluent culture.
Data Literacy
Data Management
Course
Introduction to Data Quality with Great Expectations
- IntermediateSkill Level
- 4.8+
- 316
Ensure high data quality in data science and data engineering workflows with Pythons Great Expectations library.
Data Engineering
Course
Working with Dates and Times in R
- IntermediateSkill Level
- 4.8+
- 315
Learn the essentials of parsing, manipulating and computing with dates and times in R.
Software Development
Course
Preparing for Your Associate Cloud Engineer Journey
- IntermediateSkill Level
- 4.7+
- 314
This course helps your preparation for the Associate Cloud Engineer exam, learn about the Google Cloud domains in the exam and create a study plan.
Cloud
Course
Building and Optimizing Triggers in SQL Server
- IntermediateSkill Level
- 4.8+
- 311
Learn how to design and implement triggers in SQL Server using real-world examples.
Software Development
Course
Case Study: Ecommerce Analysis in Power BI
- IntermediateSkill Level
- 4.9+
- 310
In ecommerce, increasing sales and reducing costs are key. Analyze data from an online pet supply company using Power BI.
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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