Six types of Data Analysis you will do as a Data Scientist

Imaad Mohamed Khan · Intermediate ·📊 Data Analytics & Business Intelligence ·4y ago

Key Takeaways

The video discusses six types of data analysis that a Data Scientist may encounter, including descriptive, exploratory, inferential, predictive, causal, and mechanistic analysis, using statistical measures and data sampling to draw inferences and make predictions.

Full Transcript

six types of data analysis you will do as a data scientist one descriptive present a report of what has happened already it usually involves using basic measures of statistics to represent findings two exploratory open-ended exploration to check for patterns trends or relationships three inferential looking at a sample data set available to you and making inferences from it on the population in other words running experiments getting data and drawing inferences about the population for predictive predicting labels or things that may occur in the future based on signals from the past five causal identifying of a change in one factor leads to a change in other factors of the entire population and to what extent six mechanistic finding the underlying mechanism of the observed patterns trends or relationships trying to answer the how of the occurrence to be informed of more such videos please subscribe

Original Description

Data Scientists often have multiple hats to wear. One hat they wear sometimes is that of a Data Analyst. In this video, I briefly take you through the six types of data analysis you might encounter in your work as a Data Scientist. If you liked the video, please give it a thumbs up and don't forget to subscribe to the channel.
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This video teaches the six types of data analysis that Data Scientists use to extract insights from data, including descriptive, exploratory, inferential, predictive, causal, and mechanistic analysis. By understanding these types of analysis, Data Scientists can better inform business decisions and drive strategic initiatives. The video provides a brief overview of each type of analysis and how they are used in data science.

Key Takeaways
  1. Identify the type of data analysis needed
  2. Collect and clean the data
  3. Apply statistical measures and data sampling techniques
  4. Draw inferences and make predictions
  5. Communicate findings to stakeholders
💡 Understanding the different types of data analysis is crucial for Data Scientists to extract insights from data and inform business decisions.

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