According to the reports, Data Science Analytics job market is projected to grow by 350,000 jobs. Highly qualified trainers at Excelvisor lets you get expertise in Data Science, make you understand all the tools and approach towards problem-solving skills. The entire course teaches you Python, R, Statistics, Machine Learning, Artificial Intelligence, Tableau, Deep Learning, Unix, Git, and SQL. You will be working on real-time use cases, hands-on training, live practice sessions, and industry specify live projects to become Certified Data Science professional.

Benefits of Learning Data Science

  • Data science job is the highest demanding job.

  • Helps to analyze customer behavior and make critical data-driven decisions.

  • Data Scientists performs statistical analysis and develop Machine Learning systems.

  • The course mainly focus on real-life industry projects.

  • In-depth hands-on training program that prepares for various Data Science designations.

  • To learn and explore the subject from experts with rich company background.

  • Top companies hiring Data Scientists - Google, Amazon, Microsoft, IBM, Facebook, Walmart, and many more!

  • Become a Certified Data Science Professional.

  • Best Career Guidance and Job Opportunities, which is constantly shared with you regularly.

  • Mock Exams are conducted to test your skills and prepare you to take real-time exam.

  • Mock interviews are conducted regularly to boost your confidence level.



Part 1: Visualisation

Welcome to Part 1

  • Introduction to Tableau
  • Intro
  • Installing Tableau Desktop and Tableau Public (FREE)
  • Challenge description + view data in file
  • Connecting Tableau to a Data file - CSV file
  • Navigating Tableau - Measures and Dimensions
  • Creating a calculated field
  • Adding colours
  • Adding labels and formatting
  • Exporting your worksheet

How to use Tableau for Data Mining

  • Intro
  • Project Overview
  • Connecting Tableau to an Excel File
  • How to visualise an ad-hoc A-B test in Tableau
  • Working with Aliases
  • Adding a Reference Line
  • Looking for anomalies
  • Handy trick to validate your approach / data

Advanced Data Mining With Tableau

  • Intro
  • Creating bins & Visualizing distributions
  • Creating a classification test for a numeric variable
  • Combining two charts and working with them in Tableau
  • Validating Tableau Data Mining with a Chi-Squared test
  • Chi-Squared test when there is more than 2 categories
  • Visualising Balance and Estimated Salary distribution
  • Chi-Squared Test

Welcome to Part 2

Stats Refresher

  • Intro
  • Types of variables: Categorical vs Numeric
  • Types of regressions
  • Ordinary Least Squares
  • R-squared
  • Adjusted R-squared
  • Simple Linear Regression
  • Intro
  • Introduction to Gretl
  • Import data and run descriptive statistics
  • Reading Linear Regression Output
  • Plotting and analysing the graph

Multiple Linear Regression

  • Intro
  • Caveat: assumptions of a linear regression
  • Dummy Variables
  • Dummy Variable Trap
  • Ways to build a model: BACKWARD, FORWARD, STEPWISE
  • Backward Elimination
  • Using Adjusted R-squared to create Robust models
  • Interpreting coefficients of MLR

Logistic Regression

  • Intro
  • Binary outcome: Yes/No-Type Business Problems
  • Logistic regression intuition
  • Your first logistic regression
  • False Positives and False Negatives
  • Confusion Matrix
  • Interpreting coefficients of a logistic regression

Building a robust geodemographic segmentation model

  • Intro (what you will learn in this section)
  • What is geo-demographic segmenation?
  • Let's build the model - first iteration
  • Let's build the model - backward elimination: STEP-BY-STEP
  • Transforming independent variables
  • Creating derived variables
  • Checking for multicollinearity using VIF
  • Correlation Matrix and Multicollinearity Intuition
  • Model is Ready and Section Recap
  • Assessing your model
  • Intro
  • Accuracy paradox
  • Cumulative Accuracy Profile (CAP)
  • How to build a CAP curve in Excel
  • Assessing your model using the CAP curve
  • Get my CAP curve template
  • How to use test data to prevent overfitting your model
  • Applying the model to test data
  • Comparing training performance and test performance

Drawing insights from your model

  • Intro
  • Power insights from your CAP
  • Coefficients of a Logistic Regression - Plan of Attack (advanced topic)
  • Odds ratio (advanced topic)
  • Odds Ratio vs Coefficients in a Logistic Regression (advanced topic)
  • Deriving insights from your coefficients (advanced topic)

Model maintenance

  • Intro
  • What does model deterioration look like?
  • Why do models deteriorate?
  • Three levels of maintenance for deployed models

