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306 Courses

Course

Parallel Programming with Dask in Python

  • IntermediateSkill Level
  • 4.7+
  • 67 reviews

Learn how to use Python parallel programming with Dask to upscale your workflows and efficiently handle big data.

Software Development

4 hours

Course

Introduction to Statistics in Python

  • IntermediateSkill Level
  • 4.7+
  • 9,151 reviews

Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.

Probability & Statistics

4 hours

Course

Case Study: Sales Analytics with Databricks Genie

  • BasicSkill Level
  • 4.9+
  • 50 reviews

Build a Databricks Genie space end-to-end: descriptions, synonyms, instructions, table relationships, example queries, monitoring, and benchmarks.

Data Engineering

2 hours

Course

Data Visualization in KNIME

  • BasicSkill Level
  • 4.8+
  • 217 reviews

Learn to create compelling data visualizations with KNIME, covering charts, components, and dashboards.

Data Visualization

2 hours

Course

Data Manipulation with dplyr

  • BasicSkill Level
  • 4.8+
  • 802 reviews

Build Tidyverse skills by learning how to transform and manipulate data with dplyr.

Data Manipulation

4 hours

Course

Cleaning Data in R

  • IntermediateSkill Level
  • 4.7+
  • 881 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 Strategy

  • BasicSkill Level
  • 4.7+
  • 1,847 reviews

Master strategic data management for business excellence.

Data Management

1 hour

Course

Data Fluency

  • BasicSkill Level
  • 4.8+
  • 370 reviews

Master data fluency! Learn skills for individuals and organizations, understand behaviors, and build a data-fluent culture.

Data Literacy

2 hours

Course

Intermediate Importing Data in R

  • IntermediateSkill Level
  • 4.8+
  • 306 reviews

Parse data in any format. Whether its flat files, statistical software, databases, or data right from the web.

Data Preparation

3 hours

Course

Working with Dates and Times in R

  • IntermediateSkill Level
  • 4.8+
  • 102 reviews

Learn the essentials of parsing, manipulating and computing with dates and times in R.

Software Development

4 hours

Course

Visualizing Geospatial Data in Python

  • IntermediateSkill Level
  • 4.7+
  • 376 reviews

Learn how to make attractive visualizations of geospatial data in Python using the geopandas package and folium maps.

Data Visualization

4 hours

Course

Inference for Categorical Data in R

  • AdvancedSkill Level
  • 4.8+
  • 122 reviews

In this course youll learn how to leverage statistical techniques for working with categorical data.

Probability & Statistics

4 hours

Course

Dashboard Design Concepts

  • BasicSkill Level
  • 4.8+
  • 1,254 reviews

Learn the skills needed to create impactful dashboards. Understand dashboard design fundamentals, visual analytics components, and dashboard types.

Data Visualization

2 hours

Course

NoSQL Concepts

  • IntermediateSkill Level
  • 4.8+
  • 607 reviews

In this conceptual course (no coding required), you will learn about the four major NoSQL databases and popular engines.

Data Engineering

2 hours

Course

Intermediate Python for Finance

  • IntermediateSkill Level
  • 4.7+
  • 1,076 reviews

Build on top of your Python skills for Finance, by learning how to use datetime, if-statements, DataFrames, and more.

Applied Finance

4 hours

Course

Hypothesis Testing in R

  • IntermediateSkill Level
  • 4.7+
  • 1,077 reviews

Learn how and when to use hypothesis testing in R, including t-tests, proportion tests, and chi-square tests.

Probability & Statistics

4 hours

Course

Regular Expressions in Python

  • BasicSkill Level
  • 4.7+
  • 226 reviews

Learn about string manipulation and become a master at using regular expressions.

Software Development

4 hours

Course

Credit Risk Modeling in Python

  • IntermediateSkill Level
  • 4.7+
  • 319 reviews

Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.

Applied Finance

4 hours

Course

Improving Query Performance in SQL Server

  • IntermediateSkill Level
  • 4.8+
  • 499 reviews

In this course, students will learn to write queries that are both efficient and easy to read and understand.

Software Development

4 hours

Course

AI Agents with Hugging Face smolagents

  • AdvancedSkill Level
  • 4.8+
  • 288 reviews

Learn how to build intelligent agents that reason, act, and solve real-world tasks using Python.

Artificial Intelligence

3 hours

Course

Life Insurance Products Valuation in R

  • BasicSkill Level
  • 4.8+
  • 55 reviews

Learn the basics of cash flow valuation, work with human mortality data and build life insurance products in R.

Applied Finance

4 hours

Course

Python for Spreadsheet Users

  • BasicSkill Level
  • 4.9+
  • 40 reviews

Use your knowledge of common spreadsheet functions and techniques to explore Python!

Software Development

4 hours

Course

Fraud Detection in Python

  • IntermediateSkill Level
  • 4.7+
  • 205 reviews

Learn how to detect fraud using Python.

Machine Learning

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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