• Understand essential strategic tools, including STEEP, PEST, SWOT, Porter's Five Forces, and more.
• Explore customer-centric models like the Kano Model and Quality Function Deployment.
• Develop skills to analyse and respond to customer requirements effectively.
• Learn data analysis techniquesincluding data distributioncorrelationsand regression analysis.
• Apply strategic and customer tools to improve business decision-making.
• Identify key success factorsindustry signalsand opportunities for your organisation.
• Enhance your strategic thinking and problem-solving abilities.

• Managers, supervisors and data analytic personnel who will use this in their day-to-day role.
• Anyone who wishes to have a greater understanding of analytics and analysis.

• Introduction to Strategic Thinking
• Overview of STEEP and PEST Models
• Practical exercises on conducting external environmental scans
• Dive into Porter's Five Forces Model
• Application of SWOT Analysis

• Introduction to Ansoff Matrix
• Practical exercises to evaluate market strategies
• SPACE Matrix explained

• Introduction to Customer-Centric Thinking
• Overview of the Kano Model
• Application of customer segmentation for strategic focus
• Assigning values to customers and ranking based on expectations
• Quality Function Deployment model for customer requirements

• Introduction to the Buyer Utility Map
• Exercises in generating new customer satisfaction strategies
• Conducting customer surveys and data collection techniques

• Introduction to Data Analysis with Statistics
• Recognising data analysis approaches
• Basics of quality control and reducing errors
• Overview of Quantitative vs. Qualitative data and levels of measurement
• Designing ways of expressing data distributions visually
• Identifying necessary measures for describing data distributions
• Representing correlations in data and introduction to regression analysis

Cost: 4900 USD

Duration: 5 Days

• Prepare data for PivotTable reporting and create PivotTables from various data sources
• Analyse Data Using PivotTables
• Work with Pivot Charts

• Preparing Data and Creating PivotTables
• Prepare Data.
• Create a PivotTable from a Local Data Source.
• Create a PivotTable from Multiple Local Data Sources.
• Create a PivotTable from an External Data Source.
• Analysing Data Using PivotTables
• Summarize PivotTable Data
• Organize PivotTable Data
• Filter PivotTable Data
• Format a PivotTable
• Refresh and Change PivotTable Data
• Working with Pivot Charts
• Create a PivotChart
• Manipulate PivotChart Data
• Format a PivotChart

Cost: 4900 USD

Duration: 5 Days

• Improved Data Analytics Skills
• Career Advancement
• Better Decision Making
• Competitive Advantage
• Increased Earning Potential
• Overalltaking a course on big data analysis can provide individuals with valuable skills and knowledge that are in high demand across many industriesleading to improved career prospectsbetter decision-makingand a competitive advantage for their organisations.

1. 1. Data analysts and data scientists who want to learn new techniques and tools for analysing large data sets.
2. 2. Business analysts who want to use data-driven insights to make better decisions and improve business performance.
3. 3. IT professionals are responsible for managing and maintaining large databases and data warehouses.
4. 4. Developers who want to learn how to build scalable and efficient data processing systems.
5. 5. Managers and executives who want to better understand big data technologies and how they can be used to drive business innovation and growth. Overall, anyone who wants to learn how to work with big data and extract valuable insights from it can benefit from attending a course on big data analysis.

• Introduction to Big Data & Big Data Analysis
• Understanding Big Data & its Importance
• Characteristics of Big Data
• Data Analysis and its role in big data

• Big Data Technology
• Introduction to Big Data Technologies
• Hadoop and its ecosystem
• Spark and its ecosystem

• Data Storage and Retrieval
• Data Storage and Retrieval technologies
• NoSQL Databases (MongoDB Cassandra)
• Distributed File Systems (HDFS Amazon S3)

• Data Processing and Analysis
• Data Processing analysis technologies
• Data Modelling and preparation
• Data Analysis Techniques (statistical analysis machine learning)

• Data Visualisation and Business Intelligence
• Data Visualisation and its importance
• Visualisation tools and techniques
• Introduction to business intelligence and analytics

Cost: 4900 USD

Duration: 5 Days

• Profound insight into the statistical underpinnings of artificial intelligence.
• Comprehensive understanding of machine learning principles and their practical applications.
• Mastering deep learning modelsactivation functionsweights initialisation strategiesand regularisation techniques.
• In-depth comprehension of Natural language processing NLP.
• Hands-on experience through engagement in a practical project.
• Exploration of diverse AI applications within the realm of businesses.
• Gain expertise in discerning and leveraging business opportunitiesadeptly crafting compelling use cases.

