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Data Analysis Bootcamp™ 21 Real World Case Studies

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Data Analysis Bootcamp™ 21 Real World Case Studies, available at $79.99, has an average rating of 4.48, with 168 lectures, based on 1116 reviews, and has 9799 subscribers.

You will learn about 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 This course is ideal for individuals who are Begineers to Data Anaysis or Business Analysts who wish to do more with their data or College graduates who lack real worlde experience or Business oriented persons (Management or MBAs) who'd like to use data to enhance their skills or Software Developers or Engineers who'd like to move into a Data Analyst Career or Anyone looking to understand Data and uncover insights or Those looking for a good foundation before starting a Data Science Masters/Bootcamp It is particularly useful for Begineers to Data Anaysis or Business Analysts who wish to do more with their data or College graduates who lack real worlde experience or Business oriented persons (Management or MBAs) who'd like to use data to enhance their skills or Software Developers or Engineers who'd like to move into a Data Analyst Career or Anyone looking to understand Data and uncover insights or Those looking for a good foundation before starting a Data Science Masters/Bootcamp.

Enroll now: Data Analysis Bootcamp™ 21 Real World Case Studies

Summary

Title: Data Analysis Bootcamp™ 21 Real World Case Studies

Price: $79.99

Average Rating: 4.48

Number of Lectures: 168

Number of Published Lectures: 168

Number of Curriculum Items: 168

Number of Published Curriculum Objects: 168

Original Price: $199.99

Quality Status: approved

Status: Live

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

Who Should Attend

  • 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

Target Audiences

  • 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

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 setsand 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

Course Curriculum

Chapter 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

Chapter 2: Download Code and Slides and Setup Google Colab

Lecture 1: Download Code and Slides

Lecture 2: Download Course Code, Slides and Setup Google Colab for your iPython Notebooks

Chapter 3: Python Crash Course

Lecture 1: Why use Python for Data Anakytics and Data Science?

Lecture 2: Python – Basic Variables

Lecture 3: Python – Array/Lists and Dictionaries

Lecture 4: Python – Conditional Statements

Lecture 5: Python – Loops

Lecture 6: Python – Functions

Lecture 7: Python – Classes

Chapter 4: Pandas – Data Series and Manipulation

Lecture 1: Introduction to Pandas

Lecture 2: Pandas 1 – Data Series

Lecture 3: Pandas 2A – DataFrames – Index, Slice, Stats, Finding Empty cells, Filtering

Lecture 4: Pandas 2B – DataFrames – Index, Slice, Stats, Finding Empty cells & Filtering

Chapter 5: Pandas – Data Cleaning & Aggregration

Lecture 1: Pandas 3B – Data Cleaning – Alter Colomns/Rows, Missing Data & String Operations

Lecture 2: Pandas 3A – Data Cleaning – Alter Colomns/Rows, Missing Data & String Operations

Lecture 3: Pandas 4 – Data Aggregation – GroupBy, Map, Pivot, Aggreate Functions

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

Lecture 1: Pandas 5 – Feature Engineer, Lambda and Apply

Lecture 2: Pandas 6 – Concatenating, Merging and Joinining

Chapter 7: Pandas – Time Series Data

Lecture 1: Pandas 7 – Time Series Data

Chapter 8: Advanced Pandas

Lecture 1: Pandas 7 – ADVANCED Operations – Iterows, Vectorization and Numpy

Lecture 2: Pandas 8 – ADVANCED Operations – More Map, Zip and Apply

Lecture 3: Pandas 9 – Advanced Operations – Parallel Processing

Chapter 9: Map Visualizations

Lecture 1: Map Visualizations with Plotly – Cloropeths from Scratch – USA and World

Lecture 2: Map Visualizations with Plotly – Heatmaps, Scatter Plots and Lines

Chapter 10: Statistics for Data Analysts & Visualizations

Lecture 1: Introduction to Statistics

Lecture 2: Descriptive Statistics – Why Statistical Knowledge is so Important

Lecture 3: Descriptive Statistics 1 – Exploratory Data Analysis (EDA) & Visualizations

Lecture 4: Descriptive Statistics 2 – Exploratory Data Analysis (EDA) & Visualizations

Lecture 5: Sampling, Averages & Variance And How to lie and Mislead with Statistics

Lecture 6: Variance, Standard Deviation and Bessel’s Correction

Lecture 7: Types of Variables – Quantitive and Qualitative

Lecture 8: Frequency Distributions

Lecture 9: Frequency Distributions Shapes

Lecture 10: Analyzing Frequency Distributions – What is the Best Type of Wine? Red or White?

Lecture 11: Covariance & Correlation – Do Amazon & Google know you better than anyone else?

Lecture 12: Sampling – Sample Sizes & Confidence Intervals – What Can You Trust?

Lecture 13: Mean, Mode and Median – Not as Simple As You'd Think

Lecture 14: The Normal Distribution & the Central Limit Theorem

Lecture 15: Lying with Correlations – Divorce Rates in Maine caused by Margarine Consumption

Lecture 16: Z-Scores

Chapter 11: Probability Theory

Lecture 1: Probability – An Introduction

Lecture 2: Estimating Probability

Lecture 3: Addition Rule

Lecture 4: Permutations & Combinations

Lecture 5: Bayes Theorem

Chapter 12: Hypothesis Testing

Lecture 1: Hypothesis Testing Introduction

Lecture 2: Statistical Significance

Lecture 3: Hypothesis Testing – P Value

Lecture 4: Hypothesis Testing – Pearson Correlation

Chapter 13: Google Data Studio – Introduction & Setup

Lecture 1: All about Google Data Studio

Lecture 2: Opening Google Data Studio and Uploading Data

Chapter 14: Google Data Studio – Your First Dashboard

Lecture 1: Your First Dashboard Part 1

Lecture 2: Your First Dashboard Part 2

Lecture 3: Creating New Fields

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

Lecture 1: Pivot Tables

Lecture 2: Dynamic Filtered Tables

Chapter 16: Google Data Studio – Scorecards and Time Comparison

Lecture 1: Scorecards

Lecture 2: Scorecards with Time Comparison

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

Lecture 1: Bar Charts

Lecture 2: Line Charts

Lecture 3: Time Series and Comparitive Time Series Plots

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

Lecture 1: Pie Charts, Donut Charts and Tree Maps

Lecture 2: Scatter Plots

Chapter 19: Google Data Studio – Geographic & Map Plots

Lecture 1: Google Data Studio – Geographic & Map Plots

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

Lecture 1: Google Data Studio – Scatter Plots

Chapter 21: Google Data Studio – Sharing your Interactive Dashboards

Lecture 1: Google Data Studio – Sharing your Interactive Dashboards

Chapter 22: Retail Sales Dashboard for Executives

Lecture 1: Homework Project – Create Executive Sales Dashboard

Chapter 23: Introduction to Machine Learning

Lecture 1: How Machine Learning enables Computers to Learn

Lecture 2: What is a Machine Learning Model?

Lecture 3: Types of Machine Learning

Chapter 24: Linear Regressions

Lecture 1: Linear Regression – Introduction to Cost Functions and Gradient Descent

Instructors

Rating Distribution

  • 1 stars: 27 votes
  • 2 stars: 26 votes
  • 3 stars: 129 votes
  • 4 stars: 364 votes
  • 5 stars: 570 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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