Level 7 Diploma in Data Science

Introductory Lesson
Exploratory Data Analysis
3 Topics
LO1: Handle and manage multiple datasets within R and Python environments.
LO2: Use measures of central tendency to summarize data and assess both the symmetry and variation in the data.
LO3: Present and summarise distributions of data and the relationships between variables graphically.
Statistical Inference
3 Topics
LO1: Evaluate standard discrete and standard continuous distributions.
LO2: Formulate research hypotheses and perform hypothesis testing.
LO3: Analyse the concept of variance (ANOVA) and an select an appropriate ANOVA or ANCOVA model.
Fundamentals of Predictive Modelling
3 Topics
LO1: Carry out global and individual testing of parameters used in defining predictive models.
LO2: Validate assumptions in multiple linear regression.
LO3: Validate models via data partitioning, out of sample testing and cross-validation.
Advanced Predictive Modelling
3 Topics
LO1: Develop models using binary logistic regression and assess their performance.
LO2: Develop applications of multinomial logistic regression and ordinal logistic regression.
LO3: Develop generalised linear models and carry out survival analysis and Cox regression.
Time Series Analysis
3 Topics
LO1: Assess the concepts and uses of time series analysis and test for stationarity in time series data.
LO2: Validate ARIMA (Auto Regressive Integrated Moving Average) models and use estimation.
LO3: Implement panel data regression methods.
Unsupervised Multivariate Methods
3 Topics
LO1: Define Principal Component Analysis (PCA) and its derivations and assess their application.
LO2: Understand hierarchical and non-hierarchical cluster analysis and assess their outputs.
LO3: Evaluate the concept of panel data regression and implement panel data methods.
Machine Learning
3 Topics
LO1: Appraise classification methods including Naïve Bayes and the support vector machine algorithm.
LO2: Apply decision tree and random forest algorithms to classification and regression problems.
LO3: Analyse Market Baskets and apply neural networks to classification problems.
Further Topics in Data Science
5 Topics
LO1: Perform text mining on social media data.
LO2: Develop web pages using the SHINY package.
LO3: Apply the Hadoop framework in Big Data Analytics.
LO4: Evaluate the fundamental concepts of artificial intelligence.
LO5: Use SQL programming for data analysis.
Contemporary Themes in Business Strategy
4 Topics
LO1: Evaluate the concept of transformation and the key technologies that drive it.
LO2: Assess the strategic impact of the application of Big Data and Artificial Intelligence on business organisations.
LO3: Appraise theories of innovation and distinguish between disruptive and incremental change.
LO4: Evaluate ethics practices within organisations and how they relate to issues in Data Science.
Summary of the Programme
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Time Series Analysis

Level 7 Diploma in Data Science Time Series Analysis
Lesson Content
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LO1: Assess the concepts and uses of time series analysis and test for stationarity in time series data.
LO2: Validate ARIMA (Auto Regressive Integrated Moving Average) models and use estimation.
LO3: Implement panel data regression methods.
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