Quiz No.2: Exploratory Data Analysis (EDA) - Codanics

Course Content

How and Why to Register

Dear, to register for the 6 months AI and Data Science Mentorship Program, click this link and fill the form give there: https://shorturl.at/fuMX6

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Who is a Data Scientist?

Software installation and Important Details

In this section you will learn which software are important to install and learn from.

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Software installation and websites for this course

Website introduction and opportunity to learn and earn for Free

Complete Installation of Softwares

Login to made for the course

Questions answered live on 26 09 2023

Day-1

Introduction and details about 6 months plan

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Welcome and 6 months plan

Laptop buying guide for this course

LLMs’ websites and logins to learn prompt Engineering

VScode themes and extensions for Killer look

Questions and Answers Day-1

Day-2

What is AI and How is it Different From Data Science?

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Artificial Intelligence Kya Hai?

AI vs Data Science vs ML vs DL

Big Players in AI

Benefits of AI in our Daily life

Registration Deadline for Paid Zoom Classes

Day-3

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Data Science vs. AI

Data Science vs. Data Analytics

AI and Types of AI

Reading and Blogs for the course

Find jobs in AI and Data Science

Kia AI meri job khaye ge?

Ideas for AI Developers

Main Goals of this Course

Why should I learn Python to learn AI?

Software Installation to learn Python for AI and Data Science

Conda Environments for coding in Python

Python IDEs and VScode

Day-4

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Start with Python

Day-5

Learn Python from Zero

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Conda Environments (A-Z)

First Line of code in python

Variables in Python

Operator in python

Data Types in Python

Day-6

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Indentations and if conditions in Python

User input program in python

Data structures and indexing in python

Control flow statements in python

Nested Loops in Python

Day-7

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Functions & Lmbda funtions in Python

Modules and Libraries in Python

Types of Errors in Python

Jupyter Notebook and File Handling in Python

Day-8

Master Pandas Library

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Mastering Pandas Library and EDA (Part-1)

Practice on Pandas before next lectures

Day-9

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Quiz No.1: Pandas Basics

Mastering Pandas Library and EDA (Part-2)

Day-10

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Quiz No.2: Exploratory Data Analysis (EDA)

Mastering Pandas Library and EDA (Part-3)

Learn MarkDown Language in 72 minutes

Day-11

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Mastering Pandas Library and EDA (Part-4)

Day-12

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Mastering Pandas Library and EDA (Part-5)

Day-13

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Quiz No.3: Master Pandas and EDA with Interactive Quiz

Automatic EDA with ydata-profiling

Day-14

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EDA on Google Play Store Data (Part-1)

Day-15

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Exploratory Data Analysis q zaroori hy?

Missing Values ka rola kia hy?

Day-16

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Mastering Pandas Library and EDA (Part-8)

Day-17: Complete EDA on Google PlayStore Apps

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Complete EDA on Google Play Store Apps

Day-18: Outliers

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What are outliers?

Types of Outliers

How to identify Outliers? Visual + IQR Methods

Z-score method for outliers detection

How to handle Outliers?

Outliers in one go (Quick Summary)

Day-19: Complete EDA project on Apple APP store apps Dataset

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Use AI for Fast paced EDA

Big Datasets for EDA practice

Complete EDA on Apple APP store Data (A-Z)

Day-20

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Pandas Python Library for EDA Analysis: A Comprehensive Guide

Exploratory Data Analysis (EDA) Kyun Zaroori Hai?

Missing values k Rolay

Day-21: Data Visualization-1

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What is Data Visualization?

Types of Plots/Charts

Making plots/charts in Python

Day-22: Data Science Portfolio

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What is a Data Science Portfolio?

Elements of a great Data Science Portfolio

Steps to Create Data Science Portfolio

What do recruiters look for in Data science jobs?

How this course will build your portfolio?

Polish your LinkedIn Profile for Data Science Jobs

Day-23: Data Visualization-2

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Data Visualization in Python (Blog and Code)

Data Visualization in Python

Day-24: Data Visualization-3

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Plotting with Seaborn in Python | Data Visualization

Plotly and Data Visualization for 2D and 3D Interactive plots

15 important plots to make with plotly in python

Day-25: Quiz Time, Data Visualization-4

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Data Visualization Quiz

Day-26: Data Visualization-5

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Sunburst plot in Python using plotly

Animated Plots in Python

GEO Maps in Python using plotly: Interactive and Animated maps

Saving Plotly Plots in gif files

Python plotly Visualization: A complete Beginner guide

Day-27: Data Scaling/Normalization/standardization and Encoding

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Feature Scaling/Normalization

Feature Encoding

Day-28: NumPy (Part-1)

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Introduction to numpy (a python Library)

Day-29: NumPy (Part-2)

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Mastering Numpy with extended functions of arrays

Day-30: NumPy (Part-3)

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NumPy indexing and slicing

Day-31: NumPy (Part-4)

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Creating NumPy arrays from Data

Day-32a: NumPy (Part-5)

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Matrices in NumPy python library

Day-32b: Data Preprocessing / Data Wrangling

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Mastering the art of Data preprocessing

Day-33: Zero-to-Math for Data Science (Part-1)

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What is Mathematics?

Branches of Mathematics

What did you learn Today?

Day-34: Zero-to-Math for Data Science (Part-2)

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Number Theory in few words

Applications of Number theory

Pros of learning Number Theory and Mathematics

Factors and Multiples

Divisibility Rules in Math

Day-35: Zero-to-Math for Data Science (Part-3)

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GCD and LCM in math

Modular Arithmetic

What is Modulus?

