Real-Life Data Science Exercises Included
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       Video Length : 34h40m0s
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       Tasks Number : 231
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       Students Enrolled : 790
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       Medium Level
  • Curriculum
  • 1. Get Excited
    • videocam
      Welcome to Data Science A-Z
      10m0s
  • 2. What is Data Science?
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      Intro (what you will learn in this section)
      10m0s
    • videocam
      Profession of the future
      10m0s
    • videocam
      Updates on Udemy Reviews
      10m0s
    • videocam
      Areas of Data Science
      10m0s
    • videocam
      IMPORTANT: Course Pathways
      10m0s
    • videocam
      Some Additional Resources!!
      10m0s
  • 3. Part 1: Visualisation
    • videocam
      Introduction to Tableau
      10m0s
    • videocam
      Intro (what you will learn in this section)
      10m0s
    • videocam
      Installing Tableau Desktop and Tableau Public (FREE)
      10m0s
    • videocam
      Challenge description + view data in file
      10m0s
    • videocam
      Connecting Tableau to a Data file - CSV file
      10m0s
    • videocam
      Navigating Tableau - Measures and Dimensions
      10m0s
    • videocam
      Creating a calculated field
      10m0s
    • videocam
      Adding colours
      10m0s
    • videocam
      Adding labels and formatting
      10m0s
    • videocam
      Exporting your worksheet
      10m0s
    • videocam
      Section Recap
      10m0s
    • videocam
      Tableau Basics
      10m0s
  • 4. How to use Tableau for Data Mining
    • videocam
      Intro (what you will learn in this section)
      10m0s
    • videocam
      Get the Dataset + Project Overview
      10m0s
    • videocam
      Connecting Tableau to an Excel File
      10m0s
    • videocam
      How to visualise an ad-hoc A-B test in Tableau
      10m0s
    • videocam
      Working with Aliases
      10m0s
    • videocam
      Adding a Reference Line
      10m0s
    • videocam
      Looking for anomalies
      10m0s
    • videocam
      Handy trick to validate your approach / data
      10m0s
    • videocam
      Section Recap
      10m0s
  • 5. Advanced Data Mining With Tableau
    • videocam
      Intro (what you will learn in this section)
      10m0s
    • videocam
      Creating bins & Visualizing distributions
      10m0s
    • videocam
      Creating a classification test for a numeric variable
      10m0s
    • videocam
      Combining two charts and working with them in Tableau
      10m0s
    • videocam
      Validating Tableau Data Mining with a Chi-Squared test
      10m0s
    • videocam
      Chi-Squared test when there is more than 2 categories
      10m0s
    • videocam
      Visualising Balance and Estimated Salary distribution
      10m0s
    • videocam
      Bonus: Chi-Squared Test (Stats Tutorial)
      10m0s
    • videocam
      Bonus: Chi-Squared Test Part 2 (Stats Tutorial)
      10m0s
    • videocam
      Section Recap
      10m0s
    • videocam
      Part Completed
      10m0s
  • 6. Stats Refresher
    • videocam
      Intro (what you will learn in this section)
      10m0s
    • videocam
      Types of variables: Categorical vs Numeric
      10m0s
    • videocam
      Types of regressions
      10m0s
    • videocam
      Ordinary Least Squares
      10m0s
    • videocam
      R-squared
      10m0s
    • videocam
      Adjusted R-squared
      10m0s
  • 7. Simple Linear Regression
    • videocam
      Intro (what you will learn in this section)
      10m0s
    • videocam
      Introduction to Gretl
      10m0s
    • videocam
      Get the dataset
      10m0s
    • videocam
      Import data and run descriptive statistics
      10m0s
    • videocam
      Reading Linear Regression Output
      10m0s
    • videocam
      Plotting and analysing the graph
      10m0s
  • 8. Multiple Linear Regression
    • videocam
      Intro (what you will learn in this section)
      10m0s
    • videocam
      Caveat: assumptions of a linear regression
      10m0s
    • videocam
      Get the dataset
      10m0s
    • videocam
      Dummy Variables
      10m0s
    • videocam
      Dummy Variable Trap
      10m0s
    • videocam
      Ways to build a model: BACKWARD, FORWARD, STEPWISE
      10m0s
    • videocam
      Backward Elimination - Practice time
      10m0s
    • videocam
      Using Adjusted R-squared to create Robust models
      10m0s
    • videocam
      Interpreting coefficients of MLR
      10m0s
    • videocam
      Section Recap
      10m0s
  • 9. Logistic Regression
    • videocam
      Intro (what you will learn in this section)
      10m0s
    • videocam
      Get the dataset
      10m0s
    • videocam
      Binary outcome: Yes/No-Type Business Problems
      10m0s
    • videocam
      Logistic regression intuition
      10m0s
    • videocam
      Your first logistic regression
      10m0s
    • videocam
      False Positives and False Negatives
      10m0s
    • videocam
      Confusion Matrix
      10m0s
    • videocam
