Course
Building Dashboards with Dash and Plotly
- IntermediateSkill Level
- 4.8+
- 311 reviews
Learn how to build interactive and insight-rich dashboards with Dash and Plotly.
Data Visualization
Follow short videos led by expert instructors and then practice what you’ve learned with interactive exercises in your browser.
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Course
Learn how to build interactive and insight-rich dashboards with Dash and Plotly.
Data Visualization
Course
In this course youll learn about basic experimental design, a crucial part of any data analysis.
Probability & Statistics
Course
Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.
Probability & Statistics
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Deploy, secure, and operate apps on AWS with Lambda, API Gateway, Cognito, IAM, CloudWatch, and X-Ray. Hands-on prep for the DVA-C02 exam.
Cloud
Course
Interact with a customized GPT and use your prompting skills to plan and open your restaurant.
Artificial Intelligence
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Manage the complexity in your code using object-oriented programming with the S3 and R6 systems.
Software Development
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Build a Databricks Genie space end-to-end: descriptions, synonyms, instructions, table relationships, example queries, monitoring, and benchmarks.
Data Engineering
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Master Amazon Redshifts SQL, data management, optimization, and security.
Data Engineering
Course
Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.
Probability & Statistics
Course
Julia is a new programming language designed to be the ideal language for scientific computing, machine learning, and data mining.
Software Development
Course
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
Course
Learn how to ensure clean data entry and build dynamic dashboards to display your marketing data.
Reporting
Course
This Power BI case study follows a real-world business use case where you will apply the concepts of ETL and visualization.
Data Visualization
Course
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
Machine Learning
Course
Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.
Machine Learning
Course
Develop the skills you need to clean raw data and transform it into accurate insights.
Data Preparation
Course
This course is for R users who want to get up to speed with Python!
Software Development
Course
Build CI/CD pipelines with AWS CodePipeline, CodeBuild, and CodeDeploy. Automate blue/green and canary releases, and define infrastructure with CloudFormation.
Cloud
Course
Use your knowledge of common spreadsheet functions and techniques to explore Python!
Software Development
Course
Build reliable Snowflake pipelines with DevOps and observability: Git, CI/CD, and Snowflake Trail monitoring.
Data Engineering
Course
Learn how to detect fraud using Python.
Machine Learning
Course
Learn the core techniques necessary to extract meaningful insights from time series data.
Probability & Statistics
Course
Build real-world applications with Python—practice using OOP and software engineering principles to write clean and maintainable code.
Software Development
Course
Build, deploy, and optimize serverless apps with AWS Lambda. Master event processing, error handling, concurrency, and safe deployments in a live AWS Console.
Cloud
Course
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
Machine Learning
Course
Learn the essentials of parsing, manipulating and computing with dates and times in R.
Software Development
Course
Trust and Security with Google Cloud
Cloud
Course
Staring at your inbound leads and unsure where to start? Use Claude Cowork to score your leads and build a high-quality, prioritized shortlist.
Artificial Intelligence
Course
Connect data sources to your app to build a search and analysis engine. Master capabilities like deep research agents, ideation, and NotebookLM for analysis.
Cloud
Course
Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.
Machine Learning
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.
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.
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.
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.
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.
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.
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.
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.
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.
Make progress on the go with our mobile courses and daily 5-minute coding challenges.