Numbers and their types in number theory

Day-36: Zero-to-Math for Data Science (Part-4)

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What is Algebra?

Why Algebra is important to learn?

How to learn Algebra?

Types of Algebra

History of Algebra

Algebra and Data Science

Day-37: Algebra in Data Science

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Algebra and Data Science (Reading Material)

Day-38: Pre-Algebra (Part-1)

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Pre-Algebra: Basic to Advanced understanding

Basics of pre-algebra

Introduction to pre-algebra

Day-39: Pre-Algebra (Part-2)

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Fractions and Decimals

Ratios and proportions

Percentages and Percent Calculations

Day-40: Pre-Algebra (Part-3)

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Solving equations

Measurements, Units and Geometry

Data Analysis and Algebra

Problem solving in algebra

Exercise Questions for pre-algebra

Day-41: Elementary Algebra

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Elementary Algebra

Important Tasks

Day-42: Linear Algebra (Part-1)

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What is a vector?

What are Matrices?

Day-43: Linear Algebra (Part-2)

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Linear Algebra Definitions

Cartesian coordinates Geometry and Linear Algebra

Scalars and Scaling a vector

Day-44: Linear Algebra (Part-3)

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Vectors’ Addition

Vector span and Linear Dependence of vectors

x and y intercepts in linear algebra

Dot product of vectors

Cross product of two vectors

Day-45: Linear Algebra (Part-4)

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Basis Vectors and Transformation

Linear Transformation and Matrices

Matrices are Linear Transformations

Shear Transformations

Why do we need transformations?

Matrix Multiplications as compositions

Matrices and Types (Part-1/2)

Matrices and Types (Part-2/2)

Day-46: Linear Algebra (Part-5)

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Addition and Subtraction of matrices

Matrices Multiplication

Importance of Matrices Multiplication in Data Science and AI

Determinants of Matrices

Important Tasks

Day-47: Linear Algebra (part-6)

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Inverse of a Matrix

System of Equations

Types of Linear Equations

Writing System of Equations

Matrix form of System of Equations

Assignment Alert

Day-48: Linear Algebra (Part-7)- Solving System of Equations

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Solving system of equations

Graphical Method

Substitution Method

Elimination Method

Matrix inversion Method

Advance Methods

Assignment Alert

Day-49: Linear Algebra (Part-8)- Solving System of Equations

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Advance Methods to solve system of equations

Gaussian Elimination Method

Gauss-Jordan Elimination Method

LU Decomposition Method

Singular Value Decomposition (SVD)

Day-50: Linear Algebra (Part-9)- Solving System of Equations

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Row Operations Permissible vs. non-permissible

Eigenvalues and Eigenvectors

Important Tasks before upcoming lectures

Day-51: Linear Algebra (Part-10) – Python

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Linear Algebra Articles

Solving System of Equations in python

Solve system of complex equations in python

Vectors in Python

Linear Transformation in python

Shear Transformation in python

singular value decompositions (SVD) in python

Linear Algebra-Notes

Vectors in Linear Algebra-Notes

Linear Transformation-Notes

Statistics is next

Day-52: Statistics for Data Science (Part-1)

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What is Statistics?

What will we learn?

Why statistics is important?

Scales or Levels of Measurements

Qualitative vs. Quantitative Data

Discrete vs. Continuous vs. Binary Data

What is Time Series Data?

What is spatial Data?

Day-53: Statistics for Data Science (Part-2)

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Categorical vs. Ordinal Categorical Data

Multivariate Data Type

Structured vs. Unstructured Data

Boolean Data Type

Operationalization and Proxy Measurements

Day-54: Statistics for Data Science (Part-3)

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Surrogate Endpoints in statistics

True and Error Score

Types of Errors in Data Collection

Type-I and Type-II errors

Examples from the Audience for Type I and Type II errors

Reliability and validity

Notes of All lectures on ABC of statistics for Data Science

Day-55: Statistics for Data Science (Part-4)

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Measurement and Data Bias

How to remove bias?

Statistics and types of Statistics

Why is statistics important to learn?

Data Analysis and Types of Data Analysis

Next Tasks to Learn: Sample and Population

Day-56: Statistics for Data Science (Part-5)

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Central Tendency: Mean, Median, and Mode

Day-57: Statistics for Data Science (Part-6)

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Central Tendency of the Data Complete Guide

Hormonic Mean understanding of the formula

Limitations of using mean in Data Science and statistics

Median in statistics

Mode in Statistics

Population vs. sample and their means

Variability | Dispersion | Spread of the Data

Day-58: Statistics for Data Science (Part-7)

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Inter quartile Range (IQR)

Standard Deviation and Standard Error

Normal distribution and Standard Deviation

Day-59: Statistics for Data Science (Part-8)

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Data Distributions and types of data distributions

Skewness and Kurtosis

Day-60: Statistics for Data Science (Part-9)

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Primary vs. Secondary | Data

Next Tasks for upcoming lectures on sampling

Day-61: Statistics for Data Science (Part-10)

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Data Collection in the age of big data

Best Practices for Data Collection

Representative and Non-representative sample types

Details of each Sampling Techniques for Data Collection

Hybrid Sampling

Day-62: Statistics for Data Science (Part-11)

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Descriptive Statistics

EDA and Statistics

How to choose the right statistical method?

