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Intermediate dbt
- AdvancedSkill Level
- 4.8+
- 1.3K
Take your dbt skills to the next level with this hands-on course designed for data engineers and analytics professionals.
Data Engineering
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DevOps Concepts
- BasicSkill Level
- 4.8+
- 1.3K
In this Introduction to DevOps, you’ll master the DevOps basics and learn the key concepts, tools, and techniques to improve productivity.
Software Development
Course
Regular Expressions in Python
- BasicSkill Level
- 4.8+
- 1.3K
Learn about string manipulation and become a master at using regular expressions.
Software Development
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Software Development with Claude Code
- BasicSkill Level
- 4.9+
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Claude Code brings AI assistance to your terminal. Learn the workflows that turn it into a reliable tool for real software development.
Artificial Intelligence
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Understanding the EU AI Act
- BasicSkill Level
- 4.8+
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Get your AI Act together! Understand the obligations, risks, and requirements of the EU AI Act.
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Vibe Coding with Replit
- BasicSkill Level
- 4.9+
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Learn vibe coding with Replit. Build apps like a Typeform clone, and master securing and deploying Replit apps.
Artificial Intelligence
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Supervised Learning in R: Classification
- IntermediateSkill Level
- 4.7+
- 1.2K
In this course you will learn the basics of machine learning for classification.
Machine Learning
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Data-Driven Decision Making for Business
- BasicSkill Level
- 4.8+
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Discover how to make better business decisions by applying practical data frameworks—no coding required.
Leadership
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Intermediate Data Visualization with ggplot2
- IntermediateSkill Level
- 4.8+
- 1.2K
Learn to use facets, coordinate systems and statistics in ggplot2 to create meaningful explanatory plots.
Data Visualization
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AWS Security and Cost Management Concepts
- BasicSkill Level
- 4.8+
- 1.2K
Master AWS security, governance, and cost optimization to prepare for the Cloud Practitioner certification.
Cloud
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Introduction to MLflow
- AdvancedSkill Level
- 4.8+
- 1.2K
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Machine Learning
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Time Series Analysis in Python
- IntermediateSkill Level
- 4.9+
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In this four-hour course, you’ll learn the basics of analyzing time series data in Python.
Probability & Statistics
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Introduction to Natural Language Processing in Python
- IntermediateSkill Level
- 4.7+
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Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.
Machine Learning
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Intermediate Docker
- IntermediateSkill Level
- 4.7+
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Master multi-stage builds, Docker networking tools, and Docker Compose for optimal containerized applications!
Software Development
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Introduction to Deep Learning with Keras
- IntermediateSkill Level
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Learn to start developing deep learning models with Keras.
Artificial Intelligence
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Explainable AI in Python
- IntermediateSkill Level
- 4.8+
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Artificial Intelligence
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Responsible AI Data Management
- IntermediateSkill Level
- 4.8+
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Learn the theory behind responsibly managing your data for any AI project, from start to finish and beyond.
Artificial Intelligence
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Microsoft Copilot in Excel
- BasicSkill Level
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Stop fighting Excel and start talking to it! Use Copilot in Excel to clean data, build charts, and get answers faster.
Artificial Intelligence
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Introduction to Data Visualization with Plotly in Python
- BasicSkill Level
- 4.8+
- 1.2K
Create interactive data visualizations in Python using Plotly.
Data Visualization
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AI Security and Risk Management
- BasicSkill Level
- 4.8+
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Learn the fundamentals of AI security to protect systems from threats, align security with business goals, and mitigate key risks.
Artificial Intelligence
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Cleaning Data in R
- IntermediateSkill Level
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Learn to clean data as quickly and accurately as possible to help you move from raw data to awesome insights.
Data Preparation
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Data Science for Business
- BasicSkill Level
- 4.8+
- 1.2K
Learn about data science for managers and businesses and how to use data to strengthen your organization.
Data Literacy
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Extreme Gradient Boosting with XGBoost
- IntermediateSkill Level
- 4.9+
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Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
Machine Learning
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Introduction to GPTs
- BasicSkill Level
- 4.8+
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Learn how to use GPT tools responsibly and confidently. Discover how these tools work and techniques for writing prompts and evaluating outputs.
Artificial Intelligence
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Deep Learning for Text with PyTorch
- AdvancedSkill Level
- 4.8+
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Cluster Analysis in Python
- IntermediateSkill Level
- 4.8+
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Machine Learning
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Introduction to Writing Functions in R
- BasicSkill Level
- 4.7+
- 1.1K
Take your R skills up a notch by learning to write efficient, reusable functions.
Software Development
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Experimental Design in Python
- IntermediateSkill Level
- 4.8+
- 1.1K
Implement experimental design setups and perform robust statistical analyses to make precise and valid conclusions!
Probability & Statistics
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Introduction to BigQuery
- IntermediateSkill Level
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Data Engineering
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Introduction to Apache Kafka
- IntermediateSkill Level
- 4.8+
- 1.1K
Master Apache Kafka! From core concepts to advanced architecture, learn to create, manage, and troubleshoot Kafka for real-world data streaming challenges!
Data Engineering
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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