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737 Courses

Course

Building AI Agents with Snowflake

  • IntermediateSkill Level
  • 4.8+
  • 8 reviews

Build autonomous Cortex Agents in Snowflake that query structured and unstructured data, then deploy and monitor them.

Artificial Intelligence

4 hours

Course

Feature Engineering in R

  • IntermediateSkill Level
  • 4.7+
  • 144 reviews

Learn the principles of feature engineering for machine learning models and how to implement them using the R tidymodels framework.

Machine Learning

4 hours

Course

Advanced NLP with spaCy

  • IntermediateSkill Level
  • 4.6+
  • 20 reviews

Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.

Machine Learning

5 hours

Course

Essential Google Cloud Infrastructure: Core Services

  • IntermediateSkill Level
  • 4.9+
  • 17 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

Getting Started with Google Kubernetes Engine

  • IntermediateSkill Level
  • 4.8+
  • 18 reviews

The goal of this course is to introduce the basics of Google Kubernetes Engine, or GKE, and how to get applications containerized and running in Google Cloud.

Cloud

5 hours 15 min

Course

Google DeepMind: Fine-Tune Your Model

  • IntermediateSkill Level
  • 4.8+
  • 15 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

Advanced Data Engineering with Snowflake

  • IntermediateSkill Level
  • 4.7+
  • 13 reviews

Build reliable Snowflake pipelines with DevOps and observability: Git, CI/CD, and Snowflake Trail monitoring.

Data Engineering

3 hours

Course

Google Cloud Fundamentals: Core Infrastructure

  • BasicSkill Level
  • 4.8+
  • 7 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

Google DeepMind: Accelerate Your Model

  • IntermediateSkill Level
  • 4.9+
  • 18 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

Course

Loan Amortization in Google Sheets

  • IntermediateSkill Level
  • 4.7+
  • 45 reviews

Learn how to build an amortization dashboard in Google Sheets with financial and conditional formulas.

Applied Finance

4 hours

Course

ChIP-seq with Bioconductor in R

  • IntermediateSkill Level
  • 4.7+
  • 48 reviews

Learn how to analyse and interpret ChIP-seq data with the help of Bioconductor using a human cancer dataset.

Probability & Statistics

4 hours

Course

Practicing Statistics Interview Questions in R

  • AdvancedSkill Level
  • 4.7+
  • 21 reviews

In this course, youll prepare for the most frequently covered statistical topics from distributions to hypothesis testing, regression models, and much more.

Probability & Statistics

4 hours

Course

Bond Valuation and Analysis in Python

  • BasicSkill Level
  • 4.8+
  • 69 reviews

Learn how bonds work and how to price them and assess some of their risks using the numpy and numpy-financial packages.

Applied Finance

4 hours

Course

Google: Human-Centered AI

  • BasicSkill Level
  • 4.9+
  • 20 reviews

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

Cloud

10 min

Course

Joining Data with data.table in R

  • IntermediateSkill Level
  • 4.8+
  • 71 reviews

This course will show you how to combine and merge datasets with data.table.

Data Manipulation

4 hours

Course

Google: Deploy Your First Agent

  • BasicSkill Level
  • 5
  • 11 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

Bayesian Regression Modeling with rstanarm

  • AdvancedSkill Level
  • 4.8+
  • 65 reviews

Learn how to leverage Bayesian estimation methods to make better inferences about linear regression models.

Probability & Statistics

4 hours

Course

Building Data Pipelines with Airflow

  • AdvancedSkill Level
  • 4.7+
  • 4 reviews

Author Dags with the TaskFlow API, asset-based scheduling, and deferrable sensors, and run an end-to-end SQL ETL pipeline with quality checks.

Data Engineering

4 hours

Course

Analyzing Survey Data in Python

  • IntermediateSkill Level
  • 4.6+
  • 51 reviews

Learn how to analyze survey data with Python and discover when it is appropriate to apply statistical tools that are descriptive and inferential in nature.

Probability & Statistics

4 hours

Course

Analyzing Social Media Data in R

  • IntermediateSkill Level
  • 4.8+
  • 88 reviews

Extract and visualize Twitter data, perform sentiment and network analysis, and map the geolocation of your tweets.

Data Manipulation

4 hours

Course

Building Dashboards with flexdashboard

  • IntermediateSkill Level
  • 4.7+
  • 49 reviews

In this course youll learn how to create static and interactive dashboards using flexdashboard and shiny.

Reporting

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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