Course
Practical AI with Google Gemini and NotebookLM
- BasicSkill Level
- 4.7+
- 4.1K
Master Gemini and NotebookLM to automate tasks, boost productivity, and work smarter across Googles AI ecosystem.
Artificial Intelligence
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Exploratory Data Analysis in Power BI
- BasicSkill Level
- 4.5+
- 4K
Learn how to build impactful reports with Power BI’s Exploratory Data Analysis (EDA) that uncover insights faster and drive business value.
Exploratory Data Analysis
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Data Communication Concepts
- BasicSkill Level
- 4.4+
- 4K
No one enjoys looking at spreadsheets! Bring your data to life. Improve your presentation and learn how to translate technical data into actionable insights.
Data Literacy
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Data Warehousing Concepts
- IntermediateSkill Level
- 4.7+
- 4K
This introductory and conceptual course will help you understand the fundamentals of data warehousing.
Data Engineering
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Introduction to Python for Finance
- BasicSkill Level
- 4.7+
- 3.9K
Build Python skills to elevate your finance career. Learn how to work with lists, arrays and data visualizations to master financial analyses.
Applied Finance
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Introduction to Shell
- BasicSkill Level
- 4.6+
- 3.9K
The Unix command line helps users combine existing programs in new ways, automate repetitive tasks, and run programs on clusters and clouds.
Software Development
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Case Study: HR Analytics in Power BI
- BasicSkill Level
- 4.5+
- 3.9K
Apply your skills to import, analyze and visualize Human Resources (HR) data using Power BI.
Data Manipulation
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Reports in Power BI
- IntermediateSkill Level
- 4.4+
- 3.8K
Take your Power BI visualizations up a level with the skills you already have. Learn alternative data storytelling techniques to simply building dashboards.
Data Visualization
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Introduction to Data Culture
- BasicSkill Level
- 4.5+
- 3.7K
Learn the key components of building a strong data culture within an organization.
Data Literacy
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Introduction to dbt
- IntermediateSkill Level
- 4.5+
- 3.6K
This course introduces dbt for data modeling, transformations, testing, and building documentation.
Data Engineering
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Introduction to Claude Models
- IntermediateSkill Level
- 4.6+
- 3.5K
Learn how to work with Claude using the Anthropic API to solve real-world tasks and build AI-powered applications.
Artificial Intelligence
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Data Structures and Algorithms in Python
- AdvancedSkill Level
- 4.4+
- 3.5K
Explore data structures such as linked lists, stacks, queues, hash tables, and graphs; and search and sort algorithms!
Software Development
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Data Connections in Power BI
- BasicSkill Level
- 4.6+
- 3.5K
Discover the different ways you can enhance your Power BI data importing skills.
Data Manipulation
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Introduction to Regression in R
- IntermediateSkill Level
- 4.5+
- 3.4K
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R.
Probability & Statistics
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Trend Analysis in Power BI
- IntermediateSkill Level
- 4.6+
- 3.4K
Enhance your reports with trend analysis techniques such as time series, decomposition trees, and key influencers.
Data Manipulation
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Analyzing Data in Tableau
- BasicSkill Level
- 4.6+
- 3.4K
Take your Tableau skills up a notch with advanced analytics and visualizations.
Data Visualization
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Case Study: Analyzing Customer Churn in Excel
- BasicSkill Level
- 4.7+
- 3.4K
You will investigate a dataset from a fictitious company called Databel in Excel, and need to figure out why customers are churning.
Reporting
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Forming Analytical Questions
- BasicSkill Level
- 4.5+
- 3.4K
Learn how to translate business questions to well-formed analytical questions and select the right analytical solutions.
Data Literacy
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Software Engineering Principles in Python
- BasicSkill Level
- 4.5+
- 3.4K
Learn about modularity, documentation, and automated testing to help you solve data science problems more quickly and reliably.
Software Development
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Introduction to Google Sheets
- BasicSkill Level
- 4.5+
- 3.3K
Bring your Google Sheets to life by mastering fundamental skills such as formulas, operations, and cell references.
Data Preparation
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ETL and ELT in Python
- IntermediateSkill Level
- 4.7+
- 3.3K
Learn to build effective, performant, and reliable data pipelines using Extract, Transform, and Load principles.
Data Engineering
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Report Design in Power BI
- IntermediateSkill Level
- 4.4+
- 3.3K
Continue your data visualization journey where youll learn practical techniques for incorporating DAX measures and progressive disclosure in your reports.
Data Visualization
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Understanding Microsoft Azure
- BasicSkill Level
- 4.6+
- 3.2K
Learn about the power of Microsoft Azure and cloud computing software to help you improve your data engineering skills.
Cloud
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Writing Efficient Python Code
- IntermediateSkill Level
- 4.6+
- 3.2K
Learn to write efficient code that executes quickly and allocates resources skillfully to avoid unnecessary overhead.
Software Development
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Deploying and Maintaining Assets in Power BI
- IntermediateSkill Level
- 4.4+
- 3.2K
Learn how to deploy and maintain assets in Power BI. You’ll get to grips with the Power BI Service interface and key elements in it like workspaces.
Data Manipulation
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Intermediate ChatGPT
- BasicSkill Level
- 4.6+
- 3.1K
Learn the architecture behind GPT models and master advanced prompt crafting to unlock ChatGPTs full potential.
Artificial Intelligence
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Introduction to Object-Oriented Programming in Python
- IntermediateSkill Level
- 4.7+
- 3.1K
Discover the fundamental concepts of object-oriented programming (OOP), building custom classes and objects!
Software Development
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Working with Microsoft Copilot
- BasicSkill Level
- 4.3+
- 3K
Learn to work with Microsoft Copilot. Master prompting, navigate Microsoft 365 apps, and build custom agents.
Artificial Intelligence
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Data-Driven Decision Making in SQL
- IntermediateSkill Level
- 4.6+
- 3K
Learn how to analyze a SQL table and report insights to management.
Data Literacy
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Introduction to SQL Server
- BasicSkill Level
- 4.7+
- 3K
Learn to use SQL Server to perform common data manipulation tasks and master common data manipulation tasks using this database system.
Software Development
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.