INFORMATION TECHNOLOGY PROGRAMS
Post-Graduate Certificate
Level 7 Diploma in Data Science
From Algorithms to Action — Build Your Expertise in Advanced Data Science
Course Overview
Level 7 Diploma in Data Science is a postgraduate-level qualification designed to empower learners with the skills and knowledge to explore, analyze, and interpret complex data sets and translate them into actionable insights. The diploma focuses on enabling professionals to formulate data-driven research hypotheses, uncover hidden patterns, and challenge conventional business thinking through the use of cutting-edge tools and methodologies.
This qualification prepares students to become data leaders who can support decision-making in modern organizations by applying advanced statistical, computational, and machine learning techniques. Learners will gain hands-on experience with industry-relevant tools such as Python, R, and SQL, enhancing both their technical abilities and strategic understanding.
Whether learners are aiming to enhance their professional expertise, transition into data-focused roles, or prepare for further academic pursuits such as a Master’s degree, the Level 7 Diploma in Data Science offers a powerful foundation for success.
Entry Requirements
To be eligible for this programme, applicants should meet the following criteria:
A relevant Level 6 qualification or equivalent professional experience in IT or a related field.
Be aged 21 years or older.
A strong grasp of English (IELTS 6.5 or equivalent) if the applicant’s first language is not English.
International qualifications are assessed for UK equivalency.
Applicants may be asked to provide academic or professional references and a statement of purpose.
Applicants with significant industry experience may also be admitted through Recognition of Prior Learning (RPL).
Qualification Structure
All units are mandatory.
Exploratory Data Analysis
Learning UI about basics rule of programming in both R and Python
Create and import external datasets in R and python
Export R data frames into external flat files
Data Management in R and Python (Sort, merge, aggregate and subset)
Introduction to basic concepts of Statistics, such as measures of central tendency, variation, skewness, kurtosis
Frequency tables crosstabs and bivariate correlation analysis
Data visualization: what and why? Grammar of graphics, handling data for visualization
Commonly used charts and graphs using ggplot2 package in R and matplotlib in python
Advanced graphics in R and Python Data Management in R and Python (Sort, merge, aggregate and subset)
Data Management in R and Python (Sort, merge, aggregate and subset)
Statistical Inference
Concept of random variables and statistical distribution
Discrete vs. Continuous Random Variables
Standard discrete distributions-Bernoulli, Binomial and Poisson
Using R to calculate probabilities
Fitting of discrete distributions to observed data
Standard continuous distributions-Normal, Log Normal, Exponential
Introduction to sampling distributions
Statistical Hypothesis Testing-concepts and terminology
Parameter, test statistics, level of significance, power, critical region
Parametric vs. non-Parametric Tests
t tests (one sample, independent samples, paired sample)
F test for equality of variance
Z tests for proportions (single and independent samples)
Non-parametric tests (Mann-Whitney U, Wilcoxon's signed rank)
Tests for Normality, Q-Q plot
What is analysis of variance?
Definitions: Variable, factor, levels
One Way Analysis of Variance
Two Way Analysis of Variance (including interaction effects)
Multi Way Analysis of Variance
Analysis of Covariance
Kruskal-Wallis Test
Friedman Test
Fundamentals of Predictive Modelling
Concept of random variables and statistical distribution
Concept of a statistical model
Estimation of model parameters using Least Square Method
Interpreting regression coefficients
Assessing the goodness of fit of a model
Global hypothesis testing using F distribution
Individual testing using t distributions
Concept of Multicollinearity
Calculating Variance Inflation Factors
Resolving problem by dropping variables
Ridge regression method
Stepwise regression as a strategy
Residual analysis
Shapiro Wilk test, K-S test and Q-Q plot for residuals
White’s test and Breusch-Pagan Test
Partitioning data using the caret package
Model development on training data
Model validation on testing data using R squared and RMSE
Concept of k-fold cross validation
Performing k-fold cross validation using the caret package
Identifying influential observations
Advanced Predictive Modelling
Model definition and parameter estimation
Estimation of model parameters using MLE
Interpreting regression coefficients and odds ratio
Assessing goodness of fit of the model
Global hypothesis testing using LRT distribution
Individual testing using Wald’s test
Classification table
ROC curve
K-S Statistic
Multinomial and Ordinal Logistic Regression - model building and parameter estimation
Interpretation of regression coefficients
Classification table and deviance test
Concept of GLM and link function and .GLM
Poisson Regression
Negative Binomial Regression
Survival Analysis Introduction
Cox Regression
Time Series Analysis
Components of time series
Seasonal decomposition
Trend analysis
Auto-correlogram
Partial auto-correlogram
Dickey-Fuller test
Converting non-stationary time series data into stationary time series data
Concepts of AR, MA and ARIMA models
Model identification using ACF and PACF
Parameter estimation
Residual analysis (testing for white noise process)
Selection of optimal model
What is Panel data?
