Evolved and designed by veterans in the Analytics industry, this program prepares students and working professionals to establish a hi-flying globe-trotting career in the growing Data and Analytics domain.
Who should take this course
This course is designed for the following professionals:
- Entry to senior level managers
- Entry to senior level techinical leaders
Learners are required to complete 6-12 months of faculty led Online mode.
The following are the modules covered:
Big Data 101 - Big Data Characteristics - Big Data and Business - Data Relationships and Data Model - Data Grouping - Clustering Algorithms - Getting ready for Clustering Algorithms - Clustering Algorithms - UPGMA, single Link Clustering - KPIs, Businesses & Data Elements - Mapping for business outcomes - Basic Query - Advanced Query - Embedding - Mathematics Modelling - Introduction to key mathematical concepts - Application of eigenvalues and eigenvectors - Application of the graph Laplacian - Application of PCA and SVD - Coding in DB Environment - Making Data Sets
R Programming - R Programming - Introduction to R - I - Introduction to R - II - Common Data Structures in R - Conditional Operation and Loops - Looping in R using Apply Family Functions - Creating User Defined Functions in R - Graphics with R - Advanced Graphics with R
Text Analytics - Basics of text analysis processes - Web crawling - Web Scraping from downloaded html files - Text classification - Singular Value decomposition concept - Latent Semantic Analysis - Document clustering - Topic Modeling - Class Assignments - Presentation
Statistics 101 - Introduction to Statistics - Introduction to Statistics - II - Measures of Central Tendency, Spread and Shape - I - Measures of Central Tendency, Spread and Shape - II - Measures of Central Tendency, Spread and Shape - III
Python - Understanding Basics of Python - Control Structures and for loop - Playing with while loop break and continue - Strings and files - List - Dictionary and Tuples
Statistics with R - Introduction to Data - Introduction to Probability - Distributions - Introduction to linear regression - Foundations for inference and estimation - Foundations for inference and hypothesis testing - Linear Regression and Multiple Regression
Data Mining 1 - Machine Learning with R & Python - Introduction to NumPy - Introduction to Pandas - Slicing Data - Exploratory Data Analysis - Exploratory Data Analysis (Continue) - Missing Value Imputation and Outlier Analysis - Linear Regression Motivation - Linear Regression optimization objective - Linear Regression in Python - Introduction to Regression Tree - Introduction to Classification Tree - Measures of Selecting the best Split - Cluster Analysis - Hierarchical Clustering & k-Means Clustering - Customer segmentation in Telecom Industry using Cluster Analysis - k-Means clustering - Association Rules mining - Market Basket Analysis
Advanced Statistics with R - Inference and hypothesis testing on single population - Analysis of difference in two populations - Analysis of Variance - Chi-Square Analysis - Analysis of data using Non-parametric Statistics - Linear regression analysis - Multiple regression analysis - Advanced Multiple regression analysis - Logistic Regression - Forecasting
Data Visualization with Tableau - Need for visualizing data - Research methodologies - Importance of Big data visualization - Tableau product offerings - Installation of Tableau Public - Working with Tableau - Live Case study/Discussion - Creating interactive dashboards with Tableau Public - Case study discussion - Story Boarding with Tableau Public - Case study discussion - Geomapping in Tableau - Qlik view - Basics - Google charts - Basics - Dynamic charts with Google Docs - Supplementary material & Case study discussion - Closing session & Queries
Web Analytics - Introduction to Digital Media Analytics - Introduction to Google Analytics - Concept of Account, Property and View - Concept of Sessions and Users - Concept of Dimension, Metric and Segment - Reading a Google Analytics Report - Audience Analytics - Acquisition Analytics - Behaviour Analytics - Real-Time Analytics - Setting Up and Analysing Events - Intelligent Events - Setting Up and Analysing Experiments - Setting Up and Measuring Conversion Goals - Attribution Modelling - Segment Reporting - Designing Custom Reports - Introduction to Google Adwords - Search Marketing - Display Marketing - Google Adwords Analytics - Managing a Google Analytics Account
RDBMS with SQL and DWH - Introduction to DBMS / RDBMS - Data Modelling - Physical Data Model - Getting Started with SQL Lite - DDL - DML - Introduction to Data Warehousing - Dimensional Modelling - Advanced SQL - Olap Cubes - Olap Cubes Practicals - Artificial Intelligence & Deep Learning - Industry Practices
About Course Provider
361DM is a learning science and digital education-oriented company run by professionals with over 2 decades of experience in the tech-enabled learning industry. 361DM works with corporate, institutions, universities across the world, and has designed and delivered learning programs to over 100000 students and executives in the last decade. We help higher education institutions and corporate organizations architect a robust digital strategy.