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Level 6 Diploma in Information Technology

Data Preprocessing Pitfalls and Data Quality

IT602 · Advanced Data Analytics — Week 4 of 16

CourseIT602
Week4 of 16
SemesterI
TutorOnline in the course chat

This week’s topics

Data Preprocessing Pitfalls and Data Quality

  1. Handling missing data and imputation bias
  2. Anomaly and outlier detection
  3. Data leakage and class imbalance
  4. The Trouble with Missing Data - Computerphile
  5. What is data quality? - Dr Olga Maslovskaya
  6. Data Preprocessing in ML

What you will be able to do

  • 2.1 Evaluate analytical model data preparation processes.
  • 2.2 Critically evaluate potential issues in the preparation of data for use in an applied analytical model.

Activity

Start with this week’s 3 videos, then: Seminar, guided reading and a short written task: evaluate analytical model data preparation processes.

Study materials

Videos

  • The Trouble with Missing Data - Computerphile · YouTube · Computerphile · 10 min · for registered students
  • What is data quality? - Dr Olga Maslovskaya · YouTube · National Centre for Research Methods (NCRM) · 19 min · for registered students
  • Data Preprocessing in ML · YouTube · Neso Academy · 23 min · for registered students

Further resources

  • Principles of Data Science, ch. 2: Collecting and Preparing Data · PDF · OpenStax, textbook chapter; pp. 67–112 · for registered students
  • MIT 15.071 The Analytics Edge (Spring 2017) · Reading · MIT OpenCourseWare, course · for registered students
Materials marked “for registered students” open in the student portal, which is set up as soon as you receive final acceptance.
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Admitted students ask questions on this week’s page in the student portal: an AI assistant trained on the course answers first, and during the course’s timetabled classes Connect to your tutor brings your tutor into the chat, who may start a call if needed.

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Course plan
  • Week 1: Foundations of Data Analytics
  • Week 2: Evaluation of Analytical Frameworks
  • Week 3: Data Ingestion and Pipeline Architecture
  • Week 4: Data Preprocessing Pitfalls and Data Quality
  • Week 5: Applied Descriptive Analytics
  • Week 6: Predictive Analytics for Trend and Demand Forecasting
  • Week 7: Advanced Predictive Classification Models
  • Midterm: Midterm evaluation
  • Week 9: Prescriptive Analytics and Optimisation Modelling
  • Week 10: Model Output Visualisation and Visual Analytics
  • Week 11: Applied Model Synthesis, Governance, and Visual Interpretat…
  • Week 12: Applying Advanced Data Analytics in practice: case studies
  • Week 13: Assignment workshop: research, planning and referencing
  • Week 14: Assignment workshop: drafting, feedback and improvement
  • Week 15: Review and coursework completion
  • Final: Final assessment and submission
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