EDA and the Four Pillars of statistics

Day-63: Statistics for Data Science (Part-12)

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Dependent vs. independent variables

Inferential Statistics

Hypothesis Testing

Confidence Interval in statistics

Day-64: Statistics for Data Science (Part-13)

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Chi-Square Test in Python

Next Statistical tests

Test for Normal Distribution Shapiro-Wilk Test in Python

t-tests in Python

Levene’s test in Python

One-way ANOVA in python

Day-65: Statistics for Data Science (Part-14)

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ANOVA | one-way, 2-way and N-way in Python

MANOVA in python

Correlation in Python

Day-66: Statistics for Data Science (Part-15)

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Chi-squared test case study in python

t-test and case studies in python

One-way and Two-way ANOVA case studies

Correlation | Pearson’s and Spearman’s | Case studies in python

EDA and basic Pillars of Statistics

Book announcement for Statistics ABC of statistics

Day-67: Machine Learning (Part-1)

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What is machine learning?

Types of Machine Learning

Day-68: Machine Learning (Part-2)

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ML Model Building to Deployment | Steps A-Z

What is an algorithm?

Training and Testing Data | Features and Labels | Model

Overfitting vs. Underfitting

Important Python libraries for Machine Learning

Installation steps

Day-69: Machine Learning (Part-3)

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Data Pre-processing before ML Model

Day-70: Machine Learning (Part-4)

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Steps in Data Pre-processing

Dealing with Missing values in Python

Day-71: Machine Learning (Part-5)

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Dealing with Data Inconsistencies / Anomalies

Outliers | Data Pre-processing

IQR Method to remove Outliers

Day-72: Machine Learning (Part-6)

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Data concatenation

Data Preprocessing Steps

Feature Scaling and Normalization

Standard Scaling or standardizing the data

Day-73: Machine Learning (Part-7)

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Min-max, MaxAbs, and Robust Scalars in python

Most used Scaling methods for feature scaling

Normalization and Nonlinear Data Transformation of data

L2 and L1 Normalization in python

Feature Scaling vs. Normalization

Tips about Scaling and normalization

Assignment Alert

Day-74: Machine Learning (Part-8)

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What is feature encoding?

Benefits of feature encoding

Feature Encoding in Python using scikit-learn

Feature-encoding with pandas python

Data Discretization | Data Binning

Data preprocessing steps completed

Scikit-learn base Jupyter notebook

Day-75: Machine Learning (Part-9)

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Linear Regression A-Z

Day-76: Machine Learning (Part-10)

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Logistic Regression (Theory)

Logistic Regression and example in Python A-Z

Evaluation Metrics for Regression & Classification Models

Day-77: Machine Learning (Part-11)

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Train test split matters

Support Vector Machines (SVM) Theory

Support Vector Machines (SVM) in Python

Assignment Alert about SVM

Upload Notebooks on Kaggle

Day-78: Machine Learning (Part-12)

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K-Nearest Neighbors (KNN) Theory

Euclidean Distance

Manhattan Distance

Minkowski Distance

Why Minkowski Distance is Important?

Hamming Distance

K-Nearest Neighbors (KNN) in Python

Day-79: Machine Learning (Part-13) Decision Tree

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Algorithms we have learned so far

What is Decision tree Algorithm?

Elements of Decision Tree

Entropy, Gini impurity and information gain theory

Entropy, Gini impurity and information gain in python

Decision Tree Classifier in Python

Day-80: Machine Learning (Part-14)-Ensemble Algorithms

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Ensemble Algorithms

Random Forest (Theory)

Random Forest in Python for Classification and Regression

Day-81: Machine Learning (Part-15)-Evaluation Metrics

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Evaluation Metrics in Machine Learning

Regression Metrics

Day-82: Machine Learning (Part-16)-Metrics for Classification

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Evaluation Metrics for Classification Algorithms

Day-83: Machine Learning (Part-17)

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Ensemble Algorithms Family

Boosting in Ensemble Methods

Day-84: Machine Learning (Part-18)

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Boosting algorithms and their pros. and cons.

Booting algorithms vs. Neural Networks

xgBoost vs. Random forest vs. Decision Tree | in python

Day-85: Machine Learning (Part-19)

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catBoost algorithm in Python

Day-86: Machine Learning (Part-20)

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Hyperparameter Tuning and Cross Validation

Cross Validation in machine learning (Part-1)

Cross Validation in machine learning (Part-2)

Day-87: Machine Learning (Part-21)

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pipeline in Machine Learning using Scikit-learn

Best Model Selection in Python using Scikit-learn

Day-88: Machine Learning (Part-22)

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Naive Bayes Algorithm (Part-1)

Types of Naive Bayes Algorithm | NB (Part-2)

NAIVE Bayes in Python | Naive Bayes Algorithm (Part-3)

Questions and Answers | Naive Bayes Algorithm (Part-4)

Day-89: Machine Learning (Part-23)

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Best hyperparameter tuned Model Selection

Day-90: Machine Learning (Part-24)

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How to select the best model (Presentation-1) and Feedback

How to select the best model (Presentation-2) and Feedback

Question and Answers session on 90th day of this course

Day-91: Machine Learning (Part-25)

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Encoding and Inverse Transform the data

Day-92: Machine Learning (Part-26)

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Basic Method | Missing Values Imputation (Part-1)

Machine Learning models | Missing Values Imputation (Part-2)

Advance Methods | Missing Values Imputation (Part-3)

Last lecture of 2023 for AI and Data Science

Day-93: Machine Learning (Part-27)

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Supervised Machine Learning and Assumptions

Day-94: Machine Learning (Part-28)

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Mathematics and Assumptions of Linear Regression

Multilinear Regression and Mathematics with Assumptions

Day-95: Machine Learning (Part-29)

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Polynomial Regression Theory and Assumptions

Polynomial Regression in Python with coding

Day-96: Machine Learning (Part-30)

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Ridge Regression in Python L2 Regularization