Part 3: Data Preparation

Business Intelligence (BI) Tools

  • Intro
  • Working with Data
  • What is a Data Warehouse? What is a Database?
  • Setting up Microsoft SQL Server 2014 for practice
  • Important: Practice Database
  • ETL for Data Science - what is Extract Transform Load (ETL)?
  • Microsoft BI Tools: What is SSDT-BI and what are SSIS/SSAS/SSRS ?
  • Installing SSDT with MSVS Shell

ETL Phase 1: Data Wrangling before the Load

  • Intro
  • Preparing your folder structure for your Data Science project
  • Two things you HAVE to do before the load
  • Notepad ++
  • Editpad Lite

ETL Phase 2: Step-by-step guide to uploading data using SSIS

  • Intro
  • Starting and navigating an SSIS Project
  • Creating a flat file source task and OLE DB destination
  • Setting up your flat file source connection
  • Setting up your database connection and creating a RAW table
  • Run the Upload & Disable
  • Due Dilligence: Upload Quality Assurance

Handling errors during ETL (Phases 1 & 2)

  • Intro
  • How excel can mess up your data
  • Bulletproof Blueprint for Data Wrangling before the Load
  • SSIS Error: Text qualifier not specified
  • What do you do when your source file is corrupt?
  • SSIS Error: Data truncation
  • Handy trick for finding anomalies in SQL
  • Automating Error Handling in SSIS: Conditional Split
  • Automating Error Handling in SSIS: Conditional Split (Level 2)
  • How to analyze the error files
  • Due Dilligence: the one thing you HAVE to do every time
  • Types of Errors in SSIS

SQL Programming for Data Science

  • Intro
  • Download the dataset for this section
  • Getting To Know MS SQL Management Studio
  • Shortcut to upload the data
  • SELECT * Statement
  • Using the WHERE clause to filter data
  • How to use Wildcards / Regular Expressions in SQL (% and _)
  • Comments in SQL
  • Order By
  • Data Types in SQL
  • Implicit Data Conversion in SQL
  • Using Cast() vs Convert()
  • Working with NULLs
  • Understanding how LEFT, RIGHT, INNER, and OUTER joins work
  • Joins with duplicate values
  • Joining on multiple fields
  • Practicing Joins

ETL Phase 3: Data Wrangling after the load

  • Intro
  • RAW, WRK, DRV tables
  • Create your first Stored Proc in SQL
  • Executing Stored Procedures
  • Modifying Stored Procedures
  • Create table
  • Insert INTO
  • Check if table exists + drop table + Truncate
  • Intermediate Recap - Procs
  • Create the proc for the second file
  • Adding leading zeros
  • Converting data on the fly
  • How to create a proc template
  • Archiving Procs
  • What you can do with these tables going forward [drv files etc.]

Handling errors during ETL (Phase 3)

  • Intro
  • Upload the data to RAW table
  • Create Stored Proc
  • How to deal with errors using the isnumeric() function
  • How to deal errors using the len() function
  • How to deal with errors using the isdate() function
  • Additional Quality Assurance check: Balance
  • Additional Quality Assurance check: ZipCode
  • Additional Quality Assurance check: Birthday
  • ETL Error Handling "Vehicle Service" Project

Part 4: Communication

  • Working with people
  • Intro
  • Cross-departmental Work
  • Come to me with a Business Problem
  • Setting expectations and pre-project communication
  • Go and sit with them
  • The art of saying "No"
  • Sometimes you have to go to the top
  • Building a data culture

Presenting for Data Scientists

  • Intro
  • Case study
  • Analysing the intro
  • Intro dissection - recap
  • REAL Data Science Presentation Walkthrough - Make Your Audience Say "WOW"
  • My brainstorming method
  • How to present to executives
  • The truth is not always pretty
  • Passion and the Wow-factor
  • Bonus: my full presentation | LIVE
  • Bonus: links to other examples of good storytelling

Data Science Certification Pass Guranteed!

  • Complete Your Course

  • Become Certified

  • Impress Your Employer


Pre-requisite for the course?

Basic Programming Knowledge

Basic Mathematics Knowledge

Basic Database knowledge(optional)

What is the duration of course?

40+ hours

Who can take this course?

B.Tech/ Mathematics/M.Sc Statistics/M.Sc Physics Students

Working professionals

Where you will be after this course?

You will be able to design models, you will be able to visualize your data, you will be able to extract, transfer and load your data.

Who will provide the training for this course?

Trainers who are/were working with corporate and have practical understanding of the subjects.

What is special about Excelvisor Technologies?

Excelvisor is a product based 4.0 industry company, we are very professional and we provide industry like environment to their students and we have good links and tie-ups with the corporate, which helps our students in their placements.



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