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778 Courses

Course

User-Oriented Design in Power BI

  • IntermediateSkill Level
  • 4.7+
  • 468 reviews

Learn how to design Power BI visualizations and reports with users in mind.

Data Visualization

2 hours

Course

Data Types and Functions in Snowflake

  • IntermediateSkill Level
  • 4.8+
  • 547 reviews

Learn Snowflake data types and functions to manipulate text, numbers, and dates while building custom functions and pivot tables.

Data Manipulation

3 hours

Course

Introduction to KNIME

  • BasicSkill Level
  • 4.8+
  • 544 reviews

Learn to use the KNIME Analytics Platform for data access, cleaning, and analysis with a no-code/low-code approach.

Data Preparation

3 hours

Course

Optimizing Code in Java

  • AdvancedSkill Level
  • 4.8+
  • 248 reviews

Learn key techniques to optimize Java performance, from algorithm efficiency to JVM tuning and multithreading.

Software Development

3 hours

Course

Intermediate SQL Querying with AI

  • BasicSkill Level
  • 4.9+
  • 261 reviews

Build SQL skills by writing AI prompts that generate queries for sorting, grouping, filtering, and categorizing data.

Data Manipulation

3 hours

Course

Dimensionality Reduction in Python

  • IntermediateSkill Level
  • 4.8+
  • 896 reviews

Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.

Machine Learning

4 hours

Course

Monitoring Machine Learning Concepts

  • IntermediateSkill Level
  • 4.8+
  • 521 reviews

Learn about the challenges of monitoring machine learning models in production, including data and concept drift, and methods to address model degradation.

Machine Learning

2 hours

Course

Introduction to Bioconductor in R

  • IntermediateSkill Level
  • 4.7+
  • 124 reviews

Learn to use essential Bioconductor packages for bioinformatics using datasets from viruses, fungi, humans, and plants!

Probability & Statistics

4 hours

Course

Linear Algebra for Data Science in R

  • IntermediateSkill Level
  • 4.7+
  • 141 reviews

This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.

Probability & Statistics

4 hours

Course

Introduction to Databases in Python

  • IntermediateSkill Level
  • 4.8+
  • 286 reviews

In this course, youll learn the basics of relational databases and how to interact with them.

Data Manipulation

4 hours

Course

Introduction to R for Finance

  • BasicSkill Level
  • 4.7+
  • 101 reviews

Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.

Applied Finance

4 hours

Course

Data Manipulation in Alteryx

  • BasicSkill Level
  • 4.8+
  • 667 reviews

Orchestrate data using unions, joins, parsing, and performance optimization in Alteryx.

Data Manipulation

3 hours

Course

NoSQL Concepts

  • IntermediateSkill Level
  • 4.8+
  • 548 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

Gen AI Apps: Transform Your Work

  • BasicSkill Level
  • 4.8+
  • 175 reviews

This course introduces Google’s gen AI applications, such as Google Workspace with Gemini and NotebookLM.

Cloud

1 hour 15 min

Course

Hyperparameter Tuning in Python

  • IntermediateSkill Level
  • 4.8+
  • 850 reviews

Learn techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.

Machine Learning

4 hours

Course

Building a Go-To-Market Strategy

  • BasicSkill Level
  • 4.7+
  • 417 reviews

Create a go-to-market strategy with generative AI: target industries, generate leads, and optimize website keywords.

Artificial Intelligence

1 hour

Course

Math for Finance Professionals

  • BasicSkill Level
  • 4.8+
  • 311 reviews

Learn essential finance math skills with practical Excel exercises and real-world examples.

Applied Finance

3 hours

Course

Window Functions in Snowflake

  • IntermediateSkill Level
  • 4.8+
  • 544 reviews

Discover Snowflake window functions to solve complex data problems with rankings, partitions, and rolling calculations.

Data Manipulation

3 hours

Course

Fully Automated MLOps

  • IntermediateSkill Level
  • 4.8+
  • 368 reviews

Learn about MLOps architecture, CI/CD/CM/CT techniques, and automation patterns to deploy ML systems that can deliver value over time.

Machine Learning

4 hours

Course

Introduction to Testing in Java

  • AdvancedSkill Level
  • 4.8+
  • 205 reviews

Learn how to write effective tests in Java using JUnit and Mockito to build robust, reliable applications with confidence.

Software Development

3 hours

Course

Reshaping Data with pandas

  • IntermediateSkill Level
  • 4.7+
  • 831 reviews

Reshape DataFrames from a wide to long format, stack and unstack rows and columns, and wrangle multi-index DataFrames.

Data Manipulation

4 hours

Course

Understanding Digital Transformation

  • BasicSkill Level
  • 4.8+
  • 587 reviews

Dive into the world of digital transformation and equip yourself to be an agent of change in a rapidly evolving digital landscape.

Data Literacy

1 hour

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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