Lasso Regression | L1 Regularization

Day-97: Machine Learning (Part-31)

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Heart Disease prediction | A complete ML Project (Part-1)

Heart Disease prediction | A complete ML Project (Part-2)

Heart Disease prediction | A complete ML Project (Part-3)

Day-98: Machine Learning (Part-32)

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Heart Disease prediction | A complete ML Project (Part-4)

Heart Disease prediction | A complete ML Project (Part-5)

Day-99: Machine Learning (Part-33)

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Heart Disease prediction | A complete ML Project (Part-6)

Heart Disease prediction | A complete ML Project (Part-7)

Day-100: Un-Supervised Machine Learning (Part-1)

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Un-supervised Machine Learning – Introduction

What is Clustering?

k-Means clustering – Theory

k-Mean clustering in python

Day-101: Un-Supervised Machine Learning (Part-2)

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Hierarchical Clustering | Theory

Hierarchical Clustering | Coding in python

Gaussian Mixture Models (GMMs) | Theory

Gaussian Mixture Models (GMMs) | Metrics

Day-102: Un-Supervised Machine Learning (Part-3)

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Feature Engineering (Part-1)

Feature Engineering (Part-2)

PCA | Principal Component Analysis | Theory

PCA | Principal Component Analysis | Case Study in Python

SVD | Singular Value Decomposition

t-SNE | t-distributed Stochastic Neighbor Embedding

Day-103: Deep Learning (Part-1)

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Introduction to Deep Learning

Machine Learning vs. Deep Learning

Small Data vs. Big Data for Deep Learning

Day-104: Deep Learning (Part-2)

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What is a Neural Network?

Types of Neural Network

Day-105: Deep Learning (Part-3)

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Architecture of Neural Networks

Single Layer vs. Multilayer Neural Network

Day-106: Deep Learning (Part-4)

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Multi Layer Perceptron

Types of Multi Layer Perceptron

Applications of Multi layer perceptron

Day-107: Deep Learning (Part-5)

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Python Libraries for Deep Learning

Ten steps to create a neural Network

Build Neural Networks in Python with TensorFlow

Day-108: Deep Learning (Part-6)

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GPU for Deep Learning with TensorFlow

Build a Multilayer Perceptron in Python with TensorFlow

Day-109: Deep Learning (Part-7)

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Call Back Function for Early Stopping of epochs

How many neurons should be in each layer?

Day-110: Deep Learning (Part-8)-Activation Functions

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Activation functions in Neural Network | Linear vs. Step vs. Sigmoid

tanH, ReLu, Leaky Relu, Paramteric ReLu activation functions

Softmax activation function | Multiclass classification

How to choose an activation function for neural network?

Day-111: Case Studies (Complete Machine/Deep Learning Projects)

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Case Study on UCI Heart Disease Data Project 1

Case Study on UCI Heart Disease Data Project 2

Case Study on UCI Heart Disease Data Project 3

Case Study on UCI Heart Disease Data Project 4

Competitions and Assignments for the course

Day-112: Deep Learning (Part-9)-Complete Project

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Case Study for Kaggle Competition-(Complete Deep Learning Project)

What is random seeding or Random State in ML?

Day-113: Deep Learning (Part-10)-Complete Project

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Case Study on Bank Churn Dataset

Important Assignment for Kaggle Competitions

Announcement about the AI Job Ready Course on Codanics

Day-114: Deep Learning (Part-11)-Convolutional Neural Network (CNN)

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Convolutional Neural Network (CNN) | Part-1

Convolutional Neural Network (CNN) | Part-2

Convolutional Neural Network (CNN) | Part-3

Day-115: Deep Learning (Part-12)-Convolutional Neural Network (CNN)

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Convolutional Neural Network (CNN) | Part-4

Convolutional Neural Network (CNN) | Part-5

Convolutional Neural Network (CNN) | Part-6

Day-116: Deep Learning (Part-13)-Computer Vision

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What is Computer Vision?

Python & Computer vision

Assignment alert

Day-117: Deep Learning (Part-14)-Complete CNN Project

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Paddy Rice Disease Detection Project

Day-118: Deep Learning (Part-15)-Recurrent Neural Network (RNN)

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Introduction to RNN

RNN In depth Understanding

RNN and Python for Project making

Day-119: Deep Learning (Part-16)-Natural Language Processing (NLP)

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Introduction to Natural Language Processing

Sentiment Analysis and NLP

Day-120: Deep Learning (Part-17)-Natural Language Processing (NLP)

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LSTM (Long Short Term Memory) for NLP

History of ANN, CNN, RNN, LSTM, GRU

LSTM vs. GRU types of RNN for Deep learning in NLP

Day-121: Time Series Analysis (Part-1)

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Introduction to Time Series Analaysis

Day-122: Time Series Analysis (Part-2)

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Data Analysis Types for Time Series Data

Types of Data about Time

Time series analysis and plotting in python

Day-123: Time Series Analysis (Part-3)

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Time Series Project understanding and development

Day-124: Time Series Analysis (Part-4)

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Time series Weather forecasting project (Part-1)

Time series Weather forecasting project (Part-2)

Time series Weather forecasting project (Part-2)

Day-125: Time Series Analysis (Part-5)

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prophet model from FB (meta) for time series analysis (1/2)

prophet model from FB (meta) for time series analysis (1/2)

Day-126: Time Series Analysis (Part-6): Web-scrapping

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What is web scrapping?

