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Software Development / Big Data & Analytics

Data Science A-Z™: Real-Life Data Science Exercises Included

Data Science, Data Analysis, Data Analytics, Data Analyst, Data Mining, Tableau, Statistics, Modeling, SQL, SSIS

Description

Extremely Hands-On... Incredibly Practical... Unbelievably Real! This is not one of those fluffy classes where everything works out just the way it should and your training is smooth sailing. This course throws you into the deep end. In this course you WILL experience firsthand all of the PAIN a Data Scientist goes through on a daily basis. Corrupt data, anomalies, irregularities - you name it! This course will give you a full overview of the Data Science journey. Upon completing this course you will know: How to clean and prepare your data for analysis How to perform basic visualisation of your data How to model your data How to curve-fit your data And finally, how to present your findings and wow the audience This course will give you so much practical exercises that real world will seem like a piece of cake when you graduate this class. This course has homework exercises that are so thought provoking and challenging that you will want to cry... But you won't give up! You will crush it. In this course you will develop a good understanding of the following tools: SQL SSIS Tableau Gretl This course has pre-planned pathways. Using these pathways you can navigate the course and combine sections into YOUR OWN journey that will get you the skills that YOU need. Or you can do the whole course and set yourself up for an incredible career in Data Science. The choice is yours. Join the class and start learning today! See you inside, Sincerely, Kirill Eremenko
Full details

