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30-Day Money-Back Guarantee

This course includes:

  • 15 hours on-demand video
  • 16 articles
  • 3 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
Business Business Analytics & Intelligence Data Analysis

Data Analysis Bootcamp™ 21 Real World Case Studies

Gain Business Intelligence Skills using Statistics, Data Wrangling, Data Science, Visualizations & Google Data Studio
Rating: 4.4 out of 54.4 (375 ratings)
3,555 students
Created by Rajeev D. Ratan
Last updated 4/2020
English
English [Auto]
30-Day Money-Back Guarantee

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
Curated for the Udemy for Business collection

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

Course content

50 sections • 129 lectures • 14h 55m total length

  • Preview07:22
  • Preview09:53
  • Preview06:37
  • Making Sense of Buzz Words, Data Science, Big Data, Machine & Deep Learning
    07:27
  • The Roles in the Data World - Analyst, Engineer, Scientist, Statistician, DevOps
    04:50

  • Download Code and Slides
    00:05
  • Download Course Code, Slides and Setup Google Colab for your iPython Notebooks
    03:45

  • Why use Python for Data Anakytics and Data Science?
    03:05
  • Python - Basic Variables
    06:31
  • Python - Array/Lists and Dictionaries
    11:09
  • Python - Conditional Statements
    06:55
  • Python - Loops
    08:49
  • Python - Functions
    05:29
  • Python - Classes
    08:34

  • Introduction to Pandas
    02:47
  • Pandas 1 - Data Series
    07:02
  • Pandas 2A - DataFrames - Index, Slice, Stats, Finding Empty cells, Filtering
    17:31
  • Pandas 2B - DataFrames - Index, Slice, Stats, Finding Empty cells & Filtering
    05:51

  • Pandas 3B - Data Cleaning - Alter Colomns/Rows, Missing Data & String Operations
    08:46
  • Pandas 3A - Data Cleaning - Alter Colomns/Rows, Missing Data & String Operations
    19:45
  • Pandas 4 - Data Aggregation - GroupBy, Map, Pivot, Aggreate Functions
    16:39

  • Pandas 5 - Feature Engineer, Lambda and Apply
    04:24
  • Pandas 6 - Concatenating, Merging and Joinining
    15:21

  • Pandas 7 - Time Series Data
    09:56

  • Pandas 7 - ADVANCED Operations - Iterows, Vectorization and Numpy
    11:17
  • Pandas 8 - ADVANCED Operations - More Map, Zip and Apply
    04:29
  • Pandas 9 - Advanced Operations - Parallel Processing
    04:24

  • Map Visualizations with Plotly - Cloropeths from Scratch - USA and World
    11:25
  • Map Visualizations with Plotly - Heatmaps, Scatter Plots and Lines
    04:54

  • Introduction to Statistics
    04:20
  • Descriptive Statistics - Why Statistical Knowledge is so Important
    03:47
  • Descriptive Statistics 1 - Exploratory Data Analysis (EDA) & Visualizations
    16:19
  • Descriptive Statistics 2 - Exploratory Data Analysis (EDA) & Visualizations
    06:22
  • Sampling, Averages & Variance And How to lie and Mislead with Statistics
    03:16
  • Variance, Standard Deviation and Bessel’s Correction
    09:22
  • Types of Variables - Quantitive and Qualitative
    05:41
  • Frequency Distributions
    04:41
  • Frequency Distributions Shapes
    02:55
  • Analyzing Frequency Distributions - What is the Best Type of Wine? Red or White?
    09:58
  • Covariance & Correlation - Do Amazon & Google know you better than anyone else?
    11:35
  • Sampling - Sample Sizes & Confidence Intervals - What Can You Trust?
    09:41
  • Mean, Mode and Median - Not as Simple As You'd Think
    12:38
  • The Normal Distribution & the Central Limit Theorem
    03:43
  • Lying with Correlations – Divorce Rates in Maine caused by Margarine Consumption
    01:37
  • Z-Scores
    08:16

Instructor

Rajeev D. Ratan
Data Scientist, Computer Vision Expert & Electrical Engineer
Rajeev D. Ratan
  • 4.4 Instructor Rating
  • 6,067 Reviews
  • 35,610 Students
  • 5 Courses

Hi I'm Rajeev, a Data Scientist, and Computer Vision Engineer.  

I have a BSc in Computer & Electrical Engineering and an MSc in Artificial Intelligence from the University of Edinburgh where I gained extensive knowledge of machine learning, computer vision, and intelligent robotics.   

I have published research on using data-driven methods for Probabilistic Stochastic Modeling for Public Transport and even was part of a group that won a robotics competition at the University of Edinburgh. 

I launched my own computer vision startup that was based on using deep learning in education since then I've been contributing to 2 more startups in computer vision domains and one multinational company in Data Science.

Previously, I worked for 8 years at two of the Caribbean’s largest telecommunication operators where he gained experience in managing technical staff and deploying complex telecommunications projects.

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