Web scrapping in python

Stock market data scrapping in python

Day-127: Time Series Analysis (Part-7): ARIMA models for time series

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ARIMA (Time series analysis) in python (Part-1)

ARIMA (Time series analysis) in python (Part-2)

ARIMA (Time series analysis) in python (Part-3)

Day-128: Time Series Analysis (Part-8): Complete Project

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ARIMA, SARIMA, SARIMAX in python complete project

Day-129: git & GitHub Crash Course

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git and GitHub crash course

Day-130: Over-fitting and Under-fitting in a Model

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Underfitting in a Machine/Deep Learning Model

Overfitting in a Machine/Deep Learning Model

Good Fit or Robust Model in Machine/Deep Learning

Day-131: Improving Machine/Deep Learning Model’s Performance

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How to Improve Model’s Fitness?

Project Based Improving Model’s Fitness

Day-132: Transfer Learning and Pre-trained Models (Part-1)

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Introduction to Transfer learning

Transfer Learning in Python using TensorFlow

Assignment Alert for Transfer Learning

Day-133: Transfer Learning and Pre-trained Models (Part-2)

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Use of TensorFlow for Image Classification – A complete Project

Day-134 Transfer Learning and Pre-trained Models (Part-3)

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MobileNetV2 pre-trained Mode: A complete Project

Day-135: Generative AI (Part-1)

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What is Generative AI?

Important Key Terms used in Generative AI

Day-136: Generative AI (Part-2)

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How does Generative AI work?

Generative AI in 2024

Day-137: Generative AI (Part-3)

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Generative AI | Kia meri job kha jaye ge?

Day-138: Generative AI (Part-4)

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Progressive growing Generative Adversarial Networks (GANs)

Assignment Alert

Day-139: Generative AI (Part-5)-Tensorboard

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TensorBoard: a crash course

Day-140: Generative AI (Part-6)-Diffusion Models

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Introduction to Diffusion Models

Stable Diffusion Models in python

Day-141: Generative AI (Part-7)

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SORA: An openAI’s model for AI video generation

Important Logins to create before next lecture

Day-142: Prompt Engineering

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Introduction to prompt Engineering

Types of Prompt Enginnering

Prompt Designing Strategies to write best prompt

Generate Prompts from LLMs

LLMs and prompt engineering live demo

Day-143: Huggingface

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Use pre trained models from Huggingface

Day-144: LangChain and APIs

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Introduction to API (Application Programming Interface)

LangChain workflow for LLMs

Applications of LangChain for LLMs

LangChain + OpenAI API + HuggingFace API

Day-145: Streamlit for webapp development and deployment (Part-1)

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Introduction and installation

My first app in streamlit

streamlit cheat sheet

Day-146: Streamlit for webapp development and deployment (Part-2)

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Streamlit Data Science application

Day-147: Streamlit for webapp development and deployment (Part-3)

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Streamlit webapp without coding but prompting

Day-148: Streamlit for webapp development and deployment (Part-4)

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Creating a word Cloud app on streamlit python

requirements.txt file for streamlit apps

Day-149: Streamlit for webapp development and deployment (Part-5)

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Streamlit app deployment on free servers

Day-150: Streamlit for webapp development and deployment (Part-6)

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Create an app only in 14 lines of codes

Day-151: Streamlit for webapp development and deployment (Part-7)

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Manage Secrets in streamlit deployment with python

Day-152: Streamlit for webapp development and deployment (Part-8)

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ask the pdf and other docx application

Day-153: Streamlit for webapp development and deployment (Part-9)

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Streamlit project read pdfs in a folder

Day-154: Streamlit for webapp development and deployment (Part-10)

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Stock Market Analysis and Prediction app

Day-155: Streamlit for webapp development and deployment (Part-11)

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Dall-e-3 Image generation app in python

Day-156: Streamlit for webapp development and deployment (Part-12)

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Speech to text Streamlit Application development

Day-157: Streamlit for webapp development and deployment (Part-13)

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Text to speech Streamlit application

How to Earn using Data Science and AI skills

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How to earn online using Data Science and AI

Day-158: Flask for web app development (Part-1)

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is FLASK better than streamlit?

Installation and basics of web app development in flask

Day-159: Flask for web app development (Part-2)

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flask Installation and basic application development

Open Conda environments inside vscode

Flask app for basic EDA and plotting

Image Generation app development in flask python

Day-160: Flask for web app development (Part-3)

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End to End machine Learning project in flask | house price prediction

Day-161: Flask for web app development (Part-4)

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End to End machine Learning project in flask | Tip prediction

Day-162: Flask for web app development (Part-5)

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Diabetes prediction app development

Day-163: Flask for web app development (Part-6)

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Text to speech web app development

Day-164: Flask for web app development (Part-7)

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WordCloud app development

Speech to text app using openAI API

Day-165: Flask for web app deployment (Part-8)

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Web application deployment on Amazon AWS EC2 Instance

Day-166: FastAPI (Part-1)

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Intro to FastAPI | is FastAPI really fast?

Installing FastAPI using conda

Day-167: FastAPI (Part-2)

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Book Store app in FastAPI in python

Day-168: FastAPI (Part-3)

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FastAPI basics and running server

Day-169: FastAPI (Part-4)

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Swagger docs in FastAPI

Day-170: FastAPI (Part-5)

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Path parameters in FastAPI

Day-171: FastAPI (Part-6)

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Users path parameters in FastAPI

Day-172: FastAPI (Part-7)

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Titanic app backend development with FastAPI

Day-173: FastAPI (Part-8)

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Add static plots to FastAPI app

Add interactive plots in FastAPI app using plotly

Day-174: FastAPI (Part-9)

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Different port numbers for FastAPI

Day-175: Tableau Complete Course (Part-1)

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What is Tableau?

