Course
Python for MATLAB Users
- BasicSkill Level
- 4.8+
- 129
Transition from MATLAB by learning some fundamental Python concepts, and diving into the NumPy and Matplotlib packages.
Software Development
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Course
Analyzing Social Media Data in R
- IntermediateSkill Level
- 4.9+
- 126
Extract and visualize Twitter data, perform sentiment and network analysis, and map the geolocation of your tweets.
Data Manipulation
Course
Programming Paradigm Concepts
- BasicSkill Level
- 4.8+
- 124
Explore a range of programming paradigms, including imperative and declarative, procedural, functional, and object-oriented programming.
Software Development
Course
Dimensionality Reduction in R
- BasicSkill Level
- 4.8+
- 124
Learn dimensionality reduction techniques in R and master feature selection and extraction for your own data and models.
Machine Learning
Course
Feature Engineering in R
- IntermediateSkill Level
- 4.8+
- 123
Learn the principles of feature engineering for machine learning models and how to implement them using the R tidymodels framework.
Machine Learning
Course
Support Vector Machines in R
- IntermediateSkill Level
- 4.9+
- 122
This course will introduce the support vector machine (SVM) using an intuitive, visual approach.
Machine Learning
Course
Case Studies in Statistical Thinking
- IntermediateSkill Level
- 4.9+
- 116
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
Foundations of Functional Programming with purrr
- IntermediateSkill Level
- 4.8+
- 116
Learn to easily summarize and manipulate lists using the purrr package.
Software Development
Course
Advanced NLP with spaCy
- IntermediateSkill Level
- 4.6+
- 116
Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.
Machine Learning
Course
Practicing Statistics Interview Questions in R
- AdvancedSkill Level
- 4.7+
- 104
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
Choice Modeling for Marketing in R
- AdvancedSkill Level
- 4.8+
- 99
Learn to analyze and model customer choice data in R.
Probability & Statistics
Course
Scaling with Google Cloud Operations
- BasicSkill Level
- 4.8+
- 97
Scaling with Google Cloud Operations
Cloud
Course
DataLab with SQL
- BasicSkill Level
- 4.8+
- 97
Elevate your analysis with this hands-on course using SQL with DataLab workbooks.
Reporting
Course
Case Study: HR Analytics in Tableau
- IntermediateSkill Level
- 4.9+
- 96
Explore HR data analysis in Tableau with this case study.
Data Visualization
Course
Working with DeepSeek in Python
- BasicSkill Level
- 4.7+
- 95
Discover what all of the DeepSeek hype was really about! Build applications using DeepSeeks R1 and V3 models.
Artificial Intelligence
Course
ChIP-seq with Bioconductor in R
- IntermediateSkill Level
- 4.8+
- 91
Learn how to analyse and interpret ChIP-seq data with the help of Bioconductor using a human cancer dataset.
Probability & Statistics
Applied Finance
Course
Intermediate Interactive Data Visualization with plotly in R
- IntermediateSkill Level
- 4.9+
- 90
Learn to create animated graphics and linked views entirely in R with plotly.
Data Visualization
Course
Building Dashboards with flexdashboard
- IntermediateSkill Level
- 4.7+
- 89
In this course youll learn how to create static and interactive dashboards using flexdashboard and shiny.
Reporting
Course
GDPR in Practice: Compliance and Fines
- BasicSkill Level
- 4.9+
- 87
Explore GDPR through real-world cases on data rights, breaches, and compliance challenges.
Data Management
Course
Case Study: Analyzing Healthcare Data in Tableau
- IntermediateSkill Level
- 4.8+
- 87
Practice Tableau with our healthcare case study. Analyze data, uncover efficiency insights, and build a dashboard.
Data Visualization
Course
Case Study: Inventory Analysis in Tableau
- IntermediateSkill Level
- 4.7+
- 87
Enhance your Tableau skills with this case study on inventory analysis. Analyze a dataset, create calculated fields, and create visualizations.
Data Visualization
Course
Case Study: Supply Chain Analytics in Tableau
- IntermediateSkill Level
- 4.8+
- 84
Dive into our Tableau case study on supply chain analytics. Tackle shipment, inventory management, and dashboard creation to drive business improvements.
Data Visualization
Course
Analyzing Survey Data in Python
- IntermediateSkill Level
- 4.7+
- 83
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
Bond Valuation and Analysis in Python
- BasicSkill Level
- 4.9+
- 77
Learn how bonds work and how to price them and assess some of their risks using the numpy and numpy-financial packages.
Applied Finance
Course
Case Studies: Network Analysis in R
- BasicSkill Level
- 4.7+
- 76
Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.
Probability & Statistics
Course
Interactive Data Visualization with Bokeh
- IntermediateSkill Level
- 4.7+
- 76
Learn how to create interactive data visualizations, including building and connecting widgets using Bokeh!
Data Visualization
Course
HR Analytics: Predicting Employee Churn in Python
- IntermediateSkill Level
- 4.8+
- 71
In this course youll learn how to apply machine learning in the HR domain.
Machine Learning
Course
Case Study: Net Revenue Management in Google Sheets
- IntermediateSkill Level
- 4.4+
- 69
You will use Net Revenue Management techniques in Google Sheets for a Fast Moving Consumer Goods company.
Applied Finance
Course
Building Response Models in R
- IntermediateSkill Level
- 4.9+
- 67
Learn to build simple models of market response to increase the effectiveness of your marketing plans.
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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