Data Science and Business Analytics Masterclass

The programme

Programme overview

Data Science and Business Analytics play pivotal roles in transforming vast amounts of data into actionable insights that drive effective decision-making and competitive advantages. This Masterclass provides a comprehensive understanding and practical application of the latest analytical techniques, programming languages, and strategic frameworks essential for today’s data-driven organizations.

Who should attend

Who is this programme for?

This Data Science and Business Analytics Masterclass course is suitable for:

• Data Managers
• Business Analysts
• Graduates and Scholars
• IT Professionals
• Project Managers
• Entrepreneurs
• Quality Analysts

Learning outcomes

What you will learn

• Certification upon completion
• Access to industry experts
• Networking opportunities with peers and professionals
• Skills to transform organizational data into strategic advantages

10-Day Training

Tools:

Tool | No. of Days | Purpose

Power BI | 1 | Business Intelligence, Data Analytics & Visualization

SQL | 1 | SQL Database: Structured Querying

Cassandra | 1 | No-sql Database

Python | 5 | Data Science Programming Language

Azure | 1 | Cloud Computing and Model Deployment

Apache Spark | 1 | High-level light-weight distributed computing

Total | 10 |

5-Day Training

Tools: Power BI, SQL, Python, Azure

Tool | No. of Days | Purpose

Power BI | 1 | Business Intelligence, Data Analytics & Visualization

SQL | 1 | SQL Database: Structured Querying

Python | 2 | Data Science Programming Language

Azure | 1 | Cloud Computing and Model Deployment

Total | 5 |

Participants will:

• Develop a robust understanding of data science and business analytics and their interconnectedness.
• Gain proficiency in analytical techniques and methodologies.
• Learn to apply data analytics strategically in real-world business scenarios.
• Build skills in data visualization, predictive modelling, and effective data communication.
• Enhance critical thinking, problem-solving, and strategic decision-making abilities.

Explore the programme

Detailed programme outline

Module 1: Introduction to Data Science

• Overview and scope of Data Science
• Big Data fundamentals and applications
• Descriptive statistics and hypothesis testing
• Data warehousing essentials
• Application in marketing, finance, and operations

Module 2: Business Analytics Essentials

• Understanding business intelligence and analytics
• Customer and risk analytics
• Detective analysis and root cause identification
• Data science vs. business analytics: Strategic roles
• Building effective analytics dashboards

Module 3: Techniques of Data Science

• Advanced statistical analysis (ANOVA, Regression)
• Dimension reduction methods
• Data and text mining techniques
• Supervised vs. unsupervised learning
• Exploratory data analysis (EDA)

Module 4: Forecasting and Predictive Analytics

• Fundamentals of predictive modelling
• Time series forecasting methods
• Advanced data science programming
• Machine learning algorithms and applications
• Optimization models: Linear and goal programming

Module 5: Data Exploration Techniques

• Objectives and methods of data exploration
• Qualitative vs. quantitative data analysis
• Steps for effective data exploration
• Visualization strategies for exploration

Module 6: Data Visualization and Storytelling

• Principles of effective visualization
• Creating interactive performance dashboards
• Visual analytics and insights
• Storytelling techniques to influence stakeholders

Module 7: Framing Business Problems

• Defining and formulating business problems
• Hypothesis generation and testing
• Transformative problem-solving strategies
• Identifying and setting dependent and independent variables

Module 9: Advanced Data Analysis

• Advanced data manipulation
• Sorting, filtering, conditional formatting
• Scenario analysis using What-if Analysis
• Building and interpreting Pivot Tables
• Automation of analytics

Module 8: Advanced Predictive Modelling

• Prediction vs. interpretation methodologies
• Data preprocessing and transformation
• Classification and clustering models
• Handling and interpreting outliers
• Validating and optimizing models

Module 10: Emerging Trends in Data Science

• Augmented analytics and AI-driven insights
• Data cleaning and preparation techniques
• Machine learning automation
• Cloud-based analytics solutions
• Ensuring data quality and governance
• Cognitive and prescriptive analytics

Module 11: Industry Applications of Data Science and Business Analytics

• Analytics in financial services: Fraud detection, risk management
• Customer analytics: CRM, customer segmentation, campaign management
• Data-driven decision making in IT and software industries
• Case studies: Retail, healthcare, manufacturing

Module 12: Capstone Project

• Practical application of course knowledge
• Hands-on projects solving real-world business problems
• Presentation and feedback sessions
• Project documentation and reporting standards

Learning approach

How you will learn

Our diverse instructional approaches ensure effective learning:

– Lectures & Presentations: Engage with expert-driven, stimulating content.– Course Material: Access well-crafted supporting resources.– Group Work: Collaborate on discussions and case studies for practical insights.– Workshops & Role-Play: Participate in immersive, scenario-based activities.– Practical Application: Focus on applying theoretical knowledge in real situations.– Post-Training Support: Receive extensive support after training for skill implementation.

Discuss your requirements

Dates, duration & arrangements

Contact Ratomac to confirm dates, duration, delivery format, entry requirements and any applicable assessment or certification before booking.