Curriculum

  • Get Excited
    Welcome to Data Science A-Z™
    4:39
  • What is Data Science?
    Intro (what you will learn in this section)
    0:45
    Profession of the future
    6:58
    Areas of Data Science
    5:59
    IMPORTANT: Course Pathways
    5:53
  • --------------------------- Part 1: Visualisation ---------------------------
    Welcome to Part 1
    1:58
  • Introduction to Tableau
    Intro (what you will learn in this section)
    0:29
    Installing Tableau Desktop and Tableau Public (FREE)
    6:09
    Challenge description + view data in file
    2:33
    Connecting Tableau to a Data file - CSV file
    5:18
    Navigating Tableau - Measures and Dimensions
    8:43
    Creating a calculated field
    6:15
    Adding colours
    7:38
    Adding labels and formatting
    11:01
    Exporting your worksheet
    7:41
    Section Recap
    3:35
  • How to use Tableau for Data Mining
    Intro (what you will learn in this section)
    0:45
    Get the Dataset + Project Overview
    7:13
    Connecting Tableau to an Excel File
    3:57
    How to visualise an ad-hoc A-B test in Tableau
    6:30
    Working with Aliases
    4:06
    Adding a Reference Line
    4:54
    Looking for anomalies
    8:36
    Handy trick to validate your approach / data
    9:14
    Section Recap
    5:05
  • Advanced Data Mining With Tableau
    Intro (what you will learn in this section)
    0:45
    Creating bins & Visualizing distributions
    9:56
    Creating a classification test for a numeric variable
    4:26
    Combining two charts and working with them in Tableau
    7:07
    Validating Tableau Data Mining with a Chi-Squared test
    10:30
    Chi-Squared test when there is more than 2 categories
    8:16
    Visualising Balance and Estimated Salary distribution
    11:05
    Bonus: Chi-Squared Test (Stats Tutorial)
    19:13
    Bonus: Chi-Squared Test Part 2 (Stats Tutorial)
    9:11
    Section Recap
    5:45
  • --------------------------- Part 2: Modelling ---------------------------
    Welcome to Part 2
    3:55
  • Stats Refresher
    Intro (what you will learn in this section)
    0:30
    Types of variables: Categorical vs Numeric
    5:27
    Types of regressions
    8:10
    Ordinary Least Squares
    3:12
    R-squared
    5:12
    Adjusted R-squared
    9:57
  • Simple Linear Regression
    Intro (what you will learn in this section)
    0:38
    Introduction to Gretl
    2:35
    Get the dataset
    4:04
    Import data and run descriptive statistics
    4:26
    Reading Linear Regression Output
    6:49
    Plotting and analysing the graph
    4:23
  • Multiple Linear Regression
    Intro (what you will learn in this section)
    1:16
    Caveat: assumptions of a linear regression
    1:48
    Get the dataset
    4:13
    Dummy Variables
    8:06
    Dummy Variable Trap
    2:11
    Ways to build a model: BACKWARD, FORWARD, STEPWISE
    15:42
    Backward Elimination - Practice time
    16:09
    Using Adjusted R-squared to create Robust models
    10:18
    Interpreting coefficients of MLR
    12:48
    Section Recap
    3:09
  • Logistic Regression
    Intro (what you will learn in this section)
    1:35
    Get the dataset
    4:14
    Binary outcome: Yes/No-Type Business Problems
    9:10
    Logistic regression intuition
    17:04
    Your first logistic regression
    8:05
    False Positives and False Negatives
    8:02
    Confusion Matrix
    4:58
    Interpreting coefficients of a logistic regression
    10:04
  • Building a robust geodemographic segmentation model
    Intro (what you will learn in this section)
    1:02
    Get the dataset
    7:33
    What is geo-demographic segmenation?
    5:06
    Let's build the model - first iteration
    8:27
    Let's build the model - backward elimination: STEP-BY-STEP
    11:12
    Transforming independent variables
    10:10
    Creating derived variables
    6:10
    Checking for multicollinearity using VIF
    8:12
    Correlation Matrix and Multicollinearity Intuition
    8:21
    Model is Ready and Section Recap
    6:28
  • Assessing your model
    Intro (what you will learn in this section)
    0:38
    Accuracy paradox
    2:12
    Cumulative Accuracy Profile (CAP)
    11:17
    How to build a CAP curve in Excel
    14:48
    Assessing your model using the CAP curve
    7:12
    Get my CAP curve template
    6:21
    How to use test data to prevent overfitting your model
    3:35
    Applying the model to test data
    8:10
    Comparing training performance and test performance
    11:34
    Section Recap
    3:34
  • Drawing insights from your model
    Intro (what you will learn in this section)
    0:35
    Power insights from your CAP
    13:53
    Coefficients of a Logistic Regression - Plan of Attack (advanced topic)
    3:48
    Odds ratio (advanced topic)
    8:30
    Odds Ratio vs Coefficients in a Logistic Regression (advanced topic)
    7:09
    Deriving insights from your coefficients (advanced topic)
    13:16
    Section Recap
    3:27
  • Model maintenance
    Intro (what you will learn in this section)
    0:38
    What does model deterioration look like?
    4:37
    Why do models deteriorate?
    15:27
    Three levels of maintenance for deployed models
    8:22
    Section Recap
    1:39
  • --------------------------- Part 3: Data Preparation ---------------------------
    Welcome to Part 3
    2:25
  • Business Intelligence (BI) Tools
    Intro (what you will learn in this section)
    0:24
    Working with Data
    1:16
    What is a Data Warehouse? What is a Database?
    3:29
