Business Intelligence Analyst and Data Science Certification Course
Date | Format | Duration | Fees (GBP) | Register |
---|---|---|---|---|
10 Feb - 12 Feb, 2025 | Live Online | 3 Days | £1975 | Register → |
03 Mar - 21 Mar, 2025 | Live Online | 15 Days | £8675 | Register → |
07 Apr - 11 Apr, 2025 | Live Online | 5 Days | £2850 | Register → |
05 May - 13 May, 2025 | Live Online | 7 Days | £3825 | Register → |
23 Jun - 27 Jun, 2025 | Live Online | 5 Days | £2850 | Register → |
18 Aug - 22 Aug, 2025 | Live Online | 5 Days | £2850 | Register → |
29 Sep - 03 Oct, 2025 | Live Online | 5 Days | £2850 | Register → |
29 Oct - 31 Oct, 2025 | Live Online | 3 Days | £1975 | Register → |
08 Dec - 12 Dec, 2025 | Live Online | 5 Days | £2850 | Register → |
Date | Venue | Duration | Fees (GBP) | Register |
---|---|---|---|---|
07 Apr - 18 Apr, 2025 | Manchester | 10 Days | £8750 | Register → |
05 May - 23 May, 2025 | London | 15 Days | £12400 | Register → |
02 Jun - 06 Jun, 2025 | Nairobi | 5 Days | £4350 | Register → |
07 Jul - 25 Jul, 2025 | Munich | 15 Days | £12400 | Register → |
04 Aug - 22 Aug, 2025 | Cairo | 15 Days | £11200 | Register → |
01 Sep - 05 Sep, 2025 | London | 5 Days | £4750 | Register → |
06 Oct - 10 Oct, 2025 | Kigali | 5 Days | £4350 | Register → |
03 Nov - 14 Nov, 2025 | Amsterdam | 10 Days | £8750 | Register → |
01 Dec - 12 Dec, 2025 | Barcelona | 10 Days | £8750 | Register → |
Why Select this Training Course?
This course stands out as an ideal solution for professionals who are not just looking for data analysis proficiency but a comprehensive mastery of the kaleidoscope of skills required in the fields of Business Intelligence (BI) and Data Science. It provides an intricate blend of the art of decision-making supported by rigorous scientific analysis.
What sets this course apart from others?
This programme is crafted to equip you with robust analytical frameworks and the application of cutting-edge technology in real-world business scenarios. It moves beyond theory, delving into the core of data analysis and intelligence strategies that drive business growth.
How relevant is the curriculum?
The curriculum is continuously updated, reflecting the latest industry trends to ensure you are learning not just the fundamentals but the advanced concepts reshaping the future of business intelligence and data science.
Who Should Attend?
This course is designed for:
- Business Intelligence Analysts
- Data Analysts
- Data Scientists
- Aspiring BI and Data Science Professionals
- IT Professionals transitioning to Data Roles
- Business and Data Consultants
- Business Strategists and Managers
- Marketing Analysts
What are the Course Objectives?
Upon completion, you will:
- Understand and use tools and techniques that underpin data analysis and business intelligence.
- Understand advanced data science methods to extract actionable insights.
- Develop analytic models that can be used in decision-making.
- Know the data life cycle and how to manage large data sets.
- Communicate complex data findings in a comprehensible manner to stakeholders.
How will this course be presented?
The course will feature:
- Interactive lectures on theoretical concepts and real business applications.
- Practical sessions with the latest BI and data science software.
- Case studies dissecting successful business intelligence strategies.
- Collaborative projects to simulate real-world data analysis scenarios.
- Access to a range of online learning resources for self-study and revision.
What are the Topics Covered in this Course?
Module 1: Customer Intelligence and Relationship Management
- Building a 360-degree view of the customer.
- Data-driven customer relationship management (CRM).
- Predictive analysis for customer lifetime value.
- Personalisation and targeted marketing.
- Integrating CRM systems with BI for enriched customer insights.
Module 2: Data Warehousing and ETL Processes
- Architecture of a data warehouse.
- ETL (Extract, Transform, Load) fundamentals.
- Data modelling concepts: star and snowflake schema.
- Best practices in data warehouse management.
- Advanced ETL tools and techniques.