      Interpreting coefficients of a logistic regression
      10m0s
  • 10. Building a robust geodemographic segmentation model
    • videocam
      Intro (what you will learn in this section)
      10m0s
    • videocam
      Get the dataset
      10m0s
    • videocam
      What is geo-demographic segmenation?
      10m0s
    • videocam
      Let's build the model - first iteration
      10m0s
    • videocam
      Let's build the model - backward elimination: STEP-BY-STEP
      10m0s
    • videocam
      Transforming independent variables
      10m0s
    • videocam
      Creating derived variables
      10m0s
    • videocam
      Checking for multicollinearity using VIF
      10m0s
    • videocam
      Correlation Matrix and Multicollinearity Intuition
      10m0s
    • videocam
      Model is Ready and Section Recap
      10m0s
  • 11. Assessing your model
    • videocam
      Intro (what you will learn in this section)
      10m0s
    • videocam
      Accuracy paradox
      10m0s
    • videocam
      Cumulative Accuracy Profile (CAP)
      10m0s
    • videocam
      How to build a CAP curve in Excel
      10m0s
    • videocam
      Assessing your model using the CAP curve
      10m0s
    • videocam
      Get my CAP curve template
      10m0s
    • videocam
      How to use test data to prevent overfitting your model
      10m0s
    • videocam
      Applying the model to test data
      10m0s
    • videocam
      Comparing training performance and test performance
      10m0s
    • videocam
      Section Recap
      10m0s
  • 12. Drawing insights from your model
    • videocam
      Intro (what you will learn in this section)
      10m0s
    • videocam
      Power insights from your CAP
      10m0s
    • videocam
      Coefficients of a Logistic Regression - Plan of Attack (advanced topic)
      10m0s
    • videocam
      Odds ratio (advanced topic)
      10m0s
    • videocam
      Odds Ratio vs Coefficients in a Logistic Regression (advanced topic)
      10m0s
    • videocam
      Deriving insights from your coefficients (advanced topic)
      10m0s
    • videocam
      Section Recap
      10m0s
  • 13. Model maintenance
    • videocam
      Intro (what you will learn in this section)
      10m0s
    • videocam
      What does model deterioration look like?
      10m0s
    • videocam
      Why do models deteriorate?
      10m0s
    • videocam
      Three levels of maintenance for deployed models
      10m0s
    • videocam
      Section Recap
      10m0s
  • 14. Business Intelligence (BI) Tools
    • videocam
      Intro (what you will learn in this section)
      10m0s
    • videocam
      Working with Data
      10m0s
    • videocam
      What is a Data Warehouse? What is a Database?
      10m0s
    • videocam
      Setting up Microsoft SQL Server 2014 for practice
      10m0s
    • videocam
      Important: Practice Database
      10m0s
    • videocam
      ETL for Data Science - what is Extract Transform Load (ETL)?
      10m0s
    • videocam
      Microsoft BI Tools: What is SSDT-BI and what are SSIS/SSAS/SSRS ?
      10m0s
    • videocam
      Installing SSDT with MSVS Shell
      10m0s
  • 15. ETL Phase 1: Data Wrangling before the Load
    • videocam
      Intro (what you will learn in this section)
      10m0s
    • videocam
      Preparing your folder structure for your Data Science project
      10m0s
    • videocam
      Download the dataset for this section
      10m0s
    • videocam
      Two things you HAVE to do before the load
      10m0s
    • videocam
      Notepad ++
      10m0s
    • videocam
      Editpad Lite
      10m0s
  • 16. ETL Phase 2: Step-by-step guide to uploading data using SSIS
    • videocam
      Intro (what you will learn in this section)
      10m0s
    • videocam
      Starting and navigating an SSIS Project
      10m0s
    • videocam
      Creating a flat file source task and OLE DB destination
      10m0s
    • videocam
      Setting up your flat file source connection
      10m0s
    • videocam
      Setting up your database connection and creating a RAW table
      10m0s
    • videocam
      Run the Upload & Disable
      10m0s
    • videocam
      Due Dilligence: Upload Quality Assurance
      10m0s
  • 17. Handling errors during ETL (Phases 1 & 2)
    • videocam
      Intro (what you will learn in this section)
      10m0s
    • videocam
      Download the dataset for this section
      10m0s
    • videocam
      How excel can mess up your data
      10m0s
    • videocam
      Bulletproof Blueprint for Data Wrangling before the Load
      10m0s
    • videocam
      SSIS Error: Text qualifier not specified
      10m0s
    • videocam
      What do you do when your source file is corrupt? (Part 1)
      10m0s
    • videocam
      What do you do when your source file is corrupt? (Part 2)
      10m0s
    • videocam
      SSIS Error: Data truncation
      10m0s
    • videocam
      Handy trick for finding anomalies in SQL
      10m0s
    • videocam
      Automating Error Handling in SSIS: Conditional Split
      10m0s
    • videocam
      Automating Error Handling in SSIS: Conditional Split (Level 2)
      10m0s
    • videocam
      How to analyze the error files
      10m0s
    • videocam
