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Programme Delivery

 All modules are either trainer-led remote training session or self-paced distance learning on our dedicated virtual learning environment (VLE).

Tutor support sessions ever six weeks.

 All trainer-led training days are delivered across three online sessions at the following times:

9am – 10:30am

11am – 12:30pm

1:30pm – 4pm


Programme Details

The Business Domain

  • Overview of how knowledge of the domain enables effective analysis
  • Explicit and Tacit knowledge
  • Principles of UX 
  • Introduction to creating reports and how to present them


Requirements and Data architecture

  • Data Lifecycle 
  • Data Analysis requirements and the requirement elicitation process
  • Quality of data 
  • Common sources of errors
  • Data roles and responsibilities within projects
  • Legislation and how it applies in data analysis


Data Collection and Manipulation

  • Types of data and files
  • Data filtering and profiling
  • Quantitative and Qualitative data
  • Extract, Transform and Load process
  • Data Validation and Verification
  • Data structures in R
  • Applying exploratory data methods in R


Data Analysis and Visualisation

  • Data modelling
  • Databases – Theory and how to collect and manipulate data to be used for analysis using SQL
  • Transform data sets using Power Query Editor
  • Clean data
  • Create new data sets using joins
  • Using PowerBI to create interactive dashboards
  • Using DAX to create measures


Statistical & Predictive Analysis

  • Statistics – measure of centre and spread
  • Normal distribution, outliers 
  • Descriptive analytics numerical and categorical data
  • Quantitative and Qualitative analysis
  • Clustering and text mining algorithms
  • Hypothesis testing
  • Linear and Logistic regression


Time Series Analysis & Sharing the results

  • Time Series Analysis 
  • Forecasting 
  • Summarising and presenting the results 
  • Classification methods
  • Interpret the results and make recommendations 
  • Techniques and methods to present data


AI and Machine Learning

  • Introduction to Artificial Intelligence and Machine Learning
  • The importance of AI and machine learning in data analysis
  • Neural Networks
  • Typical algorithms used in clustering analysis
  • Fuzzy logic
  • Evolutionary Computation


Certified Entry-Level Python Programmer (Opt)

  • Logic and structure
  • Control Flow
  • Literals and variables
  • Data collections
  • Operators
  • Functions
  • Data types
  • Exception handling
  • I/O operations


Assessment Gateway & EPA

  • EPA Preparation – Dedicated one-to-one sessions to support the learner as they head towards assessment, putting them in the best possible position for achievement
  • Assessment Phase – EPA can take up to 3 months to complete. This involves a Project with presentation and questioning and a Professional discussion with portfolio

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