Data Analytics

Data Analytics is among one of the fastest growing and highest paying profession. In today’s competitive business world CEOs, CFOs and other stakeholders rely on data analyst to provide inputs to make informed strategic business decisions.

NASSCOM Certified Data Analytics program has been designed by IT industry giants in association with NASSCOM to provide learners with vital skills in data analysis, data interpretation, data visualization and other areas of expertise that are in growing demand in the business world.

After completing this programme, you will be able to:

• Carry out rule-based statistical analysis
• Create documents for knowledge sharing
• Manage work to meet requirements
• Work effectively with colleagues
• Maintain a healthy, safe and secure working environment
• Provide data/information in standard formats
• Develop knowledge, skills and competence

Associate Analytics Syllabus

Duration: 400 Hours

Table of Contents

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.
• Summary Statistics - Summarizing data with R,
• Probability,
• Expected, Random,
• Bivariate Random variables,
• Probability distribution. Central Limit Theorem etc.

Unit II

NoSQL using R & Correlation and Regression Analysis (NOS 2101)

• Introduction to NoSQL,
• Connecting R to NoSQL databases.
• Excel and R integration with R connector.
• Regression Analysis,
• Assumptions of OLS Regression,
• Regression Modelling.
• Correlation, ANOVA, Forecasting, Heteroscedasticity, Autocorrelation,
• Introduction to Multiple Regression etc.

Unit III

Understand the Verticals - Engineering, Financial and others (NOS 2101)

• Understanding systems viz. Engineering Design, Manufacturing, Smart Utilities,
• Production lines, Automotive, Technology etc.
• Understanding Business problems related to various businesses

Unit IV

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 V

Work effectively with Colleagues (NOS 9002)

• Introduction to work effectively,
• Team Work, Professionalism,
• Effective Communication skills, etc.

Associate Analytics – II

Unit I

Data Management & Introduction to Big Data Tools (NOS 2101)

• Design Data Architecture and manage the data for analysis,
• Understand various sources of Data like Sensors/signal/GPS etc.
• Export all the data onto Cloud ex. AWS/Rackspace etc.
• Introduction to Big Data tools like Hadoop, Spark, Impala etc.,
• Data ETL process, Identify gaps in the data and follow-up for decision making.

Unit II

Big Data Analytics & Machine Learning Algorithms (NOS 2101)

• Run descriptive 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 observe the data ranges, Outlier detection and elimination.
• 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 III

Data Visualization (NOS 2101)

• Prepare the data for Visualization,
• Use tools like Tableau,
• QlickView and D3,
• Draw insights out of Visualization tool.

Unit IV

Maintain Healthy, Safe & Secure Working Environment (NOS 9003)

• Introduction, workplace safety,
• Report Accidents & Emergencies,
• Protect health & safety as your work,
• course conclusion, assessment

Unit V

Provide Data/Information in Standard Formats (NOS 9004)

• Introduction, Knowledge Management,
• Standardized reporting & compliances,
• Decision Models,
• course conclusion.
• Assessment

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 Objective Segmentation (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

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.

Unit IV

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.

Unit V

Develop Knowledge, Skill and Competences (NOS 9005)

• Introduction to Knowledge skills & competences,
• Training & Development,
• Learning & Development, Policies and Record keeping, etc.

Project work with data