Data Science with TableAU, Power BI, Python and R

Data Science with TableAU, Power BI, Python and R

Data Science with TableAU, Power BI, Python and R

course at a glance

  • Date : 11 Dec - 8 Jan 2020
  • No. of Classes/ Sessions : 10
  • Total Hours : 30
  • Last Date of Registration : 11 Dec 2019
  • Class Schedule :
    • Sunday - 6:30 PM - 9:30 PM
    • Sunday - 6:30 PM - 9:30 PM
    • Wednesday - 6:30 PM - 9:30 PM
    • Wednesday - 6:30 PM - 9:30 PM
  • venue : UY LAB- 31, Mohakhali Commercial Area,Colombia Super Market, 4th Floor (Lift: 5), Wireless Gate, Banani, Dhaka-1213. Contact no: 01906600015 / 01915142133

Price: TK. 12,000
(Excluding VAT & TAX)

This Training is jointly organized by BITM & UY LAB. 
Training will be held in UY LAB.

Data Science with TableAU, Power BI, Python and R :

  1. Lecture 01:Introduction, Installation
    1. Data Visualization with TableAU and Power BI
    2. Introducing to DataSet
    3. Installing Tableau Desktop and Tableau Public and Power BI
    4. Challenge description + view data in file
    5. Connecting Tableau to a Data file - CSV file
    6. Navigating Measures and Dimensions
    7. Creating a calculated field
    8. Adding colours
    9. Adding labels and formatting
    10. Exporting your worksheet 


  1. Lecture 02:Data Mining
    1. Get the Dataset + Project Overview
    2. Connecting to an Excel File
    3. AB test
    4. Working with Aliases
    5. Adding a Reference Line
    6. Looking for anomalies and validating 
    7. Creating bins & Visualizing distributions
    8. Creating a classification test for a numeric variable
    9. Combining two charts and working with them
    10. Validating Data Mining with a Chi-Squared test


  1. Lecture 03:Modeling (Stats Refresher, Simple Linear Regression, Multiple Linear Regression)
    1. Types of variables: Categorical vs Numeric
    2. Types of regressions
    3. Ordinary Least Squares
    4. R-squared
    5. Adjusted R-squared 
    6. Introduction to Gretl
    7. Get the dataset 
    8. Import data and run descriptive statistics
    9. Reading Linear Regression Output
    10. Plotting and analysing the graph 
    11. Caveat: assumptions of a linear regression
    12. Get the dataset
    13. Dummy Variables
    14. Dummy Variable Trap
    15. Ways to build a model: BACKWARD, FORWARD, STEPWISE
    16. Backward Elimination - Practice time
    17. Using Adjusted R-squared to create Robust models
    18. Interpreting coefficients of MLR 


  1. Lecture 04:Modeling (Logistic Regression, Building a robust geodemographic segmentation model)
    1. Binary outcome: Yes/No-Type Business Problems
    2. Logistic regression intuition
    3. Your first logistic regression
    4. False Positives and False Negatives
    5. Confusion Matrix
    6. Interpreting coefficients of a logistic regression 
    7. What is geo-demographic segmenation?
    8. Let's build the model - first iteration
    9. Let's build the model - backward elimination: STEP-BY-STEP
    10. Transforming independent variables
    11. Creating derived variables
    12. Checking for multicollinearity using VIF
    13. Correlation Matrix and Multicollinearity Intuition
    14. Model is Ready and Section Recap 


  1. Lecture 05:Modeling (Assessing your model, Drawing insights from your model, Model maintenance)
    1. Accuracy paradox
    2. Cumulative Accuracy Profile (CAP)
    3. How to build a CAP curve in Excel
    4. Assessing your model using the CAP curve
    5. Get my CAP curve template
    6. How to use test data to prevent overfitting your model
    7. Applying the model to test data
    8. Comparing training performance and test performance 
    9. Power insights from your CAP
    10. Coefficients of a Logistic Regression - Plan of Attack (advanced topic)
    11. Odds ratio (advanced topic)
    12. Odds Ratio vs Coefficients in a Logistic Regression (advanced topic)
    13. Deriving insights from your coefficients (advanced topic) 
    14. What does model deterioration look like?
    15. Why do models deteriorate?
    16. Three levels of maintenance for deployed models 


