Course
Develop for Azure Storage
- IntermediateSkill Level
- 4.5+
- 251
Learn how to store, secure, scale, and process data in Azure using Blob Storage, Cosmos DB, queues, and event-driven services.
Cloud
Data, AI, and Cloud Courses
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Course
Concepts in Computer Science
- BasicSkill Level
- 4.6+
- 249
Learn how computers work, design efficient algorithms, and explore computational theory to solve real-world problems.
Software Development
Course
Introduction to Spark with sparklyr in R
- IntermediateSkill Level
- 4.6+
- 246
Learn how to run big data analysis using Spark and the sparklyr package in R, and explore Spark MLIb in just 4 hours.
Data Engineering
Course
Advanced Probability: Uncertainty in Data
- AdvancedSkill Level
- 4.6+
- 245
Develop a better intuition for advanced probability, risk assessment, and simulation techniques to make data-driven business decisions with confidence.
Probability & Statistics
Course
Building Recommendation Engines with PySpark
- AdvancedSkill Level
- 4.5+
- 244
Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users.
Machine Learning
Course
Error and Uncertainty in Google Sheets
- IntermediateSkill Level
- 4.4+
- 244
Learn to distinguish real differences from random noise, and explore psychological crutches we use that interfere with our rational decision making.
Probability & Statistics
Course
Exploring Data Transformation with Google Cloud
- BasicSkill Level
- 4.8+
- 243
Exploring Data Transformation with Google Cloud
Cloud
Course
Survival Analysis in R
- IntermediateSkill Level
- 4.8+
- 243
Learn to work with time-to-event data. The event may be death or finding a job after unemployment. Learn to estimate, visualize, and interpret survival models!
Probability & Statistics
Course
Visualizing Geospatial Data in R
- IntermediateSkill Level
- 4.4+
- 242
Learn to read, explore, and manipulate spatial data then use your skills to create informative maps using R.
Data Visualization
Course
Analyzing IoT Data in Python
- IntermediateSkill Level
- 4.7+
- 239
Learn how to import, clean and manipulate IoT data in Python to make it ready for machine learning.
Data Manipulation
Course
Data Manipulation in KNIME
- BasicSkill Level
- 4.7+
- 235
Automate data manipulation with KNIME, mastering merging, aggregation, database workflows, and advanced file handling.
Data Manipulation
Course
Joining Data with data.table in R
- IntermediateSkill Level
- 4.2+
- 233
This course will show you how to combine and merge datasets with data.table.
Data Manipulation
Cloud
Course
Gen AI Apps: Transform Your Work
- BasicSkill Level
- 4.7+
- 229
This course introduces Google’s gen AI applications, such as Google Workspace with Gemini and NotebookLM.
Cloud
Course
Interactive Data Visualization with plotly in R
- BasicSkill Level
- 4.5+
- 229
Learn how to use plotly in R to create interactive data visualizations to enhance your data storytelling.
Data Visualization
Course
Scalable AI Models with PyTorch Lightning
- IntermediateSkill Level
- 4.5+
- 228
Streamline your AI projects by building modular models and mastering advanced optimization with PyTorch Lightning!
Artificial Intelligence
Course
Practicing Statistics Interview Questions in Python
- AdvancedSkill Level
- 4.5+
- 226
Prepare for your next statistics interview by reviewing concepts like conditional probabilities, A/B testing, the bias-variance tradeoff, and more.
Probability & Statistics
Course
Time Series Analysis in Tableau
- IntermediateSkill Level
- 4.3+
- 225
In this course, you’ll learn to classify, treat and analyze time series; an absolute must, if you’re serious about stepping up as an analytics professional.
Data Visualization
Course
Case Study: Financial Analysis in KNIME
- IntermediateSkill Level
- 4.1+
- 225
Apply financial analysis in KNIME with real-world data, enhancing data preparation and workflow skills.
Applied Finance
Course
Python for R Users
- IntermediateSkill Level
- 4.7+
- 221
This course is for R users who want to get up to speed with Python!
Software Development
Course
Machine Learning for Marketing in Python
- IntermediateSkill Level
- 4.5+
- 219
From customer lifetime value, predicting churn to segmentation - learn and implement Machine Learning use cases for Marketing in Python.
Machine Learning
Cloud
Course
Data Transformation with Spark SQL in Databricks
- IntermediateSkill Level
- 4.6+
- 214
Build end-to-end data pipelines - from cleaning and aggregation to streaming and orchestration.
Data Engineering
Course
Data Visualization in KNIME
- BasicSkill Level
- 4.5+
- 214
Learn to create compelling data visualizations with KNIME, covering charts, components, and dashboards.
Data Visualization
Course
Building Dashboards with shinydashboard
- BasicSkill Level
- 4.4+
- 214
Learn to create interactive dashboards with R using the powerful shinydashboard package. Create dynamic and engaging visualizations for your audience.
Reporting
Course
Analyzing Social Media Data in Python
- IntermediateSkill Level
- 4.8+
- 211
In this course, youll learn how to collect Twitter data and analyze Twitter text, networks, and geographical origin.
Data Manipulation
Course
Writing Efficient Code with pandas
- IntermediateSkill Level
- 4.6+
- 210
Learn efficient techniques in pandas to optimize your Python code.
Software Development
Course
Building Recommendation Engines in Python
- IntermediateSkill Level
- 4.5+
- 210
Learn to build recommendation engines in Python using machine learning techniques.
Machine Learning
Course
Foundations of Inference in Python
- AdvancedSkill Level
- 4.5+
- 205
Get hands-on experience making sound conclusions based on data in this four-hour course on statistical inference in Python.
Probability & Statistics
Course
Develop Azure Event-based and Message-based Solutions
- IntermediateSkill Level
- 4.4+
- 205
Learn to design scalable event-driven architectures in Azure using messaging services and real-world integrations.
Cloud
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.