## Data Analysis Bootcamp 21 Real World Case Studies

### What you’ll learn

Understand the value of data for businesses

The importance of Data Analytics

The role of a Data Analyst

Learn to use Python, Pandas, Matplotlib & Seaborn, Scikit-learn

Learn Visualization Tools such as Matplotlib, Seaborn, Plotly and Mapbox

Hypothesis Testing and A/B Testing – Understand t-tests and p values

Unsupervised Machine Learning with K-Means Clustering

Machine Learning from Linear Regressions (polynomial & multivariate), K-NNs, Logistic Regressions, SVMs, Decision Trees & Random Forests

Advanced Pandas techniques from Vectorizing to Parallel Processsng

Statistical Theory, Probability Theory, Distributions, Exploratory Data Analysis

Ananlytic Case Studies involving Retail, Health, Elections, Sports, Resturants, Airbnb, Uber and more!

Full Tutorial on Google Data Studio for Dashboard Creation

### Requirements

Familiar with basic programming concepts

Highschool level math knowledge

Broadband Internet connection

### Description

Data Analysts aim to discover how data can be used to answer questions and solve problems through the use of technology. Many believe this will be the job of the future and be the single most important skill a job application can have in 2020. In the last two decades, the pervasiveness of the internet and interconnected devices has exponentially increased the data we produce. The amount of data available to us is Overwhelming and Unprecedented. Obtaining, transforming and gaining valuable insights from this data is fast becoming the most valuable and in-demand skill in the 21st century. In this course, you’ll learn how to use Data, Analytics, Statistics, Probability, and basic Data Science to give an edge in your career and everyday life. Being able to see through the noise within data, and explain it to others will make you invaluable in any career. We will examine over 2 dozen real-world data sets and show how to obtain meaningful insights. We will take you on one of the most up-to-date and comprehensive learning paths using modern-day tools like Python, Google Colab and Google Data Studio. You’ll learn how to create awesome Dashboards, tell stories with Data and Visualizations, make Predictions, Analyze experiments and more!Our learning path to becoming a fully-fledged Data Analyst includes:The Importance of Data AnalyticsPython Crash Course Data Manipulations and Wrangling with PandasProbability and StatisticsHypothesis TestingData VisualizationGeospatial Data Visualization Story Telling with DataGoogle Data Studio Dashboard Design – Complete CourseMachine Learning – Supervised LearningMachine Learning – Unsupervised Learning (Clustering)Practical Analytical Case StudiesGoogle Data Studio Dashboard & Visualization Project: Executive Sales Dashboard (Google Data Studio)Python, Pandas & Data Analytics and Data Science Case Studies:Health Care Analytics & Diabetes PredictionAfrica Economic, Banking & Systematic Crisis DataElection Poll AnalyticsIndian Election 2009 vs 2014Supply-Chain for Shipping Data AnalyticsBrent Oil Prices AnalyticsOlympics Analysis – The Greatest OlympiansHome Advantage Analysis in Basketball and SoccerIPL Cricket Data AnalyticsPredicting the Soccer World CupPizza Resturant AnalyticsBar and Pub AnalyticsRetail Product Sales AnalyticsCustomer ClusteringMarketing Analytics – What Drives Ad PerformanceText Analytics – Airline Tweets (Word Clusters)Customer Lifetime ValuesTime Series Forecasting – Demand/Sales ForecastAirbnb Sydney Exploratory Data AnalysisA/B Testing

### Overview

Section 1: Course Introduction & the Importance of Data Analysts

Lecture 1 Course Introduction

Lecture 2 The Importance of Data Analyst

Lecture 3 Why Data is the new Oil

Lecture 4 Making Sense of Buzz Words, Data Science, Big Data, Machine & Deep Learning

Lecture 5 The Roles in the Data World – Analyst, Engineer, Scientist, Statistician, DevOps

Section 2: Download Code and Slides and Setup Google Colab

Lecture 6 Download Code and Slides

Lecture 7 Download Course Code, Slides and Setup Google Colab for your iPython Notebooks

Section 3: Python Crash Course

Lecture 8 Why use Python for Data Anakytics and Data Science?

