Qualifi Level 7 Diploma in Data Science

What you'll be able to do

• Develop models using binary logistic regression techniques.
• Assess the performance of binary logistic regression models.
• Develop applications of multinomial logistic regression.
• Develop applications of ordinal logistic regression.
• Develop generalised linear models.
• Carry out survival analysis.
• Apply Cox regression techniques.

What you'll be able to do

• Evaluate the concept of transformation within business strategy and the key technologies that drive it.
• Assess the strategic impact of the application of Big Data and Artificial Intelligence on business organisations.
• Appraise theories of innovation and distinguish between disruptive and incremental change within a business context.
• Evaluate ethics practices within organisations and how they relate to issues in Data Science.

What you'll be able to do

• Handle and manage multiple datasets effectively within R and Python environments
• Use measures of central tendency, such as mean, median, and mode, to summarise data sets
• Assess the symmetry and variation in data using appropriate statistical measures
• Present and summarise distributions of data using graphical techniques
• Summarise the relationships between variables graphically using appropriate visualisations

What you'll be able to do

• Carry out global testing of parameters used in defining predictive models to assess their overall significance
• Carry out individual testing of parameters used in defining predictive models to determine their specific contributions
• Validate assumptions in multiple linear regression, such as linearity, independence, and homoscedasticity
• Validate models via data partitioning into training and testing sets to assess their generalisability
• Validate models via out-of-sample testing using independent data to evaluate their predictive performance
• Validate models via cross-validation techniques to obtain robust estimates of their accuracy

What you'll be able to do

• Perform text mining on social media data to extract relevant insights.
• Develop web pages using the SHINY package for data visualisation and interaction.
• Apply the Hadoop framework in Big Data Analytics to process and analyse large datasets.
• Evaluate the fundamental concepts of artificial intelligence and their application in data science.
• Use SQL programming for data analysis, including data extraction, transformation, and loading.

What you'll be able to do

• Appraise classification methods including Naïve Bayes and the support vector machine algorithm.
• Apply decision tree and random forest algorithms to classification and regression problems.
• Analyse Market Baskets and apply neural networks to classification problems.

What you'll be able to do

• Evaluate standard discrete distributions, such as binomial and Poisson distributions
• Evaluate standard continuous distributions, such as normal and exponential distributions
• Formulate research hypotheses based on theoretical frameworks and research questions
• Perform hypothesis testing using appropriate statistical tests and significance levels
• Analyse the concept of variance (ANOVA) to compare means across multiple groups
• Select an appropriate ANOVA or ANCOVA model based on the research design and data characteristics

What you'll be able to do

• Assess the fundamental concepts and practical uses of time series analysis in various contexts
• Test for stationarity in time series data using appropriate statistical methods
• Validate Auto Regressive Integrated Moving Average (ARIMA) models to ensure their accuracy and reliability
• Use estimation techniques to determine the parameters of ARIMA models
• Implement panel data regression methods to analyse data with both time series and cross-sectional dimensions

What you'll be able to do

• Define Principal Component Analysis (PCA) and its derivations, including its mathematical foundations
• Assess the application of PCA and its derivations in dimensionality reduction and feature extraction
• Understand hierarchical cluster analysis methods and assess their outputs in terms of cluster structure and interpretation
• Understand non-hierarchical cluster analysis methods and assess their outputs in terms of cluster structure and interpretation
• Evaluate the concept of panel data regression and its advantages in analysing longitudinal data
• Implement panel data methods to model relationships between variables over time and across different entities