EDA / Descriptive Statistics using Python (Part - 1)

Name: EDA / Descriptive Statistics using Python (Part - 1)
Rating: 4.6 (62 reviews)

Data Science - EDA/Descriptive statistics(Part - 1)

Created byAISPRY TUTOR

Last updated 2/2024

English

What you'll learn

Students will get an elaborate understanding of exploratory data analysis, also known as descriptive statistics.
We dig deep into the first-moment business decision, aka measures of central tendency.
We gain an understanding of second-moment business decisions, aka measures of dispersion.
We further understand the importance of third and fourth-moment business decisions, aka skewness.
Finally, we also look at the multitude of graphical representations like univariate, bivariate, and multivariate plots.

Course content

8 sections • 47 lectures • 9h 26m total length

Introduction about Tutor3:14
Agenda and stages of analytics1:02
What is diagnoistic Analytics1:21
What is Predicative Analytics1:57
Explore predictive analytics by forecasting future outcomes using current data, and assess the validity of predictions amid changing conditions and varying time horizons.
What is CRISP - ML(Q)3:08
Let's take this Quiz

Business Understanding - Define the Scope of Application18:44
Define the scope of application, set business objectives and constraints, and use input and output data to build a survival analytics model that reduces defaulters and increases profits.
Business Understanding - Define Business Criteria8:13
Business Understanding - Use Case9:59
Explore business understanding and use cases, from fraud detection in credit card transactions to precision farming with drones, balancing fraud minimization, customer convenience, and cost constraints.
Quiz Questions

Agenda Data Understanding0:49
Explore data understanding by identifying data types, scales of measurement, and key terms, and compare primary and secondary data collection techniques.
Introduction to Data Understanding6:18
Explore data understanding by measuring data, analyzing it to build models, and using what-if analysis to optimize sales under constraints for informed management decisions.
Data Types - Continuous Data Vs Discrete Data11:18
Pratical Data Understanding using Real time Experiences11:15
Explore practical data understanding by comparing real-world examples to describe nominal, ordinal, interval, and ratio data, including absolute zero and subjective versus objective interpretations.
Scale Of Measurement3:34
Quantitative vs Qualitative5:04
Differentiate quantitative data from qualitative data by showing numbers and continuous or count data. Identify qualitative data as descriptive or categorical and note its role in decision making.
Structured Vs Unstructured Data13:04
Quiz

What Is Data Collection ?4:12
Understanding Secondary Data Sources13:31
Undersatanding Primary Data Sources22:15
Understanding Data Collection Using Survey6:46
Understand how to translate business reality into survey data collection, identify root causes, and design construct-based questions to gauge training enrollment and price elasticity of demand.
Understanding Data Collection Using DoE7:15
Apply design of experiments to food promotions by testing expiry timing and distance; same-day expiry with near customers yields higher redemptions, revealing how expiry and distance affect consumer response.
Understanding Possible Errors in Data Collection Stage16:21
Understanding Bias and Fairness5:17
Understand bias and fairness in data science, avoiding sensitive variables and biased data collection. Emphasize business understanding, proper data collection, and disciplined use of algorithms.
Quiz

Recap of Preliminaries Concepts2:09
Understanding Normal Distribution15:36
Understanding Standard Normal Distribution & what is Z Scores28:16
Explore the standard normal distribution, z-scores, and Six Sigma concepts; relate mean and standard deviation to a symmetric bell curve, and learn z-score standardization with mu and sigma.
Understanding Measures of Central Tendency (First Moment Business Decision )26:36
Understanding Measures of Dispersion (Second Moment Business Decision)10:46
Understanding Box Plot (Diff B/w Percentile and Quantile and Quartile)6:09
Understanding Graphical Techniques-Q-Q-Plot8:34
Understanding about Bivariate Scatter Plot35:36
Quiz

Understanding 1st & 2nd Moment Business Decision Using Python24:36
Understanding 3rd Moment Business Decision Using Python21:05
Understanding 4th Moment Business Decision Using Python20:42
Explore the fourth moment and kurtosis using Python, linking excess kurtosis to normality, skewness, and business insights through retail and e-commerce examples.
Understanding Univariate (Bar plot & Histogram ) Using Python14:22
Explore univariate data with bar plots and histograms in Python to convert raw numbers into meaningful insights, using bins, normal distribution checks, and simple examples like GMAT scores.
Understanding Univariate Plot Using Python34:37
Understanding Univariate Box plot Using Python12:21
Understanding Univariate Q-Q-Plot Using Python8:12
Understanding Bivariate Scatter Plot Using Python32:18
Explore creating a bivariate scatter plot in Python to relate waist circumference and adipose tissue, and interpret the correlation coefficient value of 0.81 and covariance.
Quiz

Requirements

It is advised for learners to have a prior understanding of CRISP-ML(Q) Methodology.
Having an understanding of other steps in the data preparation section of CRISP-ML(Q).
Understanding the involvement of Python Programming in EDA.

Description

This program will help aspirants getting into the field of data science understand the concepts of project management methodology. This will be a structured approach in handling data science projects. Importance of understanding business problem alongside understanding the objectives, constraints and defining success criteria will be learnt. Success criteria will include Business, ML as well as Economic aspects. Learn about the first document which gets created on any project which is Project Charter. The various data types and the four measures of data will be explained alongside data collection mechanisms so that appropriate data is obtained for further analysis. Primary data collection techniques including surveys as well as experiments will be explained in detail. Exploratory Data Analysis or Descriptive Analytics will be explained with focus on all the ‘4’ moments of business moments as well as graphical representations, which also includes univariate, bivariate and multivariate plots. Box plots, Histograms, Scatter plots and Q-Q plots will be explained. Prime focus will be in understanding the data preprocessing techniques using Python. This will ensure that appropriate data is given as input for model building. Data preprocessing techniques including outlier analysis, imputation techniques, scaling techniques, etc., will be discussed using practical oriented datasets.

Who this course is for:

This course is for individuals who want to upskill and make a career in the field of data science.
It is also for working professionals who would like to upskill their understanding of CRISP-ML(Q).
Students from any background are encouraged to take up this course.
Students from engineering backgrounds are welcome to enrich their learning process using this program.

EDA / Descriptive Statistics using Python (Part - 1)

What you'll learn

Explore related topics

Course content

Introduction5 lectures • 11min

Business Understanding Phase3 lectures • 37min

Data Understanding Phase - Data Types7 lectures • 51min

Data Understanding Phase - Data Collection7 lectures • 1hr 16min

Understanding Basic Statistics5 lectures • 43min

Data Preparation Phase | Exploratory Data Analysis (EDA)8 lectures • 2hr 14min

Python Installation and setup4 lectures • 46min

Data Preparation Phase | EDA Using Python8 lectures • 2hr 48min

Requirements

Description

Who this course is for: