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
Follow short videos led by expert instructors and then practice what you’ve learned with interactive exercises in your browser.
or
Course
Master data visualization in Julia. Learn how to make stunning plots while understanding when and how to use them.
Data Visualization
Course
Build modern data lakehouses on Google Cloud using BigQuery, Cloud Storage, Apache Iceberg, BigLake, federated queries, and data governance tools.
Cloud
Course
Predict employee turnover and design retention strategies.
Machine Learning
Course
Learn how to visualize big data in R using ggplot2 and trelliscopejs.
Data Visualization
Course
Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.
Probability & Statistics
Course
With Google Slides, you can create and present professional presentations for sales, projects, training modules, and much more.
Cloud
Course
Learn to upload, organize, share, and manage files and folders in Google Drive from any device.
Cloud
Course
Continue learning with purrr to create robust, clean, and easy to maintain iterative code.
Software Development
Course
Learn to create, format, and collaborate on documents in real time using Google Docs, stored securely in the cloud.
Cloud
Course
Turn a basic AI agent into a sophisticated assistant using advanced instructions, model selection, planning capabilities, and structured output.
Cloud
Course
Learn to create animated graphics and linked views entirely in R with plotly.
Data Visualization
Course
Learn to create and manage events, schedule meetings, share calendars, and use tasks and reminders to stay organized.
Cloud
Course
Learn to schedule, host, and manage video meetings in Google Meet, including screen sharing and collaboration tools.
Cloud
Course
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
Course
Learn to message individuals and groups, collaborate in spaces, and integrate Google Chat with other Workspace apps.
Cloud
Course
Learn to create and edit spreadsheets in Google Sheets, work with data, build formulas, and collaborate in real time.
Cloud
Course
Learn how to write scalable code for working with big data in R using the bigmemory and iotools packages.
Software Development
Course
Learn strategies for answering probability questions in R by solving a variety of probability puzzles.
Probability & Statistics
Course
Equip AI agents with tools for web search, code execution, database queries, and custom actions. Transform agents into capable assistants.
Cloud
Course
Master Apache Beam and Dataflow foundations including portability, Runner v2, Shuffle Service, Streaming Engine, IAM, quotas, and security.
Cloud
Course
Design and operate batch data pipelines on Google Cloud using Dataflow, Serverless Spark, Cloud Composer, and data validation techniques.
Cloud
Course
Deploy and manage Kubernetes workloads on GKE. Cover networking, deployments, jobs, persistent storage, and data management in production environments.
Cloud
Course
Build stateful AI agents that maintain context and remember user preferences using session state, memory management, and personalization.
Cloud
Course
Explore streaming data architectures on Google Cloud with Pub/Sub, Managed Kafka, Dataflow, and BigQuery for real-time data processing.
Cloud
Course
Use Gemini AI to boost your productivity in BigQuery. Explore data, accelerate code development, and discover visualization workflows.
Cloud
Course
Secure and monitor GKE production environments. Learn access control, logging, monitoring, CI/CD pipelines, and managed storage integration on Google Cloud.
Cloud
Course
Scale and manage multi-cluster GKE environments. Master fleets, Cloud Service Mesh, identity management, CI/CD at scale, and GKE Enterprise capabilities.
Cloud
Course
Build reliable Snowflake pipelines with DevOps and observability: Git, CI/CD, and Snowflake Trail monitoring.
Data Engineering
Course
Develop data pipelines with Apache Beam and Dataflow. Cover transforms, windowing, I/O connectors, schemas, state APIs, Beam SQL, and notebooks.
Cloud
Course
Learn to predict labels of nodes in networks using network learning and by extracting descriptive features from the network
Probability & Statistics
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.