ANALYTICS FOR EVERYONE

L

T

P

C

3

0

0

3

COURSE OBJECTIVES:

• To equip students with practical knowledge of AzureML, enabling them to generate business value through structured query language (SQL) and apply one and two quantity analysis techniques.
• To develop students’ ability to perform forecasting using predictive analytics concepts, including the demonstration of auto-regressive models for real-world business applications.

UNIT I

<*****RODUCTION TO AZUREML

9

Sources of Data – Analytics Value Escalator – Story of a Company – Getting Started with Azureml.

UNIT II

EXTRACT LOAD AND TRANSFORM

9

Introduction to Extract, Load and Transform – Generating Value from Multiple Sources of Data – Database and SQL – SQL Joins – Other ELT Tasks

UNIT III

DESCRIPTIVE ANALYTICS

9

Descriptive analytics Introduction – Application in World trade Data – Describing Single Quantity – Credit Card Data Set – Describing a Single Quantity in Azureml – Describing Two Quantities in Azureml.

UNIT IV

PREDICTIVE ANALYTICS ᎓ I

9

Forecasting, Time Series Analysis – Additive & Multiplicative Models – Exponential Smoothing Techniques.

UNIT V

PREDICTIVE ANALYTICS ᎓ II

9

Forecasting Accuracy – Auto-regressive and Moving Average Models – Demo using SPSS.

L:45

T:0

P: 60

Total: 45 Periods

TEXT BOOKS

T1

Fontama et.al, ᎘Pre***tive Analytics with Microsoft Azure Machine Learning᎙, Apress.

T2

Eric Siegel, ᎘Pre***tive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die᎙, Wiley.

REFERENCES

R1

Sumit Mund, ᎘Microsoft Azure Machine Learning᎙, Packt Publishing.

R2

Anil Maheswari, ᎘Data Analytics Made Accessible᎙, THM Publishers.