Tableau Learning Resources

Tableau Public Installation Free

How Tableau Public Looks Like? Interface

Load Data in Tableau Public

Load and handle Dataset in Tableau Public

Data Types check in Tableau Public

Data Filtering and sorting in Tableau Public

Day-176: Tableau Complete Course (Part-2)

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Data Visualization in Tableau Public

Scatter Plots in Tableau Public

Dynamic Scatter Plot in Tableau Public

Flip your charts in tableau public

Bar plots and sorting in Tableau public

GEO Maps in Tableau public

Day-177: Tableau Complete Course (Part-3)

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Your Own Project Visualization in Tableau Public

Saving your plots with Tableau Public

Day-178: Tableau Complete Course (Part-4)

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Area Charts in Tableau Public

Group charts in Tableau

Tableau Interactive Dashboard Development

Tableau Dashboard Deployment for Community

Day-179: Power BI (Part-1)

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Introduction to Data Visualization and Power BI

Power BI vs. Tableau vs. Python?

Top 11 benefits of using Power BI

Day-180: Power BI (Part-2)

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Install Power BI Desktop

Update Power BI features and Updates

Learning Resources for Power BI

Day-181: Power BI (Part-3)

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Business Intelligence with Power BI

Seven Steps Workflow to use Power BI

Power BI interface and Features

Connect Data in Power BI

Day-182: Power BI (Part-4)

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Data Visualization and Plotting in Power BI

Column/Bar charts in Power BI

Create a bar plot like a professional

Pro tips to make a stunning plot in Power BI

Day-183: Power BI (Part-5)

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Pie Charts in power BI

Donut Charts in Power BI

Tree Map in Power BI

Line Chats in Power BI

Day-184: Structured Querry Language (SQL) for Beginners

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Install MySQL on Windows 10

Create a MySQL Database for Beginners

Create/modify tables in mySQL

DISTINCT function in mySQL

Constraints in mySQL

Auto increment in mySQL

mySQL workbench first look and interface

Import mySQL database in mySQL workbench

Pandas processing on mySQL database

SELECT clause in mySQL

Data Manupulation in mySQL

WHERE clause and comparison operators

AND OR NOT and IN

BETWEEN clause in mySQL

Six months of AI and Data Science Mentorship Program

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You have to finish this quiz in 40 Minutes (2400 seconds)

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Exploratory Data Analysis (Basic to Advance)

This quiz will check if you have any advance knowledge of EDA

The number of attempts remaining is 5

What does a scatter plot signify?

A) The summary statistics of a dataset

B) The relationship between two numerical variables

C) The distribution of a single variable

D) The count of unique values in a dataset

Why might you perform a log transformation on a dataset?

A) To upload data to a database

B) To create visualizations

C) To manage skewness and spread in the data

D) To fill missing values

What does the term 'Encoding' refer to in handling categorical data?

A) Creating visualizations

B) Reducing the size of the dataset

C) Converting categorical data into a numerical format

D) Uploading data to a database

Why is feature engineering important in EDA?

A) To create visualizations

B) To create meaningful variables for modeling

C) To upload data to a database

D) To reduce the size of the dataset

Which clustering method does not require the number of clusters to be specified a priori?

A) K-means clustering

B) Hierarchical clustering

C) DBSCAN

D) Linear Discriminant Analysis (LDA)

Which plot is used to visualize the relationship between a numerical and a categorical variable?

A) Histogram

B) Scatter Plot

C) Pie Chart

D) Box Plot

Which method is used to calculate the correlation between variables?

A) corr() method

B) relation() method

C) rel() method

D) correlation() method

Which method is commonly used to impute missing values in a dataset?

A) Log transformation

B) Mean imputation

C) Data scaling

D) Data visualization

How can you handle imbalanced data in a dataset?

A) Log transformation

B) Data visualization

C) Resampling techniques

D) Data scaling

In the Titanic dataset how would you find the number of passengers who survived?

A) titanic['Survived'].sum()

B) titanic.sum('Survived')

C) titanic.count('Survived')

D) titanic['Survived'].count()

What is the primary purpose of data transformation in EDA?

A) To upload data to a database

B) To create visualizations

C) To reduce the size of the dataset

D) To achieve better modeling or data analysis results

Which plot is used to visualize the distribution of a categorical variable?

A) Histogram

B) Scatter Plot

C) Line Plot

D) Bar Plot

Which method is commonly used to handle missing data?

A) Normalization

B) Imputation

C) Standardization

D) Correlation

What does multivariate analysis allow you to do?

A) Analyze more than two variables to understand relationships

B) Analyze a single variable

C) Create visualizations

D) Calculate the mean

What is the purpose of a box plot in EDA?

A) To visualize the distribution of categorical data

B) To visualize time series data

C) To visualize summary statistics and identify outliers

D) To visualize the relationship between two variables

What is the benefit of using a violin plot in EDA?

A) To upload data to a database

B) To create databases

C) To fill missing values

D) To visualize the distribution and density of data

What is the purpose of a Pair Plot in EDA?

A) To visualize the spread of data

B) To visualize the distribution of single variables and relationships between two variables

C) To visualize the summary statistics of a dataset

D) To visualize the distribution of a single variable

What is the purpose of data cleaning in EDA?

A) To make data look visually appealing

B) To reduce the size of the dataset

C) To upload data to a database

D) To ensure the quality and accuracy of data

What is the primary objective of Exploratory Data Analysis (EDA)?

A) To understand the underlying patterns in the data

B) To implement machine learning algorithms

C) To build predictive models

D) To create databases

What is the main challenge in handling categorical data in EDA?