    Setting up Microsoft SQL Server 2014 for practice
    8:06
    Important: Practice Database
    9:45
    ETL for Data Science - what is Extract Transform Load (ETL)?
    2:02
    Microsoft BI Tools: What is SSDT-BI and what are SSIS/SSAS/SSRS ?
    4:05
    Installing SSDT with MSVS Shell
    4:25
  • ETL Phase 1: Data Wrangling before the Load
    Intro (what you will learn in this section)
    0:49
    Preparing your folder structure for your Data Science project
    2:21
    Download the dataset for this section
    1:28
    Two things you HAVE to do before the load
    4:57
    Notepad ++
    1:01
    Editpad Lite
    1:11
  • ETL Phase 2: Step-by-step guide to uploading data using SSIS
    Intro (what you will learn in this section)
    0:51
    Starting and navigating an SSIS Project
    1:47
    Creating a flat file source task and OLE DB destination
    1:54
    Setting up your flat file source connection
    6:09
    Setting up your database connection and creating a RAW table
    7:44
    Run the Upload & Disable
    2:40
    Due Dilligence: Upload Quality Assurance
    2:03
  • Handling errors during ETL (Phases 1 & 2)
    Intro (what you will learn in this section)
    0:51
    Download the dataset for this section
    0:47
    How excel can mess up your data
    3:47
    Bulletproof Blueprint for Data Wrangling before the Load
    7:14
    SSIS Error: Text qualifier not specified
    7:16
    What do you do when your source file is corrupt? (Part 1)
    18:02
    What do you do when your source file is corrupt? (Part 2)
    6:10
    SSIS Error: Data truncation
    15:57
    Handy trick for finding anomalies in SQL
    3:46
    Automating Error Handling in SSIS: Conditional Split Preview
    8:21
    Automating Error Handling in SSIS: Conditional Split (Level 2)
    9:04
    How to analyze the error files
    16:41
    Due Dilligence: the one thing you HAVE to do every time
    4:58
    Types of Errors in SSIS
    4:01
    Summary
    19:07
    Homework
    3:40
  • SQL Programming for Data Science
    Intro (what you will learn in this section)
    0:32
    Download the dataset for this section
    0:39
    Getting To Know MS SQL Management Studio
    2:15
    Shortcut to upload the data
    4:21
    SELECT * Statement
    5:53
    Using the WHERE clause to filter data
    5:51
    How to use Wildcards / Regular Expressions in SQL (% and _)
    4:39
    Comments in SQL
    2:44
    Order By
    5:50
    Data Types in SQL
    7:55
    Implicit Data Conversion in SQL
    3:36
    Using Cast() vs Convert()
    3:52
    Working with NULLs
    5:04
    Understanding how LEFT, RIGHT, INNER, and OUTER joins work
    6:19
    Joins with duplicate values
    2:33
    Joining on multiple fields
    5:22
    Practicing Joins
    5:01
  • ETL Phase 3: Data Wrangling after the load
    Intro (what you will learn in this section)
    0:58
    RAW, WRK, DRV tables
    5:55
    Download the dataset for this section
    1:33
    Create your first Stored Proc in SQL
    3:31
    Executing Stored Procedures
    2:50
    Modifying Stored Procedures
    8:26
    Create table
    9:31
    Insert INTO Preview
    5:43
    Check if table exists + drop table + Truncate
    6:00
    Intermediate Recap - Procs
    4:17
    Create the proc for the second file
    11:37
    Adding leading zeros
    7:30
    Converting data on the fly
    10:22
    How to create a proc template
    7:53
    Archiving Procs
    4:39
    What you can do with these tables going forward [drv files etc.]
    13:51
  • Handling errors during ETL (Phase 3)
    Intro (what you will learn in this section)
    0:54
    Download the dataset for this section
    0:47
    Upload the data to RAW table
    11:03
    Create Stored Proc
    5:09
    How to deal with errors using the isnumeric() function
    7:46
    How to deal errors using the len() function
    7:37
    How to deal with errors using the isdate() function
    7:41
    Additional Quality Assurance check: Balance
    3:52
    Additional Quality Assurance check: ZipCode Preview
    3:18
    Additional Quality Assurance check: Birthday
    4:09
    Part Completed
    9:54
    ETL Error Handling "Vehicle Service" Project
    7:46
  • --------------------------- Part 4: Communication ---------------------------
    Welcome to Part 4
    1:32
  • Working with people
    Intro (what you will learn in this section)
    0:45
    Cross-departmental Work
    4:14
    Come to me with a Business Problem
    2:11
    Setting expectations and pre-project communication
    3:31
    Go and sit with them
    5:21
    The art of saying "No"
    5:25
    Sometimes you have to go to the top
    2:38
    Building a data culture
    5:08
  • Presenting for Data Scientists
    Intro (what you will learn in this section)
    1:43
    Case study
    2:01
    Analysing the intro
    3:34
    Intro dissection - recap
    9:27
    REAL Data Science Presentation Walkthrough - Make Your Audience Say "WOW"
    16:30
    My brainstorming method
    3:04
    How to present to executives
    5:28
    The truth is not always pretty
    2:46
    Bonus: my full presentation | LIVE 2015
    2:00
    Bonus: links to other examples of good storytelling
    16:21
  • Homework Solutions
    Advanced Data Mining with Tableau: Visualising Credit Score & Tenure
    5:45
    Advanced Data Mining with Tableau: Chi-Squared Test for Country
    4:19

Skills

  • Big Data
  • Data Science
  • Data Wrangling
  • Statistics
  • Structured Query Language (SQL)

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