      Due Dilligence: the one thing you HAVE to do every time
      10m0s
    • videocam
      Types of Errors in SSIS
      10m0s
    • videocam
      Summary
      10m0s
    • videocam
      Homework
      10m0s
  • 18. SQL Programming for Data Science
    • videocam
      Intro (what you will learn in this section)
      10m0s
    • videocam
      Download the dataset for this section
      10m0s
    • videocam
      Getting To Know MS SQL Management Studio
      10m0s
    • videocam
      Shortcut to upload the data
      10m0s
    • videocam
      SELECT Statement
      10m0s
    • videocam
      Using the WHERE clause to filter data
      10m0s
    • videocam
      How to use Wildcards / Regular Expressions in SQL (% and _)
      10m0s
    • videocam
      Comments in SQL
      10m0s
    • videocam
      Order By
      10m0s
    • videocam
      Data Types in SQL
      10m0s
    • videocam
      Implicit Data Conversion in SQL
      10m0s
    • videocam
      Using Cast() vs Convert()
      10m0s
    • videocam
      Working with NULLs
      10m0s
    • videocam
      Understanding how LEFT, RIGHT, INNER, and OUTER joins work
      10m0s
    • videocam
      Joins with duplicate values
      10m0s
    • videocam
      Joining on multiple fields
      10m0s
    • videocam
      Practicing Joins
      10m0s
  • 19. ETL Phase 3: Data Wrangling after the load
    • videocam
      Intro (what you will learn in this section)
      10m0s
    • videocam
      RAW, WRK, DRV tables
      10m0s
    • videocam
      Download the dataset for this section
      10m0s
    • videocam
      Create your first Stored Proc in SQL
      10m0s
    • videocam
      Executing Stored Procedures
      10m0s
    • videocam
      Modifying Stored Procedures
      10m0s
    • videocam
      Create table
      10m0s
    • videocam
      Insert INTO
      10m0s
    • videocam
      Check if table exists + drop table + Truncate
      10m0s
    • videocam
      Intermediate Recap - Procs
      10m0s
    • videocam
      Create the proc for the second file
      10m0s
    • videocam
      Adding leading zeros
      10m0s
    • videocam
      Converting data on the fly
      10m0s
    • videocam
      How to create a proc template
      10m0s
    • videocam
      Archiving Procs
      10m0s
    • videocam
      What you can do with these tables going forward [drv files etc.]
      10m0s
  • 20. Handling errors during ETL (Phase 3)
    • videocam
      Intro (what you will learn in this section)
      10m0s
    • videocam
      Download the dataset for this section
      10m0s
    • videocam
      Upload the data to RAW table
      10m0s
    • videocam
      Create Stored Proc
      10m0s
    • videocam
      How to deal with errors using the isnumeric() function
      10m0s
    • videocam
      How to deal errors using the len() function
      10m0s
    • videocam
      How to deal with errors using the isdate() function
      10m0s
    • videocam
      Additional Quality Assurance check: Balance
      10m0s
    • videocam
      Additional Quality Assurance check: ZipCode
      10m0s
    • videocam
      Additional Quality Assurance check: Birthday
      10m0s
    • videocam
      Part Completed
      10m0s
    • videocam
      ETL Error Handling "Vehicle Service" Project
      10m0s
  • 21. Working with people
    • videocam
      Intro (what you will learn in this section)
      10m0s
    • videocam
      Cross-departmental Work
      10m0s
    • videocam
      Come to me with a Business Problem
      10m0s
    • videocam
      Setting expectations and pre-project communication
      10m0s
    • videocam
      Go and sit with them
      10m0s
    • videocam
      The art of saying "No"
      10m0s
    • videocam
      Sometimes you have to go to the top
      10m0s
    • videocam
      Building a data culture
      10m0s
  • 22. Presenting for Data Scientists
    • videocam
      Intro (what you will learn in this section)
      10m0s
    • videocam
      Case study
      10m0s
    • videocam
      Analysing the intro
      10m0s
    • videocam
      Intro dissection - recap
      10m0s
    • videocam
      REAL Data Science Presentation Walkthrough - Make Your Audience Say "WOW"
      10m0s
    • videocam
      My brainstorming method
      10m0s
    • videocam
      How to present to executives
      10m0s
    • videocam
      The truth is not always pretty
      10m0s
    • videocam
      Passion and the Wow-factor
      10m0s
    • videocam
      Bonus: my full presentation | LIVE 2015
      10m0s
    • videocam
      Bonus: links to other examples of good storytelling
      10m0s
  • 23. Homework Solutions
    • videocam
      Advanced Data Mining with Tableau: Visualising Credit Score & Tenure
      10m0s
    • videocam
      Advanced Data Mining with Tableau: Chi-Squared Test for Country
      10m0s
    • videocam
      ETL Error Handling (Phases 1 and 2)
      10m0s
    • videocam
      ETL Error Handling "Vehicle Service" Project (Part 1 of 3)
      10m0s
    • videocam
      ETL Error Handling "Vehicle Service" Project (Part 2 of 3)
      10m0s
    • videocam
      ETL Error Handling "Vehicle Service" Project (Part 3 of 3)
      10m0s
Authors

