Course
Introduction to Generative AI in Snowflake
- IntermediateSkill Level
- 4.8+
- 359 reviews
Learn to build AI applications using Snowflake Cortexs built-in LLM functions for text analysis, generation, and multi-step workflows.
Artificial Intelligence
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 to build AI applications using Snowflake Cortexs built-in LLM functions for text analysis, generation, and multi-step workflows.
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Gain an overview of AI Agents. Discover how AI Agents use autonomous action and reasoning to solve complex problems.
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Learn how to create pivot tables and quickly organize thousands of data points with just a few clicks.
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Learn to start developing deep learning models with Keras.
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You will investigate a dataset from a fictitious company called Databel in Tableau, and need to figure out why customers are churning.
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Learn how to leverage statistical techniques using spreadsheets to more effectively work with and extract insights from your data.
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Learn how to create a PostgreSQL database and explore the structure, data types, and how to normalize databases.
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Data visualization is one of the most desired skills for data analysts. This course allows you to present your findings better using Tableau.
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Learn how to clean data with Apache Spark in Python.
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Learn techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.
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Sharpen your skills in Oracle SQL including SQL basics, aggregating, combining, and customizing data.
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Master AI for marketing to plan smarter campaigns, create quality content, and build custom AI agents.
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Learn how to transform raw data into clean, reliable models with dbt through hands-on, real-world exercises.
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Discover how Marketing Analysts use data to understand customers and drive business growth.
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Master the core operations of spaCy and train models for natural language processing. Extract information from unstructured data and match patterns.
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Build SQL skills by writing AI prompts that generate queries for sorting, grouping, filtering, and categorizing data.
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Learn how to manipulate data and create machine learning feature sets in Spark using SQL in Python.
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Understand the concept of reducing dimensionality in your data, and master the techniques to do so in Python.
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Use Seaborns sophisticated visualization tools to make beautiful, informative visualizations with ease.
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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.