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711 Courses

Course

Bayesian Modeling with RJAGS

  • AdvancedSkill Level
  • 4.8+
  • 42 reviews

In this course, youll learn how to implement more advanced Bayesian models using RJAGS.

Probability & Statistics

4 hours

Course

Forecasting Product Demand in R

  • IntermediateSkill Level
  • 4.6+
  • 29 reviews

Learn how to identify important drivers of demand, look at seasonal effects, and predict demand for a hierarchy of products from a real world example.

Probability & Statistics

4 hours

Course

Optimizing R Code with Rcpp

  • IntermediateSkill Level
  • 4.9+
  • 12 reviews

Use C++ to dramatically boost the performance of your R code.

Software Development

4 hours

Course

Introduction to Anomaly Detection in R

  • IntermediateSkill Level
  • 4.8+
  • 25 reviews

Learn statistical tests for identifying outliers and how to use sophisticated anomaly scoring algorithms.

Probability & Statistics

4 hours

Course

Analyzing US Census Data in R

  • IntermediateSkill Level
  • 4.8+
  • 37 reviews

Learn to rapidly visualize and explore demographic data from the United States Census Bureau using tidyverse tools.

Exploratory Data Analysis

4 hours

Course

Machine Translation with Keras

  • AdvancedSkill Level
  • 4.8+
  • 46 reviews

Are you curious about the inner workings of the models that are behind products like Google Translate?

Artificial Intelligence

4 hours

Course

Introduction to Data Visualization with Julia

  • IntermediateSkill Level
  • 4.7+
  • 29 reviews

Master data visualization in Julia. Learn how to make stunning plots while understanding when and how to use them.

Data Visualization

4 hours

Course

Introduction to Data Engineering on Google Cloud

  • BasicSkill Level
  • 4.7+
  • 7 reviews

Learn the data engineering role on Google Cloud. Explore data sources, storage solutions, ETL/ELT architectures, BigQuery, Dataform, and Dataproc.

Cloud

3 hours 41 min

Course

Observability in Google Cloud

  • BasicSkill Level
  • 4.9+
  • 12 reviews

This course is all about application performance management tools, including Error Reporting, Cloud Trace, and Cloud Profiler.

Cloud

4 hours 30 min

Course

Google Workspace End User: Gmail

  • BasicSkill Level
  • 4.7+
  • 11 reviews

Learn to compose, send, and manage email in Gmail, organize messages with labels, and configure settings like filters and signatures.

Cloud

7 hours 15 min

Course

Logging and Monitoring in Google Cloud

  • BasicSkill Level
  • 4.9+
  • 11 reviews

This course, Logging and Monitoring in Google Cloud, covers the operations-focused components including Logging, Monitoring, and Service Monitoring.

Cloud

5 hours 15 min

Course

Data Manipulation in Julia

  • BasicSkill Level
  • 4.7+
  • 39 reviews

Master the essential skills of data manipulation in Julia. Learn how to inspect, transform, group, and visualize DataFrames using real-world datasets.

Data Manipulation

4 hours

Course

Mixture Models in R

  • IntermediateSkill Level
  • 4.8+
  • 20 reviews

Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.

Probability & Statistics

4 hours

Course

AI for Data Analysts

  • IntermediateSkill Level
  • 4.7+
  • 4 reviews

Use AI across every stage of your data analysis. Write sharper prompts, audit data quality, find insights worth chasing, and ship work you can trust.

Artificial Intelligence

4 hours

Course

Scalable Data Processing in R

  • AdvancedSkill Level
  • 4.6+
  • 22 reviews

Learn how to write scalable code for working with big data in R using the bigmemory and iotools packages.

Software Development

4 hours

Course

Google: Agent Fundamentals

  • BasicSkill Level
  • 4.8+
  • 9 reviews

Learn AI agent fundamentals — how they differ from LLMs, when to use them, and explore agent architecture, orchestration, and tools.

Cloud

1 hour

Course

Predicting CTR with Machine Learning in Python

  • IntermediateSkill Level
  • 4.8+
  • 17 reviews

Learn how to predict click-through rates on ads and implement basic machine learning models in Python so that you can see how to better optimize your ads.

Machine Learning

4 hours

Course

Probability Puzzles in R

  • BasicSkill Level
  • 4.8+
  • 65 reviews

Learn strategies for answering probability questions in R by solving a variety of probability puzzles.

Probability & Statistics

4 hours

Course

Google Workspace End User: Google Calendar

  • BasicSkill Level
  • 4.7+
  • 9 reviews

Learn to create and manage events, schedule meetings, share calendars, and use tasks and reminders to stay organized.

Cloud

4 hours 45 min

Course

Case Studies: Network Analysis in R

  • BasicSkill Level
  • 4.7+
  • 43 reviews

Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.

Probability & Statistics

4 hours

Course

Google Workspace End User: Google Meet

  • BasicSkill Level
  • 4.7+
  • 7 reviews

Learn to schedule, host, and manage video meetings in Google Meet, including screen sharing and collaboration tools.

Cloud

5 hours 30 min

Course

Google Workspace End User: Google Chat

  • BasicSkill Level
  • 4.6+
  • 10 reviews

Learn to message individuals and groups, collaborate in spaces, and integrate Google Chat with other Workspace apps.

Cloud

2 hours 30 min

Course

Predictive Analytics using Networked Data in R

  • IntermediateSkill Level
  • 4.8+
  • 31 reviews

Learn to predict labels of nodes in networks using network learning and by extracting descriptive features from the network

Probability & Statistics

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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