Cambridge
Academy
In London

Introduction:

In today's digital landscape, information exists in myriad forms and sources. The ability to extract, model, and analyze data can yield significant commercial benefits. Big Data Analytics enables organizations to identify trends, adjust operational procedures, and enhance revenue and client experience. This course offers a practitioner approach to understanding the application of data science and big data analytics, including identifying requirements, adopting technologies, selecting appropriate analytical models, and making sense of vast amounts of data.

Course Objectives

By the end of this course, participants will be able to:

·        Understand the role of Big Data for their organization.

·        Recognize when to apply Data Analytics and the Best Methods of Approach.

·        Select appropriate models and technology for Big Data.

·        Learn from case studies and use case scenarios.

·        Successfully achieve results by applying best practices in Data Analytics.

Target Audience

This course is designed for:

·        Statistical and Research Analysts.

·        Key Application Development and Data Research Personnel.

·        Technology Engineers, CTOs, and CIOs.

·        Strategic Development Directors.

·        Difference Between Data Science and Big Data Analytics:

Data Science and Big Data Analytics are interconnected fields that extract insights from complex datasets. Data Science encompasses various activities, including statistical analysis and machine learning, while Big Data Analytics specializes in processing and analyzing large datasets efficiently. Both play pivotal roles in making data-driven decisions by uncovering patterns and correlations within extensive datasets.

Course Outlines

Day  1: Big Data Analytics:

·        Current Practices and Trends.

·        Business Intelligence vs. Data Science.

·        Analytical Architecture.

·        Roles within Technology and Commercial Enterprises.

·        Key Drivers.

Day  2: Data Analytics Models and Lifecycle:

·        Data Analytics Lifecycle.

·        Discovery.

·        Data Preparation.

·        Model Planning and Review.

·        Model Creation.

·        Communication Plan.

·        From Planning to Operation.

Day  3: Data Analytical Methods and Programs Overview:

·        R Framework Overview.

·        Big Data Analytics Overview.

·        Exploratory Data Analysis.

·        Statistical Evaluation Methods.

·        Advanced Clustering Methods.

·        Association Rules.

·        Regression.

Day  4: Advanced Theory and Methods Overview:

·        Classification.

·        Time Series Analysis.

·        Textual Analysis.

·        Technology and Tools.

·        Use Case and Assessment.

Day  5: Technology, Tools, and Achieving Results:

·        Unstructured Data Analytics.

·        Advanced Analytical Tools.

·        Data Integration.

·        Project Delivery Management.

·        Data Visualization Overview.