Lecture 9 Python – Basic Variables

Lecture 10 Python – Array/Lists and Dictionaries

Lecture 11 Python – Conditional Statements

Lecture 12 Python – Loops

Lecture 13 Python – Functions

Lecture 14 Python – Classes

Section 4: Pandas – Data Series and Manipulation

Lecture 15 Introduction to Pandas

Lecture 16 Pandas 1 – Data Series

Lecture 17 Pandas 2A – DataFrames – Index, Slice, Stats, Finding Empty cells, Filtering

Lecture 18 Pandas 2B – DataFrames – Index, Slice, Stats, Finding Empty cells & Filtering

Section 5: Pandas – Data Cleaning & Aggregration

Lecture 19 Pandas 3B – Data Cleaning – Alter Colomns/Rows, Missing Data & String Operations

Lecture 20 Pandas 3A – Data Cleaning – Alter Colomns/Rows, Missing Data & String Operations

Lecture 21 Pandas 4 – Data Aggregation – GroupBy, Map, Pivot, Aggreate Functions

Section 6: Pandas – Feature Engineering & Joins/Merge/Concatenating

Lecture 22 Pandas 5 – Feature Engineer, Lambda and Apply

Lecture 23 Pandas 6 – Concatenating, Merging and Joinining

Section 7: Pandas – Time Series Data

Lecture 24 Pandas 7 – Time Series Data

Section 8: Advanced Pandas

Lecture 25 Pandas 7 – ADVANCED Operations – Iterows, Vectorization and Numpy

Lecture 26 Pandas 8 – ADVANCED Operations – More Map, Zip and Apply

Lecture 27 Pandas 9 – Advanced Operations – Parallel Processing

Section 9: Map Visualizations

Lecture 28 Map Visualizations with Plotly – Cloropeths from Scratch – USA and World

Lecture 29 Map Visualizations with Plotly – Heatmaps, Scatter Plots and Lines

Section 10: Statistics for Data Analysts & Visualizations

Lecture 30 Introduction to Statistics

Lecture 31 Descriptive Statistics – Why Statistical Knowledge is so Important

Lecture 32 Descriptive Statistics 1 – Exploratory Data Analysis (EDA) & Visualizations

Lecture 33 Descriptive Statistics 2 – Exploratory Data Analysis (EDA) & Visualizations

Lecture 34 Sampling, Averages & Variance And How to lie and Mislead with Statistics

Lecture 35 Variance, Standard Deviation and Bessel’s Correction

Lecture 36 Types of Variables – Quantitive and Qualitative

Lecture 37 Frequency Distributions

Lecture 38 Frequency Distributions Shapes

Lecture 39 Analyzing Frequency Distributions – What is the Best Type of Wine? Red or White?

Lecture 40 Covariance & Correlation – Do Amazon & Google know you better than anyone else?

Lecture 41 Sampling – Sample Sizes & Confidence Intervals – What Can You Trust?

Lecture 42 Mean, Mode and Median – Not as Simple As You’d Think

Lecture 43 The Normal Distribution & the Central Limit Theorem

Lecture 44 Lying with Correlations – Divorce Rates in Maine caused by Margarine Consumption

Lecture 45 Z-Scores

Section 11: Probability Theory

Lecture 46 Probability – An Introduction

Lecture 47 Estimating Probability

Lecture 48 Addition Rule

Lecture 49 Permutations & Combinations

Lecture 50 Bayes Theorem

Section 12: Hypothesis Testing

Lecture 51 Hypothesis Testing Introduction

Lecture 52 Statistical Significance

Lecture 53 Hypothesis Testing – P Value

Lecture 54 Hypothesis Testing – Pearson Correlation

Section 13: Google Data Studio – Introduction & Setup

Lecture 55 All about Google Data Studio

Lecture 56 Opening Google Data Studio and Uploading Data

Section 14: Google Data Studio – Your First Dashboard

Lecture 57 Your First Dashboard Part 1

Lecture 58 Your First Dashboard Part 2

Lecture 59 Creating New Fields

Section 15: Google Data Studio – Pivot & Dynamic Tables (with Filters)

Lecture 60 Pivot Tables

Lecture 61 Dynamic Filtered Tables

Section 16: Google Data Studio – Scorecards and Time Comparison

Lecture 62 Scorecards

Lecture 63 Scorecards with Time Comparison

Section 17: Google Data Studio – Bar Charts, Line Charts and Time Series Plots

Lecture 64 Bar Charts

Lecture 65 Line Charts

Lecture 66 Time Series and Comparitive Time Series Plots

Section 18: Google Data Studio – Pie charts, Donut Charts, Treemaps & Scatter Plots