Module 3: Data Mining and Predictive Analytics
- Data mining processes and methodologies.
- Predictive modelling techniques.
- Customer profiling and segmentation analysis.
- Pattern detection and hypothesis testing.
- Prescriptive analytics and optimisation methods.
Module 4: Machine Learning for Predictive Modelling
- Machine learning algorithms and their business applications.
- Supervised versus unsupervised learning.
- Feature engineering and selection.
- Model tuning and evaluation.
- Deploying machine learning models.
Module 5: Data Visualisation and Storytelling
- Principles of data visualisation.
- Advanced visualisation tools (Tableau, Power BI).
- Crafting a narrative around data.
- Designing interactive data experiences.
- Visualisation techniques for big data.
Module 6: Big Data Technologies and Frameworks
- Overarching concepts of big data.
- Utilisation of Hadoop and Spark ecosystems.
- Real-time analytics with Apache Kafka.
- Managing NoSQL databases.
- Leveraging big data in cloud computing environments.
Module 7: Advanced Statistical Analysis
- Advanced statistical tests and when to use them.
- Time series analysis and econometrics.
- Multivariate statistical methods.
- Survival analysis and hazard models.
- Bayesian statistics applications.
Module 8: Data Science Project Management
- Defining project scopes and goals in data science.
- Agile methodologies in data-driven projects.
- Team collaboration and leadership in cross-functional teams.
- Delivering BI and Data Science projects.
- Communicating results to stakeholders.
Module 9: Artificial Intelligence in BI
- AI concepts influencing business intelligence.
- Natural language processing for BI insights.
- Neural networks and deep learning applications.
- The role of AI in predictive analytics.
- AI ethics and governance.
Module 10: The Future of Business Intelligence and Data Science
- Trends shaping the future of BI and Data Science.
- The convergence of BI and AI.
- Strategies for staying current in the field.
Module 11: Advanced SQL for Data Analytics
- Complex SQL queries for data mining and analytics.
- Performance tuning and optimisation of SQL queries.
- SQL analytical functions for in-depth data analysis.
- Database design for scalable BI solutions.
- Integrating SQL with other data analysis tools and languages.
Module 12: Python and R for Data Science
- Python Data Manipulation with the Pandas library.
- Statistical Analysis and Data Visualisation with R.
- Execution and optimising Python and R code for the data analysis tasks.
- Integration of Python and R into the organisation of databases and BI tools.
- Using Python and R to construct data building and data consumption APIs.
Module 13: Advanced Analytics in Excel
- Data management using variable formulas and functions in Excel.
- Excel’s ability to construct predictive models.
- Automation with VBA scripting.
- Getting to grips with Power Query and Power Pivot for the sophisticated data sets.
- Excel as a fast-paced BI tool which supports creation of real-time data visualisations and dashboards.
Module 14: Cloud Computing and Analytics
- Using a combination of AWS, Azure, and Google Cloud for analytics.
- Cloud storage and data lake services.
- Cloud-based machine learning services.
- Establishing and operating scalable BI solutions.
- Implementing data security measures and establishing compliance procedures in cloud environments.
Module 15: Social Media and Web Analytics
- Gleaning data analytics from social networks.
- Web analysis resulting in traffic utilising Google Analytics.
- Emotion perception and online reputation management.
- Gaining insight into the customer’s path via web analytics.
- Social data in analysis and forecast of market trends.
Module 16: Cognitive Computing and BI
- Instruction about cognitive computing basics.
- Aiding businesses in identifying cognitive insights with IBM Watson.
- Integrating cognitive computing into BI strategies.
- Applying and depicting cases of cognitive computing in different industry sectors.
- Understanding future trends and opportunities in cognitive computing means for business intelligence.
Module 17: BI for Decision-Making and Strategy
- Energising BI processes by aligning BI and corporate strategy and decision-making.
- Scenario analysis and hypothetical simulations.
- The use of big data for risk detection and mitigation.
- Assessing the application of BI in championing innovation and organisational transformation.
- BI in decision-making.
Module 18: Integrating BI with Corporate Performance Management (CPM)
- KPIs, scorecards, and charts of performance tracking.
- Twofold role of BI which is considered a driver of performance management.
- CPM software and their integration as a filter with business intelligence systems.
- Case studies of CPM transforming business operations.