Data Analysis Bootcamp™ 21 Real World Case Studies

Data Analysis Bootcamp™ 21 Real World Case Studies

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What you will learn

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

Curriculum

Section 1: Course Introduction & the Importance of Data Analysts

Section 2: Download Code and Slides and Setup Google Colab

Section 3: Python Crash Course

Section 4: Pandas - Data Series and Manipulation

Section 5: Pandas - Data Cleaning & Aggregration

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

Section 7: Pandas - Time Series Data

Section 8: Advanced Pandas

Section 9: Map Visualizations

Section 10: Statistics for Data Analysts & Visualizations

Section 11: Probability Theory

Section 12: Hypothesis Testing

Section 13: Google Data Studio - Introduction & Setup

Section 14: Google Data Studio - Your First Dashboard

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

Section 16: Google Data Studio - Scorecards and Time Comparison

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

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

Section 19: Google Data Studio - Geographic & Map Plots

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

Section 21: Google Data Studio - Sharing your Interactive Dashboards

Section 22: Retail Sales Dashboard for Executives

Section 23: Introduction to Machine Learning

Section 24: Linear Regressions

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

Section 26: Assessing Model Performance

Section 27: Neural Networks Overview

Section 28: Unsupervised Learning

Section 29: Dimensionality Reduction

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

Section 31: Case Study 2 - Retail Product Sales Analytics

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

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

Section 34: Case Study 6 - Customer Lifetime Value (CLV)

Section 35: Case Study 20 - Predicting Insurance Premiums

Section 36: Case Study 21 – A/B Testing

Course Description

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

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:

  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




Who this course is for:

  • 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

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