  1. Lecture 06:Data Preparation (Business Intelligence (BI) Tools, Data Wrangling before the Load)
    1. Working with Data
    2. What is a Data Warehouse? What is a Database?
    3. Setting up Microsoft SQL Server 2014 for practice
    4. Important: Practice Database
    5. ETL for Data Science - what is Extract Transform Load (ETL)?
    6. Microsoft BI Tools: What is SSDT-BI and what are SSIS/SSAS/SSRS? 
    7. Installing SSDT with MSVS Shell 
    8. Preparing your folder structure for your Data Science project
    9. Download the dataset for this section
    10. Two things you HAVE to do before the load
    11. Notepad ++ 
    12. Editpad Lite 


  1. Lecture 07:Data Preparation (Step-by-step guide to uploading data using SSIS, Handling errors during ETL)
    1. Starting and navigating an SSIS Project
    2. Creating a flat file source task and OLE DB destination
    3. Setting up your flat file source connection
    4. Setting up your database connection and creating a RAW table
    5. Run the Upload & Disable
    6. Due Dilligence: Upload Quality Assurance
    7. How excel can mess up your data
    8. Bulletproof Blueprint for Data Wrangling before the Load
    9. SSIS Error: Text qualifier not specified
    10. What do you do when your source file is corrupt?
    11. SSIS Error: Data truncation
    12. Handy trick for finding anomalies in SQL
    13. Automating Error Handling in SSIS: Conditional Split
    14. How to analyze the error files
    15. Due Dilligence: the one thing you HAVE to do every time
    16. Types of Errors in SSIS 


  1. Lecture 08:Data Preparation (SQL Programming For Data Science)
    1. Getting To Know MS SQL Management Studio
    2. Shortcut to upload the data
    3. SELECT * Statement
    4. Using the WHERE clause to filter data
    5. How to use Wildcards / Regular Expressions in SQL (% and _) 
    6. Comments in SQL
    7. Order By
    8. Data Types in SQL
    9. Implicit Data Conversion in SQL
    10. Using Cast() vs Convert()
    11. Working with NULLs
    12. Understanding how LEFT, RIGHT, INNER, and OUTER joins work
    13. Joins with duplicate values
    14. Joining on multiple fields
    15. Practicing Joins 


  1. Lecture 9:Data Preparation (Data Wrangling after the load, Handling errors during ETL)
    1. RAW, WRK, DRV tables
    2. Download the dataset for this section
    3. Create your first Stored Proc in SQL
    4. Executing Stored Procedures
    5. Modifying Stored Procedures
    6. Create table
    7. Insert INTO
    8. Check if table exists + drop table + Truncate
    9. Intermediate Recap - Procs
    10. Create the proc for the second file
    11. Adding leading zeros
    12. Converting data on the fly
    13. How to create a proc template
    14. Archiving Procs 
    15. What you can do with these tables going forward [drv files etc.] 
    16. Download the dataset for this section
    17. Upload the data to RAW table
    18. Create Stored Proc 
    19. How to deal with errors using the isnumeric() function
    20. How to deal errors using the len() function 
    21. How to deal with errors using the isdate() function
    22. Additional Quality Assurance check: Balance
    23. Additional Quality Assurance check: ZipCode
    24. Additional Quality Assurance check: Birthday
    25. ETL Error Handling "Vehicle Service" Project 


  1. Lecture 10:(Projects, Soft skill, Overview)



Course Module Data Science with TableAU, Power BI, Python and R 30 Hrs

Tentative Class Start

11th December, 2019

Available Seat

10 / 24

who can join

  • Professionals from industries such as Information Technology, Banking & Financial Services, Manufacturing, Healthcare, Retail Distribution, & Education
  • Agile Coaches, developers, scrum masters, team leads, project managers, product owners. Anyone who uses Jira Software to manage Agile development.
  • Functional experts from Operations, Quality, Business Excellence, Customer Service, Finance, Engineering, Sales Operations & HR
  • Directors, quality managers and supervisors who are interested in leadership opportunities.
  • Anybody with an interest in Data Science, wants to improve their data mining, statistical modelling, data preparation, Data Science presentation skills
  • Any students in college who want to start a career in Data Science.
  • Any people who are not satisfied with their job and who want to become a Data Scientist.

Meet the Instructor