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528 Courses

Course

Introduction to Amazon Bedrock

  • IntermediateSkill Level
  • 4.7+
  • 134 reviews

Learn to use Amazon Bedrock to access foundation AI models and build with AI - without managing complex infrastructure.

Artificial Intelligence

3 hours

Course

Baseball Data Visualization in Power BI

  • BasicSkill Level
  • 4.8+
  • 198 reviews

Discover how to analyze and visualize baseball data using Power BI. Create scatter plots, tornado charts, and gauges to bring baseball insights alive.

Data Visualization

1 hour

Course

Introduction to Julia

  • BasicSkill Level
  • 4.8+
  • 133 reviews

Julia is a new programming language designed to be the ideal language for scientific computing, machine learning, and data mining.

Software Development

4 hours

Course

Introduction to DataLab

  • BasicSkill Level
  • 4.8+
  • 118 reviews

Learn the fundamentals of using DataLab, an AI-powered data notebook for data analysis and exploration.

Reporting

1 hour

Course

Case Study: Data Analysis in Databricks

  • AdvancedSkill Level
  • 4.6+
  • 91 reviews

Learn to analyze Airbnb data using SQL in Databricks, create dashboards, and derive actionable insights.

Importing & Cleaning Data

3 hours

Course

Python for Spreadsheet Users

  • BasicSkill Level
  • 4.8+
  • 35 reviews

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

Software Development

4 hours

Course

Case Study: Inventory Analysis in Power BI

  • IntermediateSkill Level
  • 4.7+
  • 175 reviews

This Power BI case study follows a real-world business use case on tackling inventory analysis using DAX and visualizations.

Data Visualization

5 hours

Course

Communicating with Data in the Tidyverse

  • BasicSkill Level
  • 4.8+
  • 200 reviews

Leverage the power of tidyverse tools to create publication-quality graphics and custom-styled reports that communicate your results.

Data Visualization

4 hours

Course

Spoken Language Processing in Python

  • IntermediateSkill Level
  • 4.8+
  • 282 reviews

Learn how to load, transform, and transcribe speech from raw audio files in Python.

Data Manipulation

4 hours

Course

Dealing With Missing Data in R

  • BasicSkill Level
  • 4.7+
  • 142 reviews

Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.

Data Preparation

4 hours

Course

Statistical Thinking in Python (Part 2)

  • IntermediateSkill Level
  • 4.7+
  • 258 reviews

Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.

Probability & Statistics

4 hours

Course

Analyzing Survey Data in R

  • IntermediateSkill Level
  • 4.8+
  • 223 reviews

Learn survey design using common design structures followed by visualizing and analyzing survey results.

Probability & Statistics

4 hours

Course

Foundations of Probability in Python

  • IntermediateSkill Level
  • 4.8+
  • 205 reviews

Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.

Probability & Statistics

5 hours

Course

Survival Analysis in R

  • IntermediateSkill Level
  • 4.7+
  • 201 reviews

Learn to work with time-to-event data. The event may be death or finding a job after unemployment. Learn to estimate, visualize, and interpret survival models!

Probability & Statistics

4 hours

Course

Factor Analysis in R

  • AdvancedSkill Level
  • 4.7+
  • 161 reviews

Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.

Probability & Statistics

4 hours

Course

Introduction to Redshift

  • IntermediateSkill Level
  • 4.8+
  • 109 reviews

Master Amazon Redshifts SQL, data management, optimization, and security.

Data Engineering

4 hours

Course

Dealing with Missing Data in Python

  • IntermediateSkill Level
  • 4.8+
  • 187 reviews

Learn how to identify, analyze, remove and impute missing data in Python.

Data Manipulation

4 hours

Course

Cleaning Data in SQL Server Databases

  • IntermediateSkill Level
  • 4.8+
  • 189 reviews

Develop the skills you need to clean raw data and transform it into accurate insights.

Data Preparation

4 hours

Course

GARCH Models in Python

  • IntermediateSkill Level
  • 4.8+
  • 194 reviews

Learn about GARCH Models, how to implement them and calibrate them on financial data from stocks to foreign exchange.

Applied Finance

4 hours

Course

Python for R Users

  • IntermediateSkill Level
  • 4.7+
  • 84 reviews

This course is for R users who want to get up to speed with Python!

Software Development

5 hours

Course

Google: Deploy Your First Agent

  • IntermediateSkill Level
  • 4.9+
  • 23 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

Writing Efficient Code with pandas

  • IntermediateSkill Level
  • 4.7+
  • 161 reviews

Learn efficient techniques in pandas to optimize your Python code.

Software Development

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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