Associate Analytics
Objectives:

To introduce the terminology, technology and its applications

To introduce the concept of Analytics for Business

To introduce the tools, technologies & programming languages which is used in day to
day analytics cycle
Introduction to Analytics (Associate Analytics – I)
Unit I
Introduction to Analytics and R programming (NOS 2101)
Introduction to R, RStudio (GUI): R Windows Environment, introduction to various data types,
Numeric, Character, date, data frame, array, matrix etc., Reading Datasets, Working with different
file types .txt,.csv etc. Outliers, Combining Datasets, R Functions and loops.
Manage your work to meet requirements (NOS 9001)
Understanding Learning objectives, Introduction to work & meeting requirements, Time
Management, Work management & prioritization, Quality & Standards Adherence,
Unit II
Summarizing Data & Revisiting Probability (NOS 2101)
Summary Statistics - Summarizing data with R, Probability, Expected, Random, Bivariate
Random variables, Probability distribution. Central Limit Theorem etc.
Work effectively with Colleagues (NOS 9002)
Introduction to work effectively, Team Work, Professionalism, Effective Communication skills, etc.
Unit III
SQL using R Introduction to NoSQL, Connecting R to NoSQL databases. Excel and R
integration with R connector.
Unit IV
Correlation and Regression Analysis (NOS 9001)
Regression Analysis, Assumptions of OLS Regression, Regression Modelling. Correlation,
ANOVA, Forecasting, Heteroscedasticity, Autocorrelation, Introduction to Multiple Regression etc.
Unit V
Understand the Verticals - Engineering, Financial and others (NOS 9002)
Understanding systems viz. Engineering Design, Manufacturing, Smart Utilities, Production lines,
Automotive, Technology etc.
Understanding Business problems related to various businesses
Requirements Gathering
Gathering all the data related to Business objective
Text Books:
1.
Student’s Handbook for Associate Analytics.
Reference Books:
1.
Introduction to Probability and Statistics Using R, ISBN: 978-0-557-24979-4, is a textbook
written for an undergraduate course in probability and statistics.
2.
3.
4.
An Introduction to R, by Venables and Smith and the R Development Core Team. This may be
downloaded for free from the R Project website (http://www.r-project.org/, see Manuals). There are
plenty of other free references available from the R Project website.
Montgomery, Douglas C., and George C. Runger, Applied statistics and probability for engineers.
John Wiley & Sons, 2010
The Basic
Concepts
of
Time
Series
Analysis.http://anson.ucdavis.edu/~azari/sta137/AuNotes.pdf
Time Series Analysis and Mining with R,Yanchang Zhao.
Big Data Analytics (Associate Analytics – II)
Unit I: Data Management (NOS 2101)
Design Data Architecture and manage the data for analysis, understand various sources of
Data like Sensors/signal/GPS etc. Data Management, Data Quality (noise, outliers,
missing values, duplicate data) and Data Preprocessing.
Export all the data onto Cloud ex. AWS/Rackspace etc.
Maintain Healthy, Safe & Secure Working Environment (NOS 9003) Introduction, workplace
safety, Report Accidents & Emergencies, Protect health & safety as your work, course
conclusion, assessment
Unit II
Big Data Tools (NOS 2101)
Introduction to Big Data tools like Hadoop, Spark, Impala etc., Data ETL process, Identify gaps in
the data and follow-up for decision making.
Provide Data/Information in Standard Formats (NOS 9004)
Introduction, Knowledge Management, Standardized reporting & compliances, Decision Models,
course conclusion. Assessment
Unit III
Big Data Analytics Run descriptives to understand the nature of the available data, collate all
the data sources to suffice business requirement, Run descriptive statistics for all the variables
and observer the data ranges, Outlier detection and elimination.
Unit IV
Machine Learning Algorithms (NOS 9003)
Hypothesis testing and determining the multiple analytical methodologies, Train Model on 2/3
sample data using various Statistical/Machine learning algorithms, Test model on 1/3 sample for
prediction etc.
Unit V (NOS 9004)
Data Visualization (NOS 2101)
Prepare the data for Visualization, Use tools like Tableau, QlickView and D3, Draw insights out of
Visualization tool.
Product Implementation
Text Books:
1. Student’s Handbook for Associate Analytics.
Reference Books:
1.
Introduction to Data Mining, Tan, Steinbach and Kumar, Addison Wesley, 2006
2.
3.
Data Mining Analysis and Concepts, M. Zaki and W. Meira (the authors have kindly made an
online version available): http://www.dataminingbook.info/uploads/book.pdf
Mining of Massive Datasets Jure Leskovec Stanford Univ. Anand RajaramanMilliway Labs
Jeffrey D. Ullman Stanford Univ.
(http://www.vistrails.org/index.php/Course:_Big_Data_Analysis)
Predictive Analytics (Associate Analytics – III)
Unit I
Introduction to Predictive Analytics & Linear Regression (NOS 2101)
What and Why Analytics, Introduction to Tools and Environment, Application of Modelling in
Business, Databases & Types of data and variables, Data Modelling Techniques, Missing
imputations etc.
Need for Business Modelling, Regression – Concepts, Blue property-assumptions-Least Square
Estimation, Variable Rationalization, and Model Building etc.
Unit II
Logistic Regression (NOS 2101)
Model Theory, Model fit Statistics, Model Conclusion, Analytics applications to various Business
Domains etc.
Regression Vs Segmentation – Supervised and Unsupervised Learning, Tree Building –
Regression, Classification, Overfitting, Pruning and complexity, Multiple Decision Trees etc.
Unit III
Objective Segmentation(NOS 2101)
Regression Vs Segmentation – Supervised and Unsupervised Learning, Tree Building –
Regression, Classification, Overfitting, Pruning and complexity, Multiple Decision Trees etc.
Develop Knowledge, Skill and Competences (NOS 9005)
Introduction to Knowledge skills & competences, Training & Development, Learning &
Development, Policies and Record keeping, etc.
Unit IV
Time Series Methods /Forecasting, Feature Extraction (NOS 2101)
Arima, Measures of Forecast Accuracy, STL approach, Extract features from generated model as
Height, Average, Energy etc and Analyze for prediction.
Project
Unit V
Working with Documents (NOS 0703)
Standard Operating Procedures for documentation and knowledge sharing, Defining purpose and
scope documents, Understanding structure of documents – case studies, articles, white papers,
technical reports, minutes of meeting etc., Style and format, Intectual Property and Copyright,
Document preparation tools – Visio, PowerPoint, Word, Excel etc., Version Control, Accessing
and updating corporate knowledge base, Peer review and feedback.
Text Books:
1. Student’s Handbook for Associate Analytics-III.
Reference Books and websites:
1. Gareth James • Daniela Witten • Trevor Hastie Robert Tibshirani. An Introduction to Statistical
Learning with Applications in R