Advanced Data Analytics for Business L4

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