A) Creating visualizations

B) Encoding it in a way suitable for machine learning models

C) Uploading data to a database

D) Reducing the size of the dataset

Which method is used to identify the unique values in a column of a DataFrame?

A) different()

B) uniques()

C) unique()

D) distinct()

What is the purpose of binning data in EDA?

A) To group continuous data into intervals

B) To upload data to a database

C) To fill missing values

D) To create visualizations

What does the term 'Bivariate Analysis' refer to in EDA?

A) Analysis of the entire dataset

B) Analysis of a single variable

C) Analysis of two variables to determine relationships

D) Analysis of more than two variables

What is the goal of anomaly detection in EDA?

A) To fill missing values

B) To upload data to a database

C) To identify unusual data points that deviate from the norm

D) To create visualizations

Which method is commonly used to calculate the covariance between variables?

A) std() method

B) var() method

C) cor() method

D) cov() method

What is the primary goal of statistical inference in EDA?

A) To upload data to a database

B) To create visualizations

C) To fill missing values

D) To make predictions or inferences about a population based on a sample

What is the significance of the median in a dataset?

A) It represents the middle value of a sorted dataset

B) It represents the spread of data

C) It represents the total count of values

D) It represents the most common value

Why might you use logistic regression in EDA?

A) To fill missing values

B) To upload data to a database

C) To model binary outcomes

D) To create visualizations

Which measure provides the spread of data around the mean?

A) Mode

B) Standard Deviation

C) Mean

D) Median

What is the primary purpose of advanced visualization techniques in EDA?

A) To upload data to a database

B) To fill missing values

C) To understand complex relationships in the data more clearly

D) To create databases

Which method is commonly used to test the normality of a dataset?

A) Chi-square test

B) t-test

C) Shapiro-Wilk Test

D) Correlation test

What is the primary goal of feature selection in EDA?

A) To reduce the size of the dataset

B) To upload data to a database

C) To create visualizations

D) To select the most relevant features for modeling

What is the primary goal of forecasting in time series analysis?

A) To create visualizations

B) To fill missing values

C) To upload data to a database

D) To predict future values based on past observations

What is a Quantile in a dataset?

A) Value below which a given percentage of data falls

B) The spread of data

C) The most common value

D) The average value

What does the term 'Outlier' signify in a dataset?

A) A data point significantly different from others

B) The most common data point

C) A data point in the middle of the range

D) The last data point in a dataset

Which plot is used to visualize the relationships between more than two variables?

A) Bar Chart

B) Pie Chart

C) Pair Plot

D) Scatter Plot

What is the purpose of one-hot encoding in handling categorical data?

A) To upload data to a database

B) To create visualizations

C) To reduce the size of the dataset

D) To convert categorical data into numerical format

What is the primary goal of Linear Discriminant Analysis (LDA) in EDA?

A) To fill missing values

B) To upload data to a database

C) To create visualizations

D) To find the linear combinations of features that best separate different classes

What is the primary goal of forecasting models in time series analysis?

A) To predict future values based on past and present data

B) To create visualizations

C) To upload data to a database

D) To fill missing values

Which plot can be used to visualize the distribution of categorical data?

A) Box Plot

B) Bar Chart

C) Histogram

D) Scatter Plot

Which plot is used to visualize the distribution of a dataset?

A) Histogram

B) Bar chart

C) Pie chart

D) Scatter plot

Which method is commonly used for linear dimensionality reduction?

A) K-means clustering

B) DBSCAN

C) Principal Component Analysis (PCA)

D) Hierarchical clustering

Why are heatmaps used in EDA?

A) To create databases

B) To visualize the correlation or other relationships between variables

C) To upload data to a database

D) To fill missing values

What is the benefit of encoding categorical data into numerical data?

A) To fill missing values

B) Uploading data to a database

C) Creating visualizations

D) To prepare the data for machine learning algorithms

Why is outlier detection important in EDA?

A) To upload data to a database

B) To create visualizations

C) To fill missing values

D) To identify and manage anomalous data points that may skew analysis

Which technique is commonly used for anomaly detection in time series data?

A) Decomposition

B) Linear Discriminant Analysis (LDA)

C) K-means clustering

D) Principal Component Analysis (PCA)

Which measure is commonly used to understand the dispersion or spread in a dataset?

A) Mean

B) Standard Deviation

C) Median

D) Mode

What is the IQR (Interquartile Range) used for in EDA?

A) Calculating correlation

B) Identifying the mode

C) Measuring statistical dispersion

D) Calculating the mean

Which plot is commonly used to visualize hierarchical clustering?

A) Bar chart

B) Pie chart

C) Dendrogram

D) Scatter plot

Which plot is often used in time series analysis?

A) Pie Chart

B) Scatter Plot

C) Bar Chart

D) Line Plot

What is the primary goal of correlation analysis?

A) To categorize data

B) To identify missing values

C) To predict future data points

D) To identify relationships between variables

Which method is commonly used for non-linear dimensionality reduction?

A) K-means clustering

B) Linear Discriminant Analysis (LDA)

C) t-Distributed Stochastic Neighbor Embedding (t-SNE)

D) Principal Component Analysis (PCA)

What is the purpose of binning or bucketing data in EDA?

A) To group data into intervals

B) To find the mean of the data

C) To find the median of the data

D) To remove outliers

What is the primary goal of the Chi-square test in EDA?

A) To create visualizations

B) To upload data to a database

C) To test the association between categorical variables

D) To fill missing values

What is the main objective of clustering in EDA?

A) To create visualizations

B) To upload data to a database

C) To group similar data points together based on certain features

D) To fill missing values

How is the mean of a dataset calculated?