Need for different models for Panel data
Panel data regression methods
Dummy variable method
Random effect model
Unsupervised Multivariate Methods
Concept of Data reduction
Definition of first, second, … ph principal component
Deriving principal component using Eigenvectors
Deciding optimum number of principal components
Developing scoring models using PCA
Principal component regression
Orthogonal factor model
Estimation of loading matrix
Interpreting factor solution
Deciding optimum number of factors
Using factor scores for further analysis
Factor rotation
Concept of MDS
Variable reduction using MDS
Concept of cluster analysis
Hierarchical cluster analysis methods (linkage methods)
Using dendrogram to estimate optimum number of clusters
k-means clustering methods
Using k-means runs function in R and Python to find optimum number of k
Machine Learning
Bayes theorem and its applications
Constructing classifier using Naïve Bayes method
Concept of Hyperlane
Support vector machine algorithm
Comparison with Binary Logistic Regression
Basics of Decision Tree
Concept of CART
CHAID algorithm
ctree function in R
Bootstrapping and bagging
Random forest algorithm
Definitions of support, confidence and lift
Aprioiri algorithm for market basket analysis
Neural network problem for classification problem
Further Topics in Data Science
What is text mining?
Term Document Matrix
Word cloud
Establishing connection with Twitter using twitteR package and Tweepy in Python
Introduction to SHINY
Introduction to R Markdown
Build dashboards
Host standalone apps on a webpage or embed them in R Markdown documents or build
dashboards.
What is Big Data?
Features of Big Data (Volume, Velocity and Variety)
Big Data in different industries (Healthcare, Telecom, etc.)
HADOOP architecture
Introduction to R HADOOP package
What is AI and Theory behind AI
What is Q learning
The Monte Carlo theory
SQL programming Basics
Data Wrangling and analysis
Text mining of Twitter data
Contemporary Themes in Business Strategy
Fundamentals of Cloud Computing
Compare and contrast cloud computing with traditional computing models
Software as a Service
Platform as a Services
Infrastructure as a Service
Business impact of Cloud Computing
Historical development of Artificial Intelligence
Vs of data - Volume, velocity, variety, veracity and value
Christensen’s theory of disruptive innovation
Ethical dilemmas and issues in Artificial Intelligence and Big Data
Key Outcomes
Graduates of this program will be able to:
Demonstrate core mathematical and statistical knowledge required for both basic and advanced data analysis.
Exhibit proficiency in R, Python, and SQL, applying them in practical, real-world data science tasks.
Understand the principles of data management, including cleaning, structuring, and evaluating datasets.
Use modern data visualization tools and techniques to communicate insights effectively.
Apply classical data analytics methods, such as statistical inference, predictive modeling, and dimensionality reduction.
Implement machine learning models to analyze business and organizational problems.
Understand contemporary business themes relevant to strategic planning and execution.
Evaluate and apply data science concepts within a strategic business context, helping organizations make evidence-based decisions.
Duration and Delivery
This diploma is designed to be completed in 9 to 12 months, depending on the study pace and delivery mode.
Modes of delivery include:
Blended learning (on-campus + online)
Fully online, tutor-supported learning
Interactive seminars, webinars, and case-study workshops
Individual and group project-based assignments
Assessment and Verification
The qualification is assessed through practical, work-related assignments designed to reflect real-world tasks. Each unit requires learners to demonstrate subject knowledge, critical thinking, problem-solving, and the ability to make informed recommendations. Assignments are aligned with specific learning outcomes and assessment criteria, incorporating relevant theories and concepts.
Learners are expected to apply their understanding to real organisational contexts, with mature or part-time learners encouraged to draw from personal work experience. Assessments are written to ensure academic rigour appropriate for Level 7 study. Sample assessments and marking schemes are available upon request..
Progression Opportunities
This qualification enables learners to:
Progress to a Master’s degree in IT, Data Science, Cybersecurity, Information Systems, or related disciplines.
Enter or advance within professional roles such as IT Project Manager, Data Analyst, Systems Architect, Network Consultant, or IT Director.