• Data Analysts
• Data Scientists
• Business Analysts & Researchers
• Machine Learning Professionals
• Anyone with an Interest in Deep Learning

• Understand data and exploratory data analysis (EDA).
• Measures of central tendency dispersion and correlation.
• Introduction to Machine Learning.
• Introduction to supervised learning algorithms.
• Introduction to unsupervised learning.
• Introduction to reinforcement learning.
• Complete ML workflow.

• Introduction to Deep Learning
• Neural Networks (NN).
• Feedforward neural networks.
• Activation functions.
• Backpropagation.
• Regularisation.
• Dropout.
• Optimisation algorithms.
• Weight initialisation.
• Batch normalisation.
• Hyper parameter tuning.

• Introduction to Natural Language Processing (NLP).
• Understand NLP tasks.
• Understand NLP cycle.
• Bag of Words (BoW).
• Word Embeddings.
• Word2Vec.
• Contextualised word embeddings.
• BERT.

• Deep Learning project.
• Problem identification.
• Value identification.
• Project setup.
• Project implementation.
• Evaluation.

• Discriminative AI applications.
• Generative AI applications.
• Building a business use case.
• Course wrap-up.

Cost: 4900 USD

Duration: 5 Days

• Gain a comprehensive understanding of data management principlesprocessesand technologies.
• Develop data collectioncleaningand integration strategies from various sources.
• Master data modelling techniques for efficient data organisation and storage.
• Learn to apply data quality control measures to ensure data accuracy and consistency.
• Explore critical concepts of data governance and data security best practices.
• Understand data analytics techniques and how they leverage managed data for insights.
• Develop skills for data visualisation to communicate data stories effectively.
• Stay ahead by exploring emerging data management and analytics trends.

• Data Analysts and Business Analysts: Learn advanced data preparation and analysis techniques.
• Data Scientists: Gain a deeper understanding of data management principles for robust data analysis projects.
• Database Administrators (DBAs): Develop robust data modelling and data security practices.
• IT Professionals: Understand how data management integrates with overall IT infrastructure.
• Business Managers and Executives: Gain insights into leveraging data for strategic decision-making.
• Marketing Professionals: Learn to use data for targeted marketing campaigns and customer insights.

• Introduction: The importance of data management in the digital age and its impact on business success.
• Data Lifecycle Management (DLM): Understanding the different stages of data from creation to archiving.
• Data Quality Fundamentals: Defining data quality standards and implementing data cleaning techniques.
• Data Governance Framework: Establishing a framework for data ownership access control and data security.

• Data Collection Techniques: Exploring various methods for data collection from internal & external sources.
• Data Integration: Mastering strategies to combine data from diverse formats and sources into a unified system.
• Data Warehousing & Business Intelligence (BI): Understanding the role of data warehouses and BI tools for data analysis.

• Data Modelling Fundamentals: Conceptual logical and physical data models for efficient data organisation.
• Entity-Relationship Diagrams (ERDs): Visual representations of data relationships between entities and attributes.
• Data Normalisation Techniques: Minimising data redundancy and corruption for optimal data integrity.

• Data Security Threats: Understanding common data security risks and implementing data protection measures.
• Access Control Mechanisms: Developing user roles and permissions for secure data access and management.
• Data Encryption & Anonymisation: Learning techniques to secure sensitive data and protect privacy.
• Data Backup & Disaster Recovery: Creating robust strategies for data backup recovery and business continuity.

• Introduction to Data Analytics: Exploring different data analytics techniques to extract valuable insights from data.
• Introduction to Data Visualisation Tools: Learning to create compelling data visualisations for effective communication.
• Storytelling with Data: Developing skills to communicate complex data insights to stakeholders clearly and concisely.
• The Future of Data Management: Emerging trends in data management including Big Data Cloud Computing and Artificial Intelligence (AI).

Cost: 4900 USD

Duration: 5 Days