Lecture 67 Pie Charts, Donut Charts and Tree Maps

Lecture 68 Scatter Plots

Section 19: Google Data Studio – Geographic & Map Plots

Lecture 69 Google Data Studio – Geographic & Map Plots

Section 20: Google Data Studio – Bullet and Line Area Plots

Lecture 70 Google Data Studio – Scatter Plots

Section 21: Google Data Studio – Sharing your Interactive Dashboards

Lecture 71 Google Data Studio – Sharing your Interactive Dashboards

Section 22: Retail Sales Dashboard for Executives

Lecture 72 Homework Project – Create Executive Sales Dashboard

Section 23: Introduction to Machine Learning

Lecture 73 How Machine Learning enables Computers to Learn

Lecture 74 What is a Machine Learning Model?

Lecture 75 Types of Machine Learning

Section 24: Linear Regressions

Lecture 76 Linear Regression – Introduction to Cost Functions and Gradient Descent

Lecture 77 Linear Regressions in Python from Scratch and using Sklearn

Lecture 78 Polynomial and Multivariate Linear Regression

Section 25: Classification – Logistic Regression, SVM, Decision Trees, Random Forets & KNN

Lecture 79 Logistic Regression

Lecture 80 Support Vector Machines (SVMs)

Lecture 81 Decision Trees and Random Forests & the Gini Index

Lecture 82 K-Nearest Neighbors (KNN)

Section 26: Assessing Model Performance

Lecture 83 Assessing Performance – Confusion Matrix, Precision and Recall

Lecture 84 Understanding the ROC and AUC Curve

Lecture 85 What Makes a Good Model? Regularization, Overfitting, Generalization & Outliers

Section 27: Neural Networks Overview

Lecture 86 Introduction to Neural Networks

Lecture 87 Types of Deep Learning Algoritms CNNs, RNNs & LSTMs

Section 28: Unsupervised Learning

Lecture 88 Introduction to Unsupervised Learning

Lecture 89 K-Means Clustering

Lecture 90 Choosing K – Elbow Method & Silhouette Analysis

Lecture 91 K-Means in Python – Choosing K using the Elbow Method & Silhoutte Analysis

Section 29: Dimensionality Reduction

Lecture 92 Principal Component Analysis

Lecture 93 t-Distributed Stochastic Neighbor Embedding (t-SNE)

Lecture 94 PCA & t-SNE in Python with Visualization Comparisons

Section 30: Case Study 1 – Airbnb Sydney Exploratory Data Analysis

Lecture 95 Case Study Note

Lecture 96 Understanding the Problem + Exploratory Data Analysis and Visualizations

Section 31: Case Study 2 – Retail Product Sales Analytics

Lecture 97 Data Cleaning and Preparation

Lecture 98 Sales and Revenue Analysis

Lecture 99 Analysis per Country, Repeat Customers and Items

Section 32: Case Study 3 – Marketing Analytics – What Drives Ad Performance

Lecture 100 Understanding the Problem + Exploratory Data Analysis and Visualizations

Lecture 101 Data Preparation and Machine Learning Modeling

Section 33: Case Study 4 – Customer Clustering for Travel Agency Customers

Lecture 102 Data Exploration & Description

Lecture 103 Simple Exploratory Data Analysis and Visualizations

Lecture 104 Feature Engineering

Lecture 105 K-Means Clustering of Customer Data

Lecture 106 Cluster Analysis

Section 34: Case Study 5 – Text Analytics – Airline Tweets (Word Clusters)

Lecture 107 Understanding our Dataset and Word Clouds

Lecture 108 Visualizations and Feature Extraction

Lecture 109 Training our Model

Section 35: Case Study 6 – Customer Lifetime Value (CLV)

Lecture 110 Understanding the Problem + Exploratory Data Analysis and Visualizations

