Course
Foundations of Functional Programming with purrr
- IntermediateSkill Level
- 4.8+
- 121
Learn to easily summarize and manipulate lists using the purrr package.
Software Development
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Course
Bond Valuation and Analysis in R
- IntermediateSkill Level
- 4.9+
- 120
Learn to use R to develop models to evaluate and analyze bonds as well as protect them from interest rate changes.
Applied Finance
Course
Case Studies in Statistical Thinking
- IntermediateSkill Level
- 4.9+
- 119
Take vital steps towards mastery as you apply your statistical thinking skills to real-world data sets and extract actionable insights from them.
Probability & Statistics
Course
Business Process Analytics in R
- IntermediateSkill Level
- 4.8+
- 119
Learn how to analyze business processes in R and extract actionable insights from enormous sets of event data.
Reporting
Course
ChIP-seq with Bioconductor in R
- IntermediateSkill Level
- 4.8+
- 113
Learn how to analyse and interpret ChIP-seq data with the help of Bioconductor using a human cancer dataset.
Probability & Statistics
Course
Practicing Statistics Interview Questions in R
- AdvancedSkill Level
- 4.7+
- 112
In this course, youll prepare for the most frequently covered statistical topics from distributions to hypothesis testing, regression models, and much more.
Probability & Statistics
Course
Building Dashboards with flexdashboard
- IntermediateSkill Level
- 4.7+
- 108
In this course youll learn how to create static and interactive dashboards using flexdashboard and shiny.
Reporting
Course
Using Data Stores in AWS
- IntermediateSkill Level
- 4.6+
- 108
Learn to choose, build with, and secure AWS data stores including DynamoDB and S3 through hands-on console exercises and real-world scenarios.
Cloud
Course
Life Insurance Products Valuation in R
- BasicSkill Level
- 4.9+
- 106
Learn the basics of cash flow valuation, work with human mortality data and build life insurance products in R.
Applied Finance
Course
Intermediate Network Analysis in Python
- AdvancedSkill Level
- 4.8+
- 106
Analyze time series graphs, use bipartite graphs, and gain the skills to tackle advanced problems in network analytics.
Probability & Statistics
Course
Financial Trading in R
- IntermediateSkill Level
- 4.8+
- 105
This course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies.
Applied Finance
Course
Intermediate Interactive Data Visualization with plotly in R
- IntermediateSkill Level
- 4.9+
- 100
Learn to create animated graphics and linked views entirely in R with plotly.
Data Visualization
Course
Working with DeepSeek in Python
- BasicSkill Level
- 4.7+
- 100
Discover what all of the DeepSeek hype was really about! Build applications using DeepSeeks R1 and V3 models.
Artificial Intelligence
Applied Finance
Course
Case Study: HR Analytics in Tableau
- IntermediateSkill Level
- 4.9+
- 98
Explore HR data analysis in Tableau with this case study.
Data Visualization
Course
Case Study: Analyzing Healthcare Data in Tableau
- IntermediateSkill Level
- 4.8+
- 98
Practice Tableau with our healthcare case study. Analyze data, uncover efficiency insights, and build a dashboard.
Data Visualization
Course
Analyzing Survey Data in Python
- IntermediateSkill Level
- 4.7+
- 98
Learn how to analyze survey data with Python and discover when it is appropriate to apply statistical tools that are descriptive and inferential in nature.
Probability & Statistics
Course
Intermediate Portfolio Analysis in R
- IntermediateSkill Level
- 4.9+
- 96
Advance you R finance skills to backtest, analyze, and optimize financial portfolios.
Applied Finance
Course
Multivariate Probability Distributions in R
- IntermediateSkill Level
- 4.8+
- 95
Learn to analyze, plot, and model multivariate data.
Probability & Statistics
Course
Case Study: Inventory Analysis in Tableau
- IntermediateSkill Level
- 4.7+
- 93
Enhance your Tableau skills with this case study on inventory analysis. Analyze a dataset, create calculated fields, and create visualizations.
Data Visualization
Course
Google: Enterprise Agents and Use Cases
- BasicSkill Level
- 5.0+
- 92
Map agent types to your KPIs and explore use cases that solve problems, learn how Gemini Enterprise empowers you to build and orchestrate the right agents.
Cloud
Course
Data Privacy and Anonymization in Python
- AdvancedSkill Level
- 4.9+
- 92
Learn to process sensitive information with privacy-preserving techniques.
Machine Learning
Course
Case Study: Supply Chain Analytics in Tableau
- IntermediateSkill Level
- 4.8+
- 92
Dive into our Tableau case study on supply chain analytics. Tackle shipment, inventory management, and dashboard creation to drive business improvements.
Data Visualization
Course
Scaling with Google Cloud Operations
- BasicSkill Level
- 4.8+
- 89
Scaling with Google Cloud Operations
Cloud
Course
GDPR in Practice: Compliance and Fines
- BasicSkill Level
- 4.9+
- 88
Explore GDPR through real-world cases on data rights, breaches, and compliance challenges.
Data Management
Course
Intermediate Regular Expressions in R
- IntermediateSkill Level
- 4.9+
- 86
Manipulate text data, analyze it and more by mastering regular expressions and string distances in R.
Software Development
Course
Bayesian Regression Modeling with rstanarm
- AdvancedSkill Level
- 4.8+
- 85
Learn how to leverage Bayesian estimation methods to make better inferences about linear regression models.
Probability & Statistics
Course
HR Analytics: Predicting Employee Churn in Python
- IntermediateSkill Level
- 4.8+
- 85
In this course youll learn how to apply machine learning in the HR domain.
Machine Learning
Course
Essential Google Cloud Infrastructure: Foundation
- IntermediateSkill Level
- 4.8+
- 82
This course introduces the comprehensive and flexible infrastructure and platform services provided by Google Cloud with a focus on Infrastructure Foundations.
Cloud
Course
Choice Modeling for Marketing in R
- AdvancedSkill Level
- 4.8+
- 82
Learn to analyze and model customer choice data in R.
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.
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