Prepare Data For Perspective And Predictive Modelling Like A Data Engineer | Intellipaat

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Published on May 25, 2022 8:30:13 AM ● Video Link: https://www.youtube.com/watch?v=5zhrZan6J0s

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Guide

Duration: 39:43

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🔵 Intellipaat Data Engineering course: https://intellipaat.com/pgp-data-engineering-mit-job-guarantee/
About the Webinar

Big data gets a lot of buzz in the business world. It’s true that data analytics can give you deep, useful insights about your business and its customers, but to benefit from those insights, you have to know how to interpret the data and apply it to your business strategy. Both predictive and prescriptive analytics inform your business strategies based on collected data. But the major difference between predictive analytics and prescriptive analytics is that the former forecasts potential future outcomes, while the latter helps you draw up specific recommendations. In this webinar, you will learn how to prepare data as a single source of truth so that it can be used for both these analytics and throughout the organization.

Agenda
1. What is data analytics?
2. Difference between predictive and prescriptive analytics? Which to use where?
3. Data Analytics Lifecycle
4. Basics of predictive modelling - Training/Testing - Regression/Classification
5. Kaggle Case-Study [Hands-On]

#PredictiveModellingInDataEngineering #DataEngineer #DataEngineeringProjects #DataEngineerCareerGuide #Intellipaat

🔵 Why Data Engineering is important?
Data engineering helps make data more useful and accessible for consumers of data. To do so, Data engineering must source, transform and analyze data from each system. For example, data stored in a relational database is managed as tables, like a Microsoft Excel spreadsheet. Each table contains many rows, and all rows have the same columns. A given piece of information, such as a customer order, may be stored across dozens of tables.

🔵 Why should you opt for a Data Engineering career?
Data engineers must have specialized skills in creating software solutions around data. At the same time, it’s perhaps unrealistically expected that Data Engineers will be familiar with a breadth of tools and technologies – anywhere from 10 to 30 of them. And these tools are constantly changing.

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