Lecture 111 Customer Lifetime Value Modeling

Section 36: Case Study 7 – Health Care Analytics – Predict Diabetes

Lecture 112 Understanding and Preparing Our Healthcare Data

Lecture 113 First Attempt – Trying a Naive Model

Lecture 114 Trying Different Models and Comparing the Results

Section 37: Case Study 8 – Africa Economic, Banking & Systematic Crisis Data

Lecture 115 Economic Dataset Understanding

Lecture 116 Visualizations and Correlations

Section 38: Case Study 9 – 2016 US President Election Analysis

Lecture 117 Understanding Polling Data

Lecture 118 Cleaning & Exploring our Dataset

Lecture 119 Data Wrangling with our Dataset

Lecture 120 Understanding the US Electoral System

Lecture 121 Visualizing our Polling Data

Lecture 122 Statistical Analysis of Polling Data

Lecture 123 Polling Simulations

Lecture 124 Polling Simulation Result Analysis

Lecture 125 Visualizing our results on a US Map

Section 39: Case Study 10 – Election Results Analysis – Indian Election 2009 vs 2014

Lecture 126 Intro

Lecture 127 Visualizations of Election Results

Lecture 128 Visualizing Gender Turnout

Section 40: Case Study 11 – Supply-Chain for Shipping Data Analytics

Lecture 129 Understanding our Dataset

Lecture 130 Visualizations and EDA

Lecture 131 More Visualizations

Section 41: Case Study 12 – Sports Analytics – Olypmics Analysis – The Greatest Olympians

Lecture 132 Getting The Medals Per Country

Lecture 133 Getting The Medals Per Country

Lecture 134 Analyzing the Winter Olympic Data and Viewing Medals Won Over Time

Section 42: Case Study 13 – Home Advantage Analysis in Basketball and Soccer

Lecture 135 Understanding Our Dataset and EDA

Lecture 136 Goal Difference Ratios Home versus Away

Lecture 137 How Home Advantage Has Evolved Over. Time

Section 43: Case Study 14 – IPL Cricket Data Analytics

Lecture 138 Loading and Understanding our Cricket Datasets

Lecture 139 Man of Match and Stadium Analysis

Lecture 140 Do Toss Winners Win More? And Team vs Team Comparisons

Section 44: Case Study 15 – Predicting the World Cup Winner (Soccer/Football)

Lecture 141 Understanding and Preparing Our Soccer Datasets

Lecture 142 Feature Extraction using our Soccer Data

Lecture 143 Predicting Game Outcomes with our Model

Lecture 144 Simulating the World Cup Outcome with Our Model

Section 45: Case Study 16 – Pizza Resturants Analysis

Lecture 145 Understanding our Dataset

Lecture 146 Analysis Per State

Lecture 147 Pizza Maps

Section 46: Case Study 17 – Brewery and Pub Analysis

Lecture 148 EDA, Visualizations and Map

Section 47: Case Study 18 – EDA and Forecasting Brent Oil Prices

Lecture 149 Understanding our Dataset and it’s Time Series Nature

Lecture 150 Creating our Prediction Model

Lecture 151 Making Future Predictions

Section 48: Case Study 19 – Time Series Forecasting for Sales

Lecture 152 Case Study 19 – Time Series Forecasting for Sales

Section 49: Case Study 20 – Predicting Insurance Premiums

Lecture 153 Understanding the Problem + Exploratory Data Analysis and Visualizations

Lecture 154 Data Preparation and Machine Learning Modeling

Section 50: Case Study 21 – A/B Testing

Lecture 155 Understanding the Problem + Exploratory Data Analysis and Visualizations

Lecture 156 A/B Test Result Analysis

Lecture 157 A/B Testing a Worked Real Life Example – Designing an A/B Test

Lecture 158 Statistical Power and Significance

Lecture 159 Analysis of A/B Test Resutls

Section 51: Covid-19 Data Analysis and Flourish Bar Chart Race Visualization

Lecture 160 Understanding Our Covid-19 Data

Lecture 161 Analysis of the most Recent Data

Lecture 162 World Visualizations

Lecture 163 Analyzing Confirmed Cases in each Country

Lecture 164 Mapping Covid-19 Cases

Lecture 165 Animating our Maps

Lecture 166 Comparing Countries and Continents

Lecture 167 Flourish Bar Chart Race – 1

Lecture 168 Flourish Bar Chart Race – 2

Begineers to Data Anaysis,Business Analysts who wish to do more with their data,College graduates who lack real worlde experience,Business oriented persons (Management or MBAs) who’d like to use data to enhance their skills,Software Developers or Engineers who’d like to move into a Data Analyst Career,Anyone looking to understand Data and uncover insights,Those looking for a good foundation before starting a Data Science Masters/Bootcamp

#### Course Information:

Udemy | English | 21h 6m | 9.63 GB

Created by: Rajeev D. Ratan

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