Udemy

Platform

English

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Data & Analytics

Category

Data Analysis Bootcamp™ 21 Real World Case Studies

Gain Business Intelligence Skills using Statistics, Data Wrangling, Data Science, Visualizations & Google Data Studio

4.35 (503 reviews)

Students

15 hours

Content

Apr 2020

Last Update
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What you will 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


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:

  1. The Importance of Data Analytics

  2. Python Crash Course

  3. Data Manipulations and Wrangling with Pandas

  4. Probability and Statistics

  5. Hypothesis Testing

  6. Data Visualization

  7. Geospatial Data Visualization

  8. Story Telling with Data

  9. Google Data Studio Dashboard Design - Complete Course

  10. Machine Learning - Supervised Learning

  11. Machine Learning - Unsupervised Learning (Clustering)

  12. Practical Analytical Case Studies

Google Data Studio Dashboard & Visualization Project:

  1. Executive Sales Dashboard (Google Data Studio)

Python, Pandas & Data Analytics and Data Science Case Studies:

  1. Health Care Analytics & Diabetes Prediction

  2. Africa Economic, Banking & Systematic Crisis Data

  3. Election Poll Analytics

  4. Indian Election 2009 vs 2014

  5. Supply-Chain for Shipping Data Analytics

  6. Brent Oil Prices Analytics

  7. Olympics Analysis - The Greatest Olympians

  8. Home Advantage Analysis in Basketball and Soccer

  9. IPL Cricket Data Analytics

  10. Predicting the Soccer World Cup

  11. Pizza Resturant Analytics

  12. Bar and Pub Analytics

  13. Retail Product Sales Analytics

  14. Customer Clustering

  15. Marketing Analytics - What Drives Ad Performance

  16. Text Analytics - Airline Tweets (Word Clusters)

  17. Customer Lifetime Values

  18. Time Series Forecasting - Demand/Sales Forecast

  19. Airbnb Sydney Exploratory Data Analysis

  20. A/B Testing





Screenshots

Data Analysis Bootcamp™ 21 Real World Case Studies
Data Analysis Bootcamp™ 21 Real World Case Studies
Data Analysis Bootcamp™ 21 Real World Case Studies
Data Analysis Bootcamp™ 21 Real World Case Studies

Content

Course Introduction & the Importance of Data Analysts

Course Introduction

The Importance of Data Analyst

Why Data is the new Oil

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

The Roles in the Data World - Analyst, Engineer, Scientist, Statistician, DevOps

Download Code and Slides and Setup Google Colab

Download Code and Slides

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

Python Crash Course

Why use Python for Data Anakytics and Data Science?

Python - Basic Variables

Python - Array/Lists and Dictionaries

Python - Conditional Statements

Python - Loops

Python - Functions

Python - Classes

Pandas - Data Series and Manipulation

Introduction to Pandas

Pandas 1 - Data Series

Pandas 2A - DataFrames - Index, Slice, Stats, Finding Empty cells, Filtering

Pandas 2B - DataFrames - Index, Slice, Stats, Finding Empty cells & Filtering

Pandas - Data Cleaning & Aggregration

Pandas 3B - Data Cleaning - Alter Colomns/Rows, Missing Data & String Operations

Pandas 3A - Data Cleaning - Alter Colomns/Rows, Missing Data & String Operations

Pandas 4 - Data Aggregation - GroupBy, Map, Pivot, Aggreate Functions

Pandas - Feature Engineering & Joins/Merge/Concatenating

Pandas 5 - Feature Engineer, Lambda and Apply

Pandas 6 - Concatenating, Merging and Joinining

Pandas - Time Series Data

Pandas 7 - Time Series Data

Advanced Pandas

Pandas 7 - ADVANCED Operations - Iterows, Vectorization and Numpy

Pandas 8 - ADVANCED Operations - More Map, Zip and Apply

Pandas 9 - Advanced Operations - Parallel Processing

Map Visualizations

Map Visualizations with Plotly - Cloropeths from Scratch - USA and World

Map Visualizations with Plotly - Heatmaps, Scatter Plots and Lines

Statistics for Data Analysts & Visualizations

Introduction to Statistics

Descriptive Statistics - Why Statistical Knowledge is so Important

Descriptive Statistics 1 - Exploratory Data Analysis (EDA) & Visualizations

Descriptive Statistics 2 - Exploratory Data Analysis (EDA) & Visualizations

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

Variance, Standard Deviation and Bessel’s Correction

Types of Variables - Quantitive and Qualitative

Frequency Distributions

Frequency Distributions Shapes

Analyzing Frequency Distributions - What is the Best Type of Wine? Red or White?

Covariance & Correlation - Do Amazon & Google know you better than anyone else?

Sampling - Sample Sizes & Confidence Intervals - What Can You Trust?

