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772 Courses

Course

Introduction to Spark with sparklyr in R

  • IntermediateSkill Level
  • 4.7+
  • 82 reviews

Learn how to run big data analysis using Spark and the sparklyr package in R, and explore Spark MLIb in just 4 hours.

Data Engineering

4 hours

Course

Data Manipulation with data.table in R

  • BasicSkill Level
  • 4.6+
  • 21 reviews

Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.

Data Manipulation

4 hours

Course

Case Study: Financial Analysis in KNIME

  • IntermediateSkill Level
  • 4.8+
  • 117 reviews

Apply financial analysis in KNIME with real-world data, enhancing data preparation and workflow skills.

Applied Finance

3 hours

Course

Time Series Analysis in PostgreSQL

  • IntermediateSkill Level
  • 4.8+
  • 93 reviews

Learn how to use PostgreSQL to handle time series analysis effectively and apply these techniques to real-world data.

Data Manipulation

4 hours

Course

Developing R Packages

  • IntermediateSkill Level
  • 4.7+
  • 150 reviews

Learn to develop R packages and boost your coding skills. Discover package creation benefits, practice with dev tools, and create a unit conversion package.

Software Development

4 hours

Course

Data Visualization in KNIME

  • BasicSkill Level
  • 4.8+
  • 199 reviews

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

Data Visualization

2 hours

Course

HR Analytics: Exploring Employee Data in R

  • IntermediateSkill Level
  • 4.8+
  • 37 reviews

Learn how to manipulate, visualize, and perform statistical tests through a series of HR analytics case studies.

Exploratory Data Analysis

5 hours

Course

Data Manipulation in KNIME

  • BasicSkill Level
  • 4.8+
  • 247 reviews

Automate data manipulation with KNIME, mastering merging, aggregation, database workflows, and advanced file handling.

Data Manipulation

3 hours

Course

Intermediate Power Automate

  • IntermediateSkill Level
  • 4.8+
  • 7 reviews

Build reliable Power Automate cloud flows with triggers, branching, approvals, error handling, and production handover.

Artificial Intelligence

3 hours

Course

Sentiment Analysis in R

  • IntermediateSkill Level
  • 4.7+
  • 95 reviews

Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.

Machine Learning

4 hours

Course

Developing applications on AWS

  • IntermediateSkill Level
  • 5
  • 5 reviews

Build cloud apps on AWS with API Gateway, Lambda, SQS, SNS, EventBridge, and Kinesis. Master serverless and event-driven patterns for the DVA-C02 exam.

Cloud

3 hours

Course

Google: Deploy Your First Agent

  • IntermediateSkill Level
  • 4.9+
  • 16 reviews

Deploy ADK agents to production using Vertex AI Agent Engine and Cloud Run. Add persistent cross-session memory with Memory Bank.

Cloud

1 hour

Course

Inference for Categorical Data in R

  • AdvancedSkill Level
  • 4.8+
  • 110 reviews

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

Probability & Statistics

4 hours

Course

Google: Human-Centered AI

  • BasicSkill Level
  • 4.9+
  • 34 reviews

Learn human-centric AI orchestration. Distinguish between augmentation and automation, and balance machine efficiency with human intuition.

Cloud

10 min

Course

Google Cloud Fundamentals: Core Infrastructure

  • BasicSkill Level
  • 4.8+
  • 9 reviews

Learn Google Cloud essentials including computing, storage, networking, and resource management through videos and hands-on labs in this foundational course.

Cloud

5 hours

Course

Python for MATLAB Users

  • BasicSkill Level
  • 4.8+
  • 30 reviews

Transition from MATLAB by learning some fundamental Python concepts, and diving into the NumPy and Matplotlib packages.

Software Development

4 hours

Course

Google DeepMind: Fine-Tune Your Model

  • IntermediateSkill Level
  • 4.7+
  • 17 reviews

Unleash the power of language models with fine-tuning. In this course, you will learn how to adjust a pre-trained model to a specific task.

Cloud

8 hours

Course

Case Study: Supply Chain Analytics in Tableau

  • IntermediateSkill Level
  • 4.7+
  • 71 reviews

Dive into our Tableau case study on supply chain analytics. Tackle shipment, inventory management, and dashboard creation to drive business improvements.

Data Visualization

4 hours

Course

Conditional Formatting in Google Sheets

  • BasicSkill Level
  • 4.8+
  • 99 reviews

Learn how to use conditional formatting with your data through built-in options and by creating custom formulas.

Data Manipulation

2 hours

Course

Support Vector Machines in R

  • IntermediateSkill Level
  • 4.8+
  • 86 reviews

This course will introduce the support vector machine (SVM) using an intuitive, visual approach.

Machine Learning

4 hours

Course

Essential Google Cloud Infrastructure: Core Services

  • IntermediateSkill Level
  • 4.9+
  • 23 reviews

This course introduces the comprehensive and flexible infrastructure and platform services provided by Google Cloud with a focus on Core Services.

Cloud

8 hours 15 min

Course

Google DeepMind: Accelerate Your Model

  • IntermediateSkill Level
  • 4.9+
  • 20 reviews

Train more powerful models with a single GPU, learn how hardware can speed up model training and the key considerations when training models on a GPU.

Cloud

7 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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