About the course

This comprehensive Data Science training provides you detailed learning in data science, data analytics, project life cycle, data acquisition, analysis, statistical methods and machine learning. Data Science is a multi-disciplinary field to study how information or data can be turned into a valuable resource for implementing various business and IT strategies. You will become proficient in deploying Recommenders using Apache Mahout, data analysis, data transformation, experimentation, and evaluation.

What are the requirements?

There is no requirement to start this training

What I am going to get from this course

  • Understand Data Science, Project Lifecycle, and Data Acquisition
  • Comprehend Machine Learning Algorithms
  • Know the tools and techniques of Experimentation, Evaluation and Project Deployment
  • Take in the idea of Prediction and Analysis Segmentation through Clustering
  • Take in the essentials of Big Data and approaches to integrate R with Hadoop
  • Live Projects in Data science, Analytics.
  • Work on data mining, data structures, data manipulation.

Target Audience

Big Data professionals, Business Intelligence professionals & Business Analytics professionals Big data Statisticians who are looking to improve statistics skills Developers who want to learn Machine Learning (ML) Techniques Individuals who want to take up the roles of Data Scientist and Machine Learning Expert

Curriculum


Module 1: What is Data Science?

  • 1.1  Introduction
  • 1.2  Profession of the future
  • 1.3  Areas of Data Science

Module 2: Introduction to Tableau

  • 2.1  Introduction
  • 2.2  Installing Tableau Desktop and Tableau Public (FREE)
  • 2.3  Challenge description + view data in file
  • 2.4  Connecting Tableau to a Data file - CSV file
  • 2.5  Navigating Tableau - Measures and Dimensions
  • 2.6  Creating a calculated field
  • 2.7  Adding colours
  • 2.8  Adding labels and formatting
  • 2.9  Exporting your worksheet
  • 2.10  Section Recap
  • 2.11  Tableau Basics

Module 3: How to use Tableau for Data Mining

  • 3.1  Introduction
  • 3.2  Get the Dataset + Project Overview
  • 3.3  Connecting Tableau to an Excel File
  • 3.4  How to visualise an ad-hoc A-B test in Tableau
  • 3.5  Preview
  • 3.6  Working with Aliases
  • 3.7  Adding a Reference Line
  • 3.8  Looking for anomalies
  • 3.9  Handy trick to validate your approach / data

Module 4: Advanced Data Mining With Tableau

  • 4.1  Introduction
  • 4.2  Creating bins & Visualizing distributions
  • 4.3  Creating a classification test for a numeric variable
  • 4.4  Preview
  • 4.5  Combining two charts and working with them in Tableau
  • 4.6  Validating Tableau Data Mining with a Chi-Squared test
  • 4.7  Chi-Squared test when there is more than 2 categories
  • 4.8  Visualising Balance and Estimated Salary distribution

Module 5: Modelling

  • 5.1  Introduction
  • 5.2  Types of variables: Categorical vs Numeric
  • 5.3  Types of regressions
  • 5.4  Ordinary Least Squares
  • 5.5  R-squared
  • 5.6  Preview
  • 5.7  Adjusted R-squared
  • 5.8  Simple Linear Regression
  • 5.9  Multiple Linear Regression
  • 5.10  Logistic Regression

Module 6: Building a robust geodemographic segmentation model

  • 6.1  Get the dataset
  • 6.2  What is geo-demographic segmenation?
  • 6.3  Let's build the model - first iteration
  • 6.4  Let's build the model - backward elimination: STEP-BY-STEP
  • 6.5  Transforming independent variables
  • 6.6  Creating derived variables
  • 6.7  Checking for multicollinearity using VIF
  • 6.8  Correlation Matrix and Multicollinearity Intuition

Module 7: Assessing your model

  • 7.1  Introduction
  • 7.2  Accuracy paradox
  • 7.3  Cumulative Accuracy Profile (CAP)
  • 7.4  How to build a CAP curve in Excel
  • 7.5  Assessing your model using the CAP curve
  • 7.6  Get my CAP curve template
  • 7.7  How to use test data to prevent overfitting your model
  • 7.8  Applying the model to test data

Module 8: Drawing insights from your model

  • 8.1  Introduction
  • 8.2  Power insights from your CAP
  • 8.3  Coefficients of a Logistic Regression - Plan of Attack (advanced topic)
  • 8.4  Odds ratio (advanced topic)
  • 8.5  Odds Ratio vs Coefficients in a Logistic Regression (advanced topic)
  • 8.6  Deriving insights from your coefficients (advanced topic)

Module 9: Model maintenance

  • 9.1  Introduction
  • 9.2  What does model deterioration look like?
  • 9.3  Why do models deteriorate?
  • 9.4  Three levels of maintenance for deployed models

Module 10: Data Preparation

  • 10.1  Business Intelligence (BI) Tools
  • 10.2  ETL Phase 1: Data Wrangling before the Load
  • 10.3  ETL Phase 2: Step-by-step guide to uploading data using SSIS
  • 10.4  Handling errors during ETL (Phases 1 & 2)
  • 10.5  SQL Programming for Data Science
  • 10.6  ETL Phase 3: Data Wrangling after the load
  • 10.7  Handling errors during ETL (Phase 3)

Module 11: Working with people

  • 11.1  Introduction
  • 11.2  Cross-departmental Work
  • 11.3  Setting expectations and pre-project communication
  • 11.4  Go and sit with them
  • 11.5  The art of saying "No"
  • 11.6  Building a data culture
  • 11.7  Presenting for Data Scientists
  • 11.8  Analysing the intro
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What kind of learning does ITlearn360.com provide?

ITlearn360.com offers instructor-led online live sessions and classroom-based corporate trainings and bootcamps for various courses and certifications to the learners.

Who are the instructors @ITlearn360.com?

@ITlearn360.com, we have an instructor community of industry professionals who are working in leading organizations and are veterans in their respective fields. These experts belong to various industries and are willing to share their talent with learners like you.

Are classes @ITlearn360.com conducted through online video streaming?

Yes, the classes @ITlearn360.com are conducted through online video streaming where there is two-way communication between users and instructors. The users can speak by using a microphone, chat by sending a message through a chat window and share their screens with an instructor. For better understanding, users also get recorded video of the class.

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