Course
Case Study: Ecommerce Analysis in Power BI
- IntermediateSkill Level
- 4.8+
- 333
In ecommerce, increasing sales and reducing costs are key. Analyze data from an online pet supply company using Power BI.
Data Visualization
Data, AI, and Cloud Courses
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670 Courses
Course
AI-Assisted Restaurant Planning
- BasicSkill Level
- 4.6+
- 331
Interact with a customized GPT and use your prompting skills to plan and open your restaurant.
Artificial Intelligence
Course
Transactions and Error Handling in PostgreSQL
- IntermediateSkill Level
- 4.6+
- 329
Ensure data consistency by learning how to use transactions and handle errors in concurrent environments.
Software Development
Course
Calculations in Sigma
- BasicSkill Level
- 4.8+
- 327
Build dynamic Sigma calculations to explore data, automate logic, and uncover trends with practical business examples.
Data Manipulation
Course
Introduction to Amazon Bedrock
- IntermediateSkill Level
- 4.5+
- 325
Learn to use Amazon Bedrock to access foundation AI models and build with AI - without managing complex infrastructure.
Artificial Intelligence
Course
Advanced Deep Learning with Keras
- IntermediateSkill Level
- 4.7+
- 323
Learn how to develop deep learning models with Keras.
Artificial Intelligence
Course
Practicing Machine Learning Interview Questions in Python
- AdvancedSkill Level
- 4.4+
- 323
Sharpen your knowledge and prepare for your next interview by practicing Python machine learning interview questions.
Machine Learning
Course
Interactive Maps with leaflet in R
- BasicSkill Level
- 4.5+
- 322
Learn how to produce interactive web maps with ease using leaflet.
Data Visualization
Course
GARCH Models in Python
- IntermediateSkill Level
- 4.7+
- 321
Learn about GARCH Models, how to implement them and calibrate them on financial data from stocks to foreign exchange.
Applied Finance
Course
Customer Segmentation in Python
- IntermediateSkill Level
- 4.4+
- 320
Learn how to segment customers in Python.
Data Manipulation
Course
Case Study: Exploratory Data Analysis in R
- BasicSkill Level
- 4.4+
- 320
Use data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.
Exploratory Data Analysis
Course
Dealing With Missing Data in R
- BasicSkill Level
- 4.3+
- 320
Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.
Data Preparation
Course
Introduction to Data Versioning with DVC
- IntermediateSkill Level
- 4.5+
- 315
Explore Data Version Control for ML data management. Master setup, automate pipelines, and evaluate models seamlessly.
Machine Learning
Course
Hierarchical and Recursive Queries in SQL Server
- AdvancedSkill Level
- 4.7+
- 313
Learn how to write recursive queries and query hierarchical data structures.
Software Development
Course
Inference for Numerical Data in R
- AdvancedSkill Level
- 4.4+
- 305
In this course youll learn techniques for performing statistical inference on numerical data.
Probability & Statistics
Course
Digital Transformation with Google Cloud
- BasicSkill Level
- 4.8+
- 304
This course provides an overview of the opportunities and challenges companies encounter in their digital transformation journey.
Cloud
Course
Foundations of Probability in Python
- IntermediateSkill Level
- 4.4+
- 304
Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.
Probability & Statistics
Course
Building Agentic Workflows with LlamaIndex
- AdvancedSkill Level
- 4.4+
- 302
Build AI agentic workflows that can plan, search, remember, and collaborate, using LlamaIndex.
Artificial Intelligence
Course
Case Study: Data Analysis in Databricks
- AdvancedSkill Level
- 4.2+
- 300
Learn to analyze Airbnb data using SQL in Databricks, create dashboards, and derive actionable insights.
Importing & Cleaning Data
Course
Visualizing Time Series Data in R
- IntermediateSkill Level
- 4.6+
- 299
Learn how to visualize time series in R, then practice with a stock-picking case study.
Data Visualization
Course
Customer Analytics and A/B Testing in Python
- IntermediateSkill Level
- 4.4+
- 299
Learn how to use Python to create, run, and analyze A/B tests to make proactive business decisions.
Probability & Statistics
Course
Optimizing Code in Java
- AdvancedSkill Level
- 4.8+
- 297
Learn key techniques to optimize Java performance, from algorithm efficiency to JVM tuning and multithreading.
Software Development
Course
Introduction to Portfolio Analysis in R
- BasicSkill Level
- 4.6+
- 297
Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.
Applied Finance
Course
Introduction to Redshift
- IntermediateSkill Level
- 4.6+
- 292
Master Amazon Redshifts SQL, data management, optimization, and security.
Data Engineering
Course
Case Study: Building Software in Python
- AdvancedSkill Level
- 4.6+
- 291
Build real-world applications with Python—practice using OOP and software engineering principles to write clean and maintainable code.
Software Development
Course
Data Modeling in Sigma
- BasicSkill Level
- 4.7+
- 290
Stop rewriting the same joins and calculations, and dive into well-governed, scalable analytics using Sigma data models.
Reporting
Course
Cleaning Data in SQL Server Databases
- IntermediateSkill Level
- 4.6+
- 290
Develop the skills you need to clean raw data and transform it into accurate insights.
Data Preparation
Course
Introduction to Network Analysis in Python
- IntermediateSkill Level
- 4.7+
- 282
This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.
Probability & Statistics
Course
Plan and Implement a Data Analytics Environment with Microsoft Fabric
- BasicSkill Level
- 4.6+
- 282
Learn how to set up and manage your Microsoft Fabric infrastructure.
Other
Course
Case Study: Competitor Sales Analysis in Power BI
- IntermediateSkill Level
- 4.7+
- 281
This Power BI case study follows a real-world business use case where you will apply the concepts of ETL and visualization.
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.