Mean, Mode and Median - Not as Simple As You'd Think

The Normal Distribution & the Central Limit Theorem

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

Z-Scores

Probability Theory

Probability - An Introduction

Estimating Probability

Addition Rule

Permutations & Combinations

Bayes Theorem

Hypothesis Testing

Hypothesis Testing Introduction

Statistical Significance

Hypothesis Testing – P Value

Hypothesis Testing – Pearson Correlation

Google Data Studio - Introduction & Setup

All about Google Data Studio

Opening Google Data Studio and Uploading Data

Google Data Studio - Your First Dashboard

Your First Dashboard Part 1

Your First Dashboard Part 2

Creating New Fields

Google Data Studio - Pivot & Dynamic Tables (with Filters)

Pivot Tables

Dynamic Filtered Tables

Google Data Studio - Scorecards and Time Comparison

Scorecards

Scorecards with Time Comparison

Google Data Studio - Bar Charts, Line Charts and Time Series Plots

Bar Charts

Line Charts

Time Series and Comparitive Time Series Plots

Google Data Studio - Pie charts, Donut Charts, Treemaps & Scatter Plots

Pie Charts, Donut Charts and Tree Maps

Scatter Plots

Google Data Studio - Geographic & Map Plots

Google Data Studio - Geographic & Map Plots

Google Data Studio - Bullet and Line Area Plots

Google Data Studio - Scatter Plots

Google Data Studio - Sharing your Interactive Dashboards

Google Data Studio - Sharing your Interactive Dashboards

Retail Sales Dashboard for Executives

Homework Project - Create Executive Sales Dashboard

Introduction to Machine Learning

How Machine Learning enables Computers to Learn

What is a Machine Learning Model?

Types of Machine Learning

Linear Regressions

Linear Regression – Introduction to Cost Functions and Gradient Descent

Linear Regressions in Python from Scratch and using Sklearn

Polynomial and Multivariate Linear Regression

Classification - Logistic Regression, SVM, Decision Trees, Random Forets & KNN

Logistic Regression

Support Vector Machines (SVMs)

Decision Trees and Random Forests & the Gini Index

K-Nearest Neighbors (KNN)

Assessing Model Performance

Assessing Performance – Confusion Matrix, Precision and Recall

Understanding the ROC and AUC Curve

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

Neural Networks Overview

Introduction to Neural Networks

Types of Deep Learning Algoritms CNNs, RNNs & LSTMs

Unsupervised Learning

Introduction to Unsupervised Learning

K-Means Clustering

Choosing K – Elbow Method & Silhouette Analysis

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

Dimensionality Reduction

Principal Component Analysis

t-Distributed Stochastic Neighbor Embedding (t-SNE)

PCA & t-SNE in Python with Visualization Comparisons

Case Study 1 - Airbnb Sydney Exploratory Data Analysis

Case Study Note

Understanding the Problem + Exploratory Data Analysis and Visualizations

Case Study 2 - Retail Product Sales Analytics

Data Cleaning and Preparation

Sales and Revenue Analysis

Analysis per Country, Repeat Customers and Items

Case Study 3 - Marketing Analytics - What Drives Ad Performance

Understanding the Problem + Exploratory Data Analysis and Visualizations

Data Preparation and Machine Learning Modeling

Case Study 4 - Customer Clustering for Travel Agency Customers

Data Exploration & Description

Simple Exploratory Data Analysis and Visualizations

Feature Engineering

K-Means Clustering of Customer Data

Cluster Analysis

Case Study 5 - Text Analytics - Airline Tweets (Word Clusters)

Case Study 5 - Text Analytics - Airline Tweets (Word Clusters)

Case Study 6 - Customer Lifetime Value (CLV)

