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711 Courses

Course

Image Processing in Python

  • IntermediateSkill Level
  • 4.8+
  • 202 reviews

Learn to process, transform, and manipulate images at your will.

Machine Learning

4 hours

Course

Monetizing Artificial Intelligence

  • BasicSkill Level
  • 4.7+
  • 821 reviews

Explore AI and data monetization strategies, build ethical infrastructures, and align products with business goals.

Artificial Intelligence

1 hour

Course

Claude 101

  • BasicSkill Level
  • 4.8+
  • 68 reviews

Learn how to use Claude for everyday work tasks, understand core features, and explore resources for more advanced learning on other topics.

Artificial Intelligence

2 hours

Course

Deep Learning for Text with PyTorch

  • AdvancedSkill Level
  • 4.7+
  • 720 reviews

Discover the exciting world of Deep Learning for Text with PyTorch and unlock new possibilities in natural language processing and text generation.

Artificial Intelligence

4 hours

Course

Extreme Gradient Boosting with XGBoost

  • IntermediateSkill Level
  • 4.8+
  • 248 reviews

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

4 hours

Course

Intermediate dbt

  • AdvancedSkill Level
  • 4.7+
  • 870 reviews

Take your dbt skills to the next level with this hands-on course designed for data engineers and analytics professionals.

Data Engineering

2 hours

Course

Data-Driven Decision Making for Business

  • BasicSkill Level
  • 4.8+
  • 856 reviews

Discover how to make better business decisions by applying practical data frameworks—no coding required.

Leadership

2 hours

Course

Applying SQL to Real-World Problems

  • IntermediateSkill Level
  • 4.8+
  • 1,110 reviews

Find tables, store and manage new tables and views, and write maintainable SQL code to answer business questions.

Reporting

4 hours

Course

DevOps Concepts

  • BasicSkill Level
  • 4.8+
  • 804 reviews

In this Introduction to DevOps, you’ll master the DevOps basics and learn the key concepts, tools, and techniques to improve productivity.

Software Development

4 hours

Course

Data Management Concepts

  • BasicSkill Level
  • 4.8+
  • 994 reviews

Master the key concepts of data management, from life cycle stages to security and governance.

Data Management

2 hours

Course

Responsible AI Data Management

  • IntermediateSkill Level
  • 4.7+
  • 961 reviews

Learn the theory behind responsibly managing your data for any AI project, from start to finish and beyond.

Artificial Intelligence

1 hour

Course

Introduction to MLflow

  • AdvancedSkill Level
  • 4.7+
  • 708 reviews

Learn how to use MLflow to simplify the complexities of building machine learning applications. Explore MLflow tracking, projects, models, and model registry.

Machine Learning

4 hours

Course

Data Science for Business

  • BasicSkill Level
  • 4.8+
  • 918 reviews

Learn about data science for managers and businesses and how to use data to strengthen your organization.

Data Literacy

2 hours

Course

Explainable AI in Python

  • IntermediateSkill Level
  • 4.8+
  • 1,033 reviews

Gain the essential skills using Scikit-learn, SHAP, and LIME to test and build transparent, trustworthy, and accountable AI systems.

Artificial Intelligence

4 hours

Course

Time Series Analysis in Python

  • IntermediateSkill Level
  • 4.8+
  • 132 reviews

In this four-hour course, you’ll learn the basics of analyzing time series data in Python.

Probability & Statistics

4 hours

Course

Introduction to GPTs

  • BasicSkill Level
  • 4.7+
  • 607 reviews

Learn how to use GPT tools responsibly and confidently. Discover how these tools work and techniques for writing prompts and evaluating outputs.

Artificial Intelligence

1 hour

Course

Cleaning Data in R

  • IntermediateSkill Level
  • 4.7+
  • 754 reviews

Learn to clean data as quickly and accurately as possible to help you move from raw data to awesome insights.

Data Preparation

4 hours

Course

Data Storytelling Case Study: Green Businesses

  • BasicSkill Level
  • 4.8+
  • 771 reviews

Practice data storytelling using real-world examples! Communicate complex insights effectively with a dataset of certified green businesses.

Data Literacy

1 hour

Course

Intermediate Docker

  • IntermediateSkill Level
  • 4.7+
  • 758 reviews

Master multi-stage builds, Docker networking tools, and Docker Compose for optimal containerized applications!

Software Development

4 hours

Course

Experimental Design in Python

  • IntermediateSkill Level
  • 4.7+
  • 1,732 reviews

Implement experimental design setups and perform robust statistical analyses to make precise and valid conclusions!

Probability & Statistics

4 hours

Course

Introduction to Google Workspace with Gemini

  • BasicSkill Level
  • 4.8+
  • 192 reviews

You learn about the key features of Gemini and how they can be used to improve productivity and efficiency in Google Workspace.

Artificial Intelligence

30 min

Course

Introduction to BigQuery

  • IntermediateSkill Level
  • 4.8+
  • 413 reviews

Unlock BigQuerys power: grasp its fundamentals, execute queries, and optimize workflows for efficient data analysis.

Data Engineering

4 hours

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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