Certifications:  IBM Data Analyst or IBM Data Science Professional Certificate.

Level: Intermediate

• Tools like Python, SQL, Excel, and Jupyter Notebooks
• Practical analysis, data wrangling, and dashboarding

Ideal as a bridge between foundation and professional certification, especially for learners aiming at CAP or industry data roles.

3. Associate Certified Analytics Professional (aCAP)

✅ Requirements:

• Designed for entry-level professionals or recent graduates

• Must hold a degree in analytics or a related field

• No work experience required

📘 Exam Focus:

• Same seven domains as CAP, but focuses on knowledge and understanding, not application

🧭 Positioning:

• Pre-professional certification

• Best for students or early-career professionals

🎯 Comparable to BCS?

• Somewhat. While still introductory, aCAP leans more toward theoretical understanding of analytics as a process. BCS is more practice-oriented and aligned with IT/business analysis professionals.

Course Summary

The Intermediate Level focuses on applying data science methods to solve real-world problems. Participants will dive deeper into machine learning, and advanced data visualisation, and work with unstructured data. This hands-on course emphasises project-based learning and prepares learners for real-life data science tasks.

Course Outline

• Week 1-2: Advanced Data Visualisation
  1. Custom visualisations with Python (Matplotlib, Seaborn)
  2. Data storytelling and dashboard design
  3. Case studies in visualisation
• Week 3: Machine Learning Models
  1. Decision trees, Random forests, and K-Nearest Neighbors
  2. Feature engineering and model optimisation
  3. Hyperparameter tuning
• Week 4: Working with Unstructured Data
  1. Introduction to text data and natural language processing (NLP)
  2. Basic sentiment analysis
  3. Handling large datasets (Hadoop/Spark)
• Week 5-6: Predictive Analytics
  1. Building predictive models (time series, forecasting)
  2. Evaluating and fine-tuning models
  3. Case studies in predictive analytics
• Week 7: Hands-on Data Science Project
  1. Developing a data pipeline
  2. Presenting and defending model results
  3. Collaboration and feedback
• Week 8: Final Project Presentation
  1. Comprehensive project incorporating learned techniques
  2. Peer review and feedback

 Learning Objectives

1. Apply machine learning techniques to real-world datasets
2. Develop predictive models and evaluate their performance
3. Create advanced data visualisations to communicate complex insights
4. Work with unstructured data and use it in analysis

Who Should Attend

1. Analysts or data scientists with foundational knowledge
2. Professionals looking to apply machine learning to business problems
3. Teams seeking to implement data science projects in their organisations