Kevin Gautama is a systems design and programming engineer with 16 years of expertise in the fields of electrical and electronics and information technology.

He teaches at the Hanoi University of Industry in the period 2003-2011 and he has a certificate of vocational training by the Ministry of Industry and Commerce and the Hanoi University of Industry.

From extensive design experience through numerous engineering projects, the author founded the Enziin Academy.

The Enziin Academy is a startup in the field of educational, it's core goal is to training design engineers in the fields technology related.

The Enziin Academy is headquartered in Stockholm-Sweden with an orientation operating multi-lingual and global.

The author's skills in IT:

  • Implementing the application infrastructure on Amazon's cloud computing platform.
  • Linux server system administration (Sysadmin).
  • Design load balancing and content distribution system.
  • MySQL database administration.
  • C/C++/C# Programming
  • Ruby and Ruby on Rails Programming
  • Python and Django Programming
  • The WPF/C# on the .NET Framework Programming
  • The PHP/JAVA Programming
  • Machine Learning and Expert System.
  • Internet of Things.

The author's skills in the fields of electric and electronic:

  • The design of popular CPU / MCU systems.
  • Design FPGA / CPLD system (Xilinx - Altera).
  • Design and programming of DSP systems (Texas Instruments).
  • Embedded ARM system design.
  • The RTOS Programming
  • Design and programming electronic power systems.
  • PLC - inverter - sensor - electric control cabinet industrial.
  • Control systems distributed connection with Server.

Read more...

Real-Life Data Science Exercises Included


Real-Life Data Science Exercises Included

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