Big Data for Managers Certification Course

Why select this training course?

A data set is considered “big data” when it is so huge or complicated that more conventional data analysis methods prove insufficient. Big data management is the same as data management but focuses on research and analytics to deal with huge data in various formats, like text, numbers and pictures. Big data’s required quantity and complexity have created challenges for data management, requiring sophisticated techniques to extract value from the collected data.

Why is big data becoming so important for modern-day organizations?

 Big data gives you new insights and opportunities for improvement. Advanced analytics tools analyze the multiple datasets, including unstructured data and text logs, in your transaction processing systems to give you new insights for a competitive advantage.

What is the role of a big data manager? 

A Big Data Manager is responsible for developing and managing business-oriented systems. The manager’s responsibility for business intelligence and big data analytics solutions is to ensure good data quality and accessibility. They need to be able to analyze the organization’s current processes, determine how they can be improved, and execute changes to improve them.

Big data managers manage and analyze massive amounts of unstructured, semi-structured and structured data. They ensure that business data is accessible for application development and support managers. Big Data Managers also work to ensure that data remains relevant through business cycles by performing detailed analysis, performance tuning and managing new technology implementations.

Rcademy’s Big Data for Managers Certification Course will help you make sense of the big data hype, giving you an understanding of both its potential benefits and risks. You’ll learn what makes a dataset “big” and how to properly store, clean, and analyze that data to be useful to your business.

Big Data Managers are in high demand across various industries. Rcademy’s Big Data for Managers Certification Course gives you the necessary skills, knowledge and expertise to initiate big data projects successfully. This certification will teach you to analyze, interpret and communicate information from various sources. It focuses on providing a fundamental understanding of Big Data, the current market situation, the most important Big Data technologies, and various data mining methods. It is designed specifically for professionals who are expected to make decisions concerning Big Data based on their knowledge or the basis of expert recommendations.

Who should attend?

The course is a pre-requisite for individuals looking to shape themselves as an asset for their data-centric organizations:

• Data analysts
• Chief Data Officers
• Data Governance managers
• Data Scientists
• Data administrators
• Business analysts
• Managers and professionals from different walks of life
• Financial analysts/financial statement analysts
• Quantitative analysts
• Finance managers/strategic managers
• Entrepreneurs

What are the course objectives?

Rcademy’s Big Data for Managers Certification Course has been designed with the following objectives:

• To develop the necessary skills within participants needed to be successful big data managers
• To develop an understanding of key concepts of big data and the interplay of five Vs
• To understand the application and implication of Big Data on business operations
• To learn to analyze big data to reveal valuable insights and opportunities
• To improve the quality of decision-making through big data analytics
• To learn about tools and techniques available to store and analyze big data
• To develop communication channels to effectively convey data changes or requirements to other staff
• To encourage data-oriented decision making
• To recognize emerging trends and best practices opted by managers globally for Big Data Management
• To create value from big data generation

How will this course be presented?

• Interactive sessions
• Live Projects
• Use of case studies
• Management games
• Learning preparation of reports, charts, graphs
• Real-time exercises
• Problem-solving and Group discussion sessions

What are the topics covered in this course?

Module 1: Introduction to Big Data

• Origin of Big Data
• Why is it important?
• The Implication of Big Data
• Big Data Analytics
• Cloud Computing
• Structured and Unstructured Big Data
• Challenges of Big Data

Module 2: Three Vs of Big Data

• Volume of Data
• Velocity
• Velocity
• Two new V: Value and Veracity

Module 3: Use Cases of Big Data

• Customer Experience
• Product Development
• Machine Learning
• Risk identification and mitigation
• Customer acquisition and retention
• Forecasting and Price optimization

Module 4: Impact of Big Data

• Big data generation sources
• Becoming data-driven
• Data warehousing
• Handling big data through the cloud

Module 5: Working on Big Data

• Deciding Big Data Strategy
• Integration of Data
• Extract, transform and Load
• Storing of Big Data: Data warehouses and Data Lakes, Data Pipelines
• Analysing of Big Data: Grid computing, in-memory analytics

Module 6: Exploratory Data Analysis

• Data analytics lifecycle
• Dealing with missing data
• Improving the accuracy of the analysis
• Data summarisation
• Data visualization

Module 7: Big Data Technologies

• Big data ecosystems
• Pig and Hive architecture
• Recent Trends in Big Data

Module 8: Global Practices for Big Data Managers

• Centre of excellence
• Standardization of governance
• Aligning structured and unstructured data
• Discovery lab

Module 9: Big Data Analytics Techniques

• Data fusion
• Data Integration
• A/B Testing
• Data Mining
• Machine Learning
• Natural Language Processing

Module 10: Statistical Techniques for Big Data

• Regression analysis
• Association rule learning
• Predictive modeling

Module 11: 

Creating Value from Big Data

• Data democratization
• Data contextualisation
• Data experimentation
• Data Execution

Module 12: Introduction to HADOOP

• RDBMS vs HADOOP
• Basics of HADOOP
• HADOOP distributed file system
• Data processing with HADOOP