Data Science and Big Data AnalyticsLeadership and management

In any city around the world 00447455203759 Course Code: AC/2024/284

Course Description

Course Duration: Five Training Days

Course Language: Arabic or English

Include:

Scientific material with TAB

Workshops

Reception and farewell at the airport

Coffee Break

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.

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.

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.

Targeted Competencies:
Upon completion of this training, participants will evolve in the following competencies:
- Big Data Analytics.
- Adaptation and Approach of Lifecycles and Models.
- Methods and Models for Statistical Evaluation.
- Advanced Methods and Models for Big Data Analytics.
- Selecting appropriate tools to achieve the best from Data Analytics.

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 Content:
Unit 1: Big Data Analytics:
- Current Practices and Trends.
- Business Intelligence vs. Data Science.
- Analytical Architecture.
- Roles within Technology and Commercial Enterprises.
- Key Drivers.

Unit 2: Data Analytics Models and Lifecycle:
- Data Analytics Lifecycle.
  - Discovery.
  - Data Preparation.
  - Model Planning and Review.
  - Model Creation.
  - Communication Plan.
  - From Planning to Operation.

Unit 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.

Unit 4: Advanced Theory and Methods Overview:
- Classification.
- Time Series Analysis.
- Textual Analysis.
- Technology and Tools.
- Use Case and Assessment.

Unit 5: Technology, Tools, and Achieving Results:
- Unstructured Data Analytics.
- Advanced Analytical Tools.
- Data Integration.
- Project Delivery Management.
- Data Visualization Overview.

Conclusion:
Completing this Data Science & Big Data Analytics course provides a comprehensive understanding of both disciplines. Data Science encompasses statistical analysis and machine learning, while Big Data Analytics focuses on efficiently processing and analyzing large datasets. Graduates gain expertise in making data-driven decisions, empowering them for careers in data-intensive fields like life sciences. Understanding the nuances between Big Data Analytics and Data Science enables professionals to leverage advanced techniques effectively.