A) The value occurring most frequently

B) Sum of all values divided by the number of values

C) The middle value of a sorted list of numbers

D) Highest value minus lowest value

Which method is used to calculate the correlation matrix?

A) corr() method

B) relation() method

C) correlation() method

D) cov() method

Which method is commonly used to identify the optimal number of clusters in a dataset?

A) Elbow Method

B) Principal Component Analysis (PCA)

C) t-SNE

D) Linear Discriminant Analysis (LDA)

What does a Box Plot represent?

A) The summary statistics of a dataset including outliers

B) The distribution of categorical data

C) The relationship between two variables

D) The correlation between variables

Which plot is commonly used to visualize the distribution of a continuous variable?

A) Scatter Plot

B) Histogram

C) Pie Chart

D) Bar Chart

What does the term 'Feature Extraction' refer to in EDA?

A) Filling missing values

B) Creating visualizations

C) Creating new features from existing features to better capture the underlying data patterns

D) Uploading data to a database

How are missing values represented in a Pandas DataFrame?

A) NaN

B) Null

C) Missing

D) 0

Why might you use a 3D plot in EDA?

A) To upload data to a database

B) To create databases

C) To visualize relationships among three variables

D) To fill missing values

What is the primary goal of dimensionality reduction in EDA?

A) To fill missing values

B) To create visualizations

C) To reduce the complexity and size of the dataset while retaining as much information as possible

D) To upload data to a database

How can you determine the strength of a relationship between two variables?

A) By calculating the median

B) By creating visualizations

C) By calculating the correlation coefficient

D) By calculating the mean

What does the term 'Multivariate Analysis' refer to in EDA?

A) Analysis of two variables

B) Analysis of a single variable

C) Analysis of summary statistics

D) Analysis involving more than two variables

What is the null hypothesis in hypothesis testing?

A) A statement of no effect or no difference

B) A statement of effect or difference

C) A statement of causation

D) A statement of correlation

What does a histogram represent?

A) Summary statistics of a dataset

B) Relationship between two variables

C) Frequency distribution of a continuous variable

D) Unique values in a dataset

What is the benefit of scaling data in EDA?

A) To create visualizations

B) To ensure that variables are compared on a similar scale

C) To upload data to a database

D) To fill missing values

What is the significance of the variance in a dataset?

A) It provides a measure of skewness

B) It provides a measure of central tendency

C) It provides a measure of the spread of data

D) It provides a measure of correlation

Which measure is used to understand the spread of a dataset?

A) Median

B) Mean

C) Mode

D) Variance

What is the purpose of a scatter matrix plot in EDA?

A) To fill missing values

B) To create single variable distributions

C) To upload data to a database

D) To visualize pairwise relationships in a dataset

What is the purpose of hypothesis testing in EDA?

A) To create visualizations

B) To fill missing values

C) To make inferences about populations based on samples

D) To upload data to a database

What is the main objective of normalization in data processing?

A) To scale the data to a specific range

B) To fill missing values

C) To create visualizations

D) To calculate the mean

Which method is used to replace missing values in a dataset?

A) replace() method

B) missing() method

C) null() method

D) fillna() method

Which graph is used to display the relationship between two categorical variables?

A) Stacked Bar Chart

B) Histogram

C) Box Plot

D) Scatter Plot

Which method is commonly used for seasonal decomposition of time series?

A) STL (Seasonal and Trend decomposition using Loess)

B) t-SNE

C) Linear Discriminant Analysis (LDA)

D) Principal Component Analysis (PCA)

Which plot is commonly used to visualize the correlation between variables?

A) Pie Chart

B) Bar Plot

C) Line Plot

D) Heatmap

What is the alternative hypothesis in hypothesis testing?

A) A statement of causation

B) A statement of correlation

C) A statement of effect or difference

D) A statement of no effect or no difference

What is the purpose of dimensionality reduction in EDA?

A) To reduce the number of variables and simplify analysis

B) To upload data to a database

C) To fill missing values

D) To create visualizations

How can you describe the skewness of a dataset?

A) The measure of spread of the distribution

B) The measure of peakness of the distribution

C) The measure of asymmetry of the distribution

D) The measure of central tendency

Why might you use a box plot in EDA?

A) To visualize the distribution and identify outliers

B) To upload data to a database

C) To fill missing values

D) To create databases

Which method is commonly used for feature extraction in EDA?

A) Data scaling

B) Mean imputation

C) Data visualization

D) Principal Component Analysis (PCA)

What is the purpose of data transformation in EDA?

A) To reduce the size of the dataset

B) To upload data to a database

C) To create visualizations

D) To achieve a better fit for modeling

What does the term 'Kurtosis' signify in a dataset?

A) The measure of the tail and sharpness of the central peak of a distribution

B) The measure of the spread of data

C) The measure of the central tendency

D) The measure of the relationship between variables

Which test is commonly used to compare the means of two groups?

A) Correlation test

B) ANOVA

C) Chi-square test

D) t-test

Which method is commonly used to handle missing categorical data?

A) Mode imputation

B) Mean imputation

C) Standard Deviation imputation

D) Median imputation

How can you handle missing values in time series data?

A) Interpolation

B) Mean imputation

C) Mode imputation

D) Standard Deviation imputation

What does the p-value signify in hypothesis testing?

A) The probability of observing the data given that the null hypothesis is true

B) The probability of the alternative hypothesis being true

C) The probability of observing the data given that the alternative hypothesis is true

D) The probability of the null hypothesis being true

Why is time series analysis important in EDA?

A) To upload data to a database

B) To analyze data points collected or recorded at specific time intervals

C) To reduce the size of the dataset

D) To create visualizations

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