MASTER CLASS ON DATA SCIENCE & BIG DATA ANALYTICS

TRAINING OVERVIEW

DixonTech's Master Class on Data Science & Big Data Analytics offers a transformative journey into the world of data-driven decision-making. As the influential data scientist, Nate Silver, once said, “The numbers have no way of speaking for themselves. We speak for them.” In today's dynamic business landscape, leveraging Big Data and Data Science principles is essential for driving sustainable growth and enhancing organizational effectiveness. Our comprehensive training equips participants with the knowledge and skills to extract meaningful patterns, identify outliers, and make accurate forecasts from vast datasets. By mastering these techniques, participants, and their organizations, can optimize operations, reduce costs, and stay ahead of the competition. This popular DixonTech Training not only provides insights into the principles and benefits of Big Data and Data Science but also offers valuable guidance on navigating potential pitfalls. Join us and harness the power of Big Data to propel your journey towards success.

TRAINING TOPICS

• Big data analytics
• Data analytics models and lifecycle
• Data analytical methods and programs overview
• Advanced theory and methods overview
• Technology, tools AND ACHIEVING RESULTS

By the end of this training course, delegates will be able to

• Proficiently identify Big Data sources and employ strategies for gathering and utilizing data effectively.
• Utilize Data Science and Analytics methodologies to extract valuable insights from large datasets.
• Apply clustering, forecasting, and unstructured data analysis models to uncover patterns and trends.
• Effectively communicate the benefits of Big Data Analytics to stakeholders, demonstrating its value proposition.
• Leverage Big Data to optimize team and organizational performance, driving informed decision-making and strategic initiatives.

TRAINING IS TAILORED TO

TRAINING METHODOLOGY

DixonTech's Master Class on Data Science & Big Data Analytics employs a dynamic and immersive methodology designed to equip participants with practical skills and in-depth understanding of data-driven decision-making. Through a combination of hands-on exercises, case studies, interactive lectures, and real-world applications, participants will engage with key concepts and techniques in data science and big data analytics. Our approach emphasizes experiential learning, allowing participants to explore various data sources, utilize advanced analytics tools, and apply machine learning algorithms to solve complex business challenges. Furthermore, participants will have the opportunity to collaborate with peers, share insights, and receive personalized feedback from industry experts. With a focus on practical application and real-world relevance, our training methodology ensures that participants are well-prepared to leverage the power of data to drive innovation and strategic growth within their organizations.

DAY ONE:

BIG DATA ANALYTICS

• Current Practices and trends in Big Data Analytics
• Business Intelligence v Data Science
• Analytical Architecture for Big Data
• Roles for Big Data within the Technology and Commercial Enterprise
• Key Drivers for Big Data Analytics
• Case Study and Summary

DAY TWO: 

DATA ANALYTICS MODELS AND LIFECYCLE

• Data Analytics Lifecycle
  • Discovery
  • Preparation of Data
  • Model Planning and Review
  • Model Creation
  • Communication Plan
  • From Planning to Operation
  • Practise

DAY THREE:

DATA ANALYTICAL METHODS AND PROGRAMS OVERVIEW

• Overview of R Framework
• Overview of 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

DAY FOUR: 

ADVANCED THEORY AND METHODS OVERVIEW

• Advanced Analytical Theory of Classification
• Advanced Analytical Theory of Time Series Analysis
• Advanced Analytical Theory of Textual Analysis
• Technology and Tools for Advanced Data Analytics
• Use Case and Assessment
• Case Study and Summary

DAY FIVE: 

TECHNOLOGY, TOOLS AND ACHIEVING RESULTS

• Unstructured Data Analytics
• Advanced Analytical Tools in Database Analytics
• How to integrate Data Analytics
• Current Best Practice Management and Approach for Project Delivery
• Data Visualization Overview