Data Analytics

Overview:

Financial services operators, organizations across various sectors are creating data pipelines to improve their customer intelligence, maintain regulatory compliance, reduce fraud and drive better marketing campaigns that improve their brand and generate revenue. Financial Institutions, companies, manufacturing etc are leveraging more on data to find new insights that drive higher business performance.

Analyzing customer interaction data from various sources such as transactions, CRM and even social media activities helps financial services understand the customer’s journey. With big data analytics, you can answer questions to help understand the customers, customer behavior and how to get customers to use products more.

Learning outcome:

1. This learning programme introduces participants to the Big Data Canvas, a methodology for ensuring that data strategies remain feasible while pursuing the most valuable outcomes.
2. Recognize the dynamics of big data, analytics, and data science in various financial applications.
3. Identify the Critical Success Factors for an organization’s Big Data strategy
4.  Shape your organization’s big data strategy by leveraging data science best practices.
5. Gain understanding of business analytics with the use of robust data and the ability to consider the relationships between this data science and finance to make holistic judgments when analyzing situations.
6. Apply ethical practices in everyday business activities and make well-reasoned ethical business and data management decisions. Select, Prepare, Construct, and Integrate, Structure, and Format data to be most effective to ensure the models meet the business goals.
7. Develop actionable plans from existing data and initiatives to increase sales, reduce marketing costs and improve customer retention.
8. Demonstrate knowledge of statistical data analysis techniques utilized in business decision making.
9. Manage data efficiently and allow users to perform multiple tasks with ease.

Course Modules:

Module 1:

• Concept of Business Intelligence

• Description of Business Intelligence concept for Business use

• Simple architecture of an EDW

• Data mart and its attributes

• Data Integration

• Introduction to Data Modelling

• Introduction to Facts and Dimensions

Module 2:

• Data Visualization using Power BI

• Excel vs Power BI

• Custom Q&A questions

• Transform data into actionable insights

Module 3:

• Business Analytics concept

• Introduction to Descriptive Analytics

• Introduction to Prescriptive Analytics

• Introduction to Predictive Analytics

• Market basket analysis

• Classification

• Regression

• Segmentation

Module 4:

• Introduction to Data Science (Using Python)

• Introduction to Python for Data Science

• Vectors, Matrices, Factors, Data frames, Lists

• Data Pre-Processing