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.
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Playlist

Uploads from Imaad Mohamed Khan · Imaad Mohamed Khan · 26 of 34

1 Does AI know Fashion? - Mitali Sodhi - Mantissa Data Science Meetups
Does AI know Fashion? - Mitali Sodhi - Mantissa Data Science Meetups
Imaad Mohamed Khan
2 Mantissa Data Science Webinar - 1 with Santhosh Shetty
Mantissa Data Science Webinar - 1 with Santhosh Shetty
Imaad Mohamed Khan
3 Recommender Systems -  Imaad Mohamed Khan - Mantissa Data Science Meetups
Recommender Systems - Imaad Mohamed Khan - Mantissa Data Science Meetups
Imaad Mohamed Khan
4 Data Science is more than just Data Scientist - Different Roles in the field of Data Science
Data Science is more than just Data Scientist - Different Roles in the field of Data Science
Imaad Mohamed Khan
5 What topics to prepare for Data Science Interviews in 2020?
What topics to prepare for Data Science Interviews in 2020?
Imaad Mohamed Khan
6 Programming as a human activity
Programming as a human activity
Imaad Mohamed Khan
7 What are the languages or tools used by Data Scientists in their work?
What are the languages or tools used by Data Scientists in their work?
Imaad Mohamed Khan
8 Linear Regression From Scratch - Part 1
Linear Regression From Scratch - Part 1
Imaad Mohamed Khan
9 Linear Regression From Scratch - Part 2
Linear Regression From Scratch - Part 2
Imaad Mohamed Khan
10 Linear Regression From Scratch - Part 3
Linear Regression From Scratch - Part 3
Imaad Mohamed Khan
11 Journey into Data Science - Fireside chat with Adarsha and Karthikeyan
Journey into Data Science - Fireside chat with Adarsha and Karthikeyan
Imaad Mohamed Khan
12 Off the ground - Python in 5 Steps
Off the ground - Python in 5 Steps
Imaad Mohamed Khan
13 How LinkedIn uses Data Science to build your feed - LinkedIn Feed Algorithm Explained
How LinkedIn uses Data Science to build your feed - LinkedIn Feed Algorithm Explained
Imaad Mohamed Khan
14 Fireside chat with Eric Weber - Learnings in Data Science
Fireside chat with Eric Weber - Learnings in Data Science
Imaad Mohamed Khan
15 Part 2 - How LinkedIn uses Data Science to build your feed | LinkedIn Feed Algorithm Explained
Part 2 - How LinkedIn uses Data Science to build your feed | LinkedIn Feed Algorithm Explained
Imaad Mohamed Khan
16 Using Streamlit's Share Feature to easily deploy (and share) videos using Github
Using Streamlit's Share Feature to easily deploy (and share) videos using Github
Imaad Mohamed Khan
17 Airbnb Experiences Ranking Algorithm Explained - Part I
Airbnb Experiences Ranking Algorithm Explained - Part I
Imaad Mohamed Khan
18 Airbnb Experiences Ranking Algorithm Explained - Part II
Airbnb Experiences Ranking Algorithm Explained - Part II
Imaad Mohamed Khan
19 Airbnb Experiences Ranking Algorithm Explained - Part III
Airbnb Experiences Ranking Algorithm Explained - Part III
Imaad Mohamed Khan
20 Big Data, Hadoop and Machine Learning Explained using Dams
Big Data, Hadoop and Machine Learning Explained using Dams
Imaad Mohamed Khan
21 Fireside Chat with Hiromu Hota - Transitioning from Research to Industry
Fireside Chat with Hiromu Hota - Transitioning from Research to Industry
Imaad Mohamed Khan
22 Introduction to Anomaly Detection and One Class Classification
Introduction to Anomaly Detection and One Class Classification
Imaad Mohamed Khan
23 Reading and manipulating Google Sheets (GSheets) using Python libraries
Reading and manipulating Google Sheets (GSheets) using Python libraries
Imaad Mohamed Khan
24 Writing to Google Sheets (GSheets) using Python libraries
Writing to Google Sheets (GSheets) using Python libraries
Imaad Mohamed Khan
25 Fireside Chat with Mirza Rahim Baig - Business Problem Solving and Data Science Career Tips
Fireside Chat with Mirza Rahim Baig - Business Problem Solving and Data Science Career Tips
Imaad Mohamed Khan
Six types of Data Analysis you will do as a Data Scientist
Six types of Data Analysis you will do as a Data Scientist
Imaad Mohamed Khan
27 Automatic Speech Recognition (ASR) with Facebook AI's wav2vec 2.0 model using Huggingface
Automatic Speech Recognition (ASR) with Facebook AI's wav2vec 2.0 model using Huggingface
Imaad Mohamed Khan
28 9 Anti-patterns to avoid MLOps mistakes
9 Anti-patterns to avoid MLOps mistakes
Imaad Mohamed Khan
29 8 pitfalls to avoid while using Machine Learning Interpretation Techniques (SHAP, PDP, LIME, PFI)
8 pitfalls to avoid while using Machine Learning Interpretation Techniques (SHAP, PDP, LIME, PFI)
Imaad Mohamed Khan
30 Fireside Chat with Shadab Khan - AI in Healthcare and Data Science Career Tips
Fireside Chat with Shadab Khan - AI in Healthcare and Data Science Career Tips
Imaad Mohamed Khan
31 Features and Feature Engineering in Machine Learning - An Introduction
Features and Feature Engineering in Machine Learning - An Introduction
Imaad Mohamed Khan
32 Building your own AI text generation tool with aitextgen using GPT-2/GPT-3
Building your own AI text generation tool with aitextgen using GPT-2/GPT-3
Imaad Mohamed Khan
33 Organising Data Science projects using CRISP-DM
Organising Data Science projects using CRISP-DM
Imaad Mohamed Khan
34 Introduction to Prompt Engineering
Introduction to Prompt Engineering
Imaad Mohamed Khan

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.

Related AI Lessons

What are the real-world applications of data science?
Learn how data science is applied in real-world industries to drive better decisions and improve efficiency
Dev.to AI
Why Statistics is Important in Data Science
Statistics is the foundation of data science, enabling professionals to extract insights and make informed decisions from data, and its importance cannot be overstated
Medium · Data Science
Does This Have AI in It Yet?
You can build AI-friendly systems using existing data discipline skills, no new skills required
Medium · Data Science
Foundation First : Why Poor Data Quality Silently Destroys Enterprise AI, Analytics, and System…
Poor data quality can silently destroy enterprise AI, analytics, and systems, making it crucial to prioritize data foundation
Medium · AI
Up next
Spreadsheet Guy Meets the CFO: "Define How Much"
Digital Transformation with Eric Kimberling
Watch →