Course
NoSQL Concepts
- IntermediateSkill Level
- 4.8+
- 561 reviews
In this conceptual course (no coding required), you will learn about the four major NoSQL databases and popular engines.
Data Engineering
Follow short videos led by expert instructors and then practice what you’ve learned with interactive exercises in your browser.
or
Course
In this conceptual course (no coding required), you will learn about the four major NoSQL databases and popular engines.
Data Engineering
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Solidify your decision science skills by designing data-informed frameworks and implementing efficient solutions.
Data Literacy
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Create multi-modal systems using OpenAIs text and audio models, including an end-to-end customer support chatbot!
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Expand your Google Sheets vocabulary by diving deeper into data types, including numeric data, logical data, and missing data.
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Learn how to build interactive and insight-rich dashboards with Dash and Plotly.
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Learn to build and customize Sigma charts to tell clear, compelling data stories—no coding required.
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Master data fluency! Learn skills for individuals and organizations, understand behaviors, and build a data-fluent culture.
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Learn to start developing deep learning models with Keras.
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Learn about the difference between batching and streaming, scaling streaming systems, and real-world applications.
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Learn about AWS Boto and harnessing cloud technology to optimize your data workflow.
Cloud
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Learn how to store, secure, scale, and process data in Azure using Blob Storage, Cosmos DB, queues, and event-driven services.
Cloud
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Build cloud apps on AWS with API Gateway, Lambda, SQS, SNS, EventBridge, and Kinesis. Master serverless and event-driven patterns for the DVA-C02 exam.
Cloud
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Learn key financial concepts such as capital investment, WACC, and shareholder value.
Applied Finance
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Explore association rules in market basket analysis with Python by bookstore data and creating movie recommendations.
Machine Learning
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Ensure high data quality in data science and data engineering workflows with Pythons Great Expectations library.
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Build conversational AI apps that answer questions from your data with Cortex Search and Cortex Analyst on Snowflake.
Artificial Intelligence
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Learn powerful command-line skills to download, process, and transform data, including machine learning pipeline.
Data Manipulation
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Learn to create, secure, and manage APIs with Azure API Management through hands-on practice.
Cloud
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Using Python and NumPy, learn the most fundamental financial concepts.
Applied Finance
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This course will show you how to integrate spatial data into your Python Data Science workflow.
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Get to know the Google Cloud Platform (GCP) with this course on storage, data handling, and business modernization using GCP.
Cloud
Cloud
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Map agent types to your KPIs and explore use cases that solve problems, learn how Gemini Enterprise empowers you to build and orchestrate the right agents.
Cloud
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Practice Power BI with our healthcare case study. Analyze data, uncover efficiency insights, and build a dashboard.
Data Visualization
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Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.
Probability & Statistics
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Test a chatbot that matches customers with ideal skincare products using your prompting skills for personalized results.
Artificial Intelligence
Course
Combine text, images, audio, and video with the latest AI models from Hugging Face, and generate new images and videos!
Artificial Intelligence
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Analyze market dynamics and craft a strategic entry plan for an EV manufacturer using generative AI.
Artificial Intelligence
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Help a fictional company in this interactive Power BI case study. You’ll use Power Query, DAX, and dashboards to identify the most in-demand data jobs!
Data Manipulation
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.