Understanding the Problem + Exploratory Data Analysis and Visualizations

Customer Lifetime Value Modeling

Case Study 7 - Health Care Analytics - Predict Diabetes

Healthcare Analytics - Predict Diabetes

Case Study 8 - Africa Economic, Banking & Systematic Crisis Data

Case Study 8 - Africa Economic, Banking & Systematic Crisis Data

Case Study 9 - 2016 US President Election Analysis

Case Study 9 - Election Polls - Making Better Predictions

Case Study 10 - Election Results Analysis - Indian Election 2009 vs 2014

Case Study 10 - Election Results Analysis - Indian Election 2009 vs 2014

Case Study 11 - Supply-Chain for Shipping Data Analytics

Case Study 11 - Supply-Chain for Shipping Data Analytics

Case Study 12 - Sports Analytics - Olypmics Analysis - The Greatest Olympians

Case Study 12 - Sports Analytics - Olypmics Analysis - The Greatest Olympians

Case Study 13 - Home Advantage Analysis in Basketball and Soccer

Case Study 13 - Home Advantage Analysis in Basketball and Soccer

Case Study 14 - IPL Cricket Data Analytics

Case Study 14 - IPL Cricket Data Analytics

Case Study 15 - Using Data to predict the Football (Soccer) World Cup Winner

Case Study 15 - Using Data to predicting the Football (Soccer) World Cup

Case Study 16 - Pizza Resturants Analysis

Case Study 16 - Pizza Resturants Analysis

Case Study 17 - Brewery and Pub Analysis

Case Study 17 - Bar and Pub Analysis

Case Study 18 - EDA and Forecasting Brent Oil Prices

Case Study 18 - EDA and Forecasting Brent Oil Prices

Case Study 19 - Time Series Forecasting for Sales

Case Study 19 - Time Series Forecasting for Sales

Case Study 20 - Predicting Insurance Premiums

Understanding the Problem + Exploratory Data Analysis and Visualizations

Data Preparation and Machine Learning Modeling

Case Study 21 – A/B Testing

Understanding the Problem + Exploratory Data Analysis and Visualizations

A/B Test Result Analysis

A/B Testing a Worked Real Life Example - Designing an A/B Test

Statistical Power and Significance

Analysis of A/B Test Resutls


Reviews

A
Audrey19 September 2020

The demo in the lecture video clip is close to impossible to follow with the lecturer scrolling up and down jumping between notebook cells all the time. If there is no video download allowed, at least provide download of lesson slide deck.

G
Goodluck15 September 2020

The course content, teacher, and presentation method are highly promising so far. I am happy that I chose this course. Quite eager to proceed.

J
Jacqueline3 July 2020

He starts with the basics, and makes it all easy to understand. I am confident that I have the skills for this course, and will succeed, based on how the presentations are progressing.

S
Sampath2 July 2020

The title says 21 case studies but in reality, only 7 case studies video are available. Not happy with the course.

T
Tiaan13 May 2020

This course was a very good match for me as it is exactly my interests and also in line with my field of work.

K
K.1 May 2020

The narrator seems to be new in teaching, explanation is good in some parts but rushes through other parts. I would recommend only if you know Python else you will find this difficult to follow.

B
Brenda27 February 2020

So far the instructor is doing a great job on introducing the course and explaining the what and why of data analysis.

S
Subhojit27 February 2020

This is a course not at all worth doing. The author is showing EDA with explaining what is the interpretation of it. Maximum case studies doesn't have any video, only the code without explanation. Spent too much of time on mundane stuff and hardly showed any real application. For example, shown linear regression without showing p value, adjusted R square, residual analysis, etc. Technical part should have been covered along with the interpretation of the result.

J
Jayasree5 February 2020

Yes, I am interested to know this new jorgan. Data collection, extract, transform, load, classification, saving in databases, warehouses for easy access, data query, data mining ..... is the path I travelled so far. .... with not too much to learn about where it is used, with not even an option to know the application as such. Now Data Analysis (hope something close to Business Analysis ???). Ready to take this ride ... YeaaaaaaHaaa !! But the new set of tools is bit intimidating . let me see. Thanks for the opportunity to express my expectation.

S
Sridhar23 January 2020

Lot of knowledge wrapped into few case studies, which are very beginner friendly and can be easily applied to other datasets. However, if you took Rajeev's other course "DataScience and DeepLearning for Business", you will find some repetition of the case studies, However Number of case studies which are new is much more than the repeated ones. Note to Rajeev: I think your "Case Study Note" appearning in Case Study 1, can be moved to the beginning of the course, as i found the relevance of it, in the very beginning itself. and Most Importantly, Thank you very much, for simplifying a complicated subject and making it beginner friendly, without overwhelming them.

Y
Yash11 January 2020

I am on 18th video yet and so far it has been going amazingly well! Very satisfied. Although, I must say that I had prior experience of using Pandas with Python it may be very much possible that I am able to fully understand it. For newbies it could be a bit fast but nothing that can't be googled to overcome conceptual difficulties.

S
Sadiqqua2 January 2020

Got this course to help with my BSc final year data sci project - I need to make a dashboard and have good visualisations. My project supervisor at school is not helpful and he hardly responds to my questions I was frustrated!! Today I asked the instructor for this course a question about Google data studio - i got a good helpful response! Great responsive instructor! This course is all I need to finish my project. It's very easy to follow because I do not have a good background on data science, my bsc is mainly on networking - very different to my project.

L
Li2 January 2020

Excellent course, learning a lot so far! The case studies look extremely exciting and present really good learning opportunities

A
Avery2 January 2020

Sports are my thing and the projects have IPL cricket and football worldcup predictions. I chose this course for the projects! I did basic data analytics in other courses (in school) but it was the typical projects - titanic, iris and the most adventurous data we played with was some basic netflix data. I browsed the projects, they are very detailed & step-by-step. Can't wait to do them! I have only looked at a few videos so far because of my limited time with work but I will definitely be doing all these projects!!!

T
Tobias2 January 2020

Really really well taught! Love the detailed explanations, good use of diagrams and the incredible projects/case studies! Really happy I purchased this course


Coupons

DateDiscountStatus
2/29/202095% OFFExpired

2694796

Udemy ID

12/8/2019

Course created date

1/4/2020

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