Purpose
There are many different sources and kinds of information. Information extraction, modeling, and analysis capabilities can have considerable economic advantages. Big Data analytics may help businesses find trends and make operational changes to take advantage of the findings and improve client satisfaction and income.
This training session will offer a practitioner-oriented approach to identifying the needs for the use of data science, how they might be adopted, available technologies, which analytical models may be suited to deliver useful data, and how to make sense of Big Data and Analytics.
Course Objectives
By completion of the 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; Consider How to Choose Appropriate Models and Technology for Big Data; Learn from Case Study Examples and Use Case Scenarios; Successfully Achieve Results by Applying Best Practice in Data Analytics. By the end of the course, participants should be able to:
Duration: 5 Days
Target Audience
Senior executives, technical engineers, and individuals working in corporate data technology, research, and statistical analysis can benefit from this training session.
Although this training program is appropriate for a wide spectrum of professionals, it will especially help
Key application development and data research personnel, technology engineers, CTOs, and CIOs, and directors of strategic development
Program Outline
TOPIC 1: Big Data Analytics
Roles for Big Data within the Technology and Commercial Enterprise; Current Practices and Trends in Big Data Analytics; Business Intelligence vs. Data Science; Analytical Architecture for Big Data; Key Drivers for Big Data Analytics; Case Study and Summary
TOPIC 2: Â Data Analytics Models and Lifecycle
Discovery Day Two: Data Preparation Day Three: Model Planning and Review Day Four: Model Creation Day Five: Communication Plan Day Six: From Planning to Operation Day Seven: Case Study and Summary
TOPIC 3: Tools and Techniques for Data Analysis Overview
Overview of the R Framework; Big Data Analytics; Exploratory Data Analysis; Statistical Methods of Evaluation; Advanced Methods of Clustering; Advanced Theory and Methods of Association Rules; Advanced Theory and Methods of Regression; Case Study and Summary; Advanced Theory and Methods of Association Rules; Advanced Theory and Methods of Regression
TOPIC 4: Advanced theory and techniques
Technology and tools for advanced data analytics; use case and assessment; case study and summary; advanced analytical theory of classification; advanced analytical theory of time series analysis; advanced analytical theory of textual analysis;
TOPIC 5: Using Technology and Tools to Get Results
- Data Visualization Overview • Summary and Case Study • Unstructured Data Analytics • Advanced Analytical Tools in Database Analytics • How to Integrate Data Analytics • Current Best Practice Management and Approach for Project Delivery