Statistics for Data Science - Module 1 | Measures of Central Tendency & Dispersion Tutorial

Welcome to Module 1 of our Statistics for Data Science Playlist! In this video, we dive deep into the foundational topic of statistics for data science. We'll explore descriptive statistics, including Measures of Central Tendency & Variation, equipping you with the essential tools to understand and analyse data effectively. Join our FREE masterclasses that are taken by Leading Industry Experts: https://www.scaler.com/events?utm_sou...

Whether you're just starting your data science journey or looking to brush up on these fundamental concepts, this module is your stepping stone towards mastering the art of extracting valuable insights from data. Don't forget to like, subscribe, and hit the notification bell to stay updated as we progress through this comprehensive playlist!

Statistics Notes - https://drive.google.com/drive/folder...

Playlist link:    • Statistics and Probability Course for...  

What is Statistics?
Statistics is the science of collecting, organizing, interpreting, analyzing, and presenting data. It involves the study of data patterns, distributions, and relationships to draw meaningful conclusions and make informed decisions. In essence, statistics provides the tools and techniques to transform raw data into valuable insights.

How important is Statistics for Data Science?
Statistics is fundamental to data science as it underpins the entire data analysis process. It helps in understanding data distributions, making data-driven decisions, and evaluating the effectiveness of models. In data science, statistics is not just important; it's indispensable for extracting meaningful insights from data.

What is Data Science?
Data science is an interdisciplinary field that involves the use of various techniques, algorithms, processes, and systems to extract valuable insights, knowledge, and predictions from structured and unstructured data. It combines elements of statistics, mathematics, computer science, domain expertise, and data engineering to analyze, interpret, and solve complex problems, make data-driven decisions, and drive innovation across various industries.

Topics Covered:
0:00 - Introduction
0:13 - Agenda
0:27 - What is Statistics?
0:59 - Descriptive, Inferential and Hypothesis Statistics
18:48 - How to read & understand Data
23:36 - Variables (Columns in Data)
36:09 - Descriptive Statistics
37:02 - Measures of Central Tendency (Mode, Mean, Median)
58:20 - Measures of Variation (Range, Variance, Standard Deviation)
1:09:20 - Variance
1:26:33 - Coefficient of Variance

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