DS701 - Exploratory Data Analysis (8 credits)‍

This unit provides learners with an in-depth understanding of R and Python programming and the fundamentals of statistics. This includes writing R and Python commands for data management and basic statistical analysis. The unit will help the learner to understand and perform descriptive statistics and present the data using appropriate graphs/diagrams and serves as a foundation for advanced analytics. Most industry analysis starts with Exploratory Data Analysis and a thorough study of this will help learners to perform data health checks and provide initial business insights.

DS702 - Statistical Inference (12 credits)

This unit provides learners with an in-depth understanding of statistical distribution and hypothesis testing. Statistical distributions include Binomial, Poisson, Normal, Log Normal, Exponential, t, F and Chi Square. Parametric and non-parametric tests used in research problems are covered in this unit. The unit will help learners to formulate research hypotheses, select appropriate tests of hypothesis, write mainly R programs to perform hypothesis testing and to draw inferences using the output generated. Learners will also study planned experiments as part of the unit.

DS703 - Fundamentals of Predictive Modelling (15 credits)

This unit provides a strong foundation for predictive modelling. Its objective is to define the entire modelling process with the help of real life case studies. Many concepts in predictive modelling methods are common and therefore, these concepts will be discussed in detail in this unit. A good understanding of predictive modelling leads to a smart data scientist as many business problems are related to successfully predicting future outcomes.

DS704 - Advanced Predictive Modelling (15 credits)

In this unit, learners are introduced to model development for categorical dependent variables. Binary dependent variables are encountered in many domains such as risk management, marketing and clinical research and this unit covers detailed model building processes for binary dependent variables. In addition, multinomial models and ordinal scaled variables will also be discussed.

DS705 - Time Series Analysis (15 credits)‍

The objective of this unit is to discuss time series forecasting methods. Learners will analyse and forecast macroeconomic variables such as GDP and inflation. Panel data regression methods will also be discussed in this unit.

DS706 - Unsupervised Multivariate Methods (15 credits)

Data reduction is a key process in business analytics projects. In this unit, learners will learn data reduction methods such as PCA, factor analysis and MDS. They will also learn to form segments using cluster analysis methods. Forming segments and then analysing is a key technique for large groups of data and their intrinsic information comes out in detail once segmented thoughtfully.

DS707 - Machine Learning (15 credits)

Machine learning algorithms are new generation algorithms used in conjunction with classical predictive modelling methods. In this unit, learners will understand applications of various machine learning algorithms for classification problems.

DS708 - Further Topics in Data Science (15 credits)

In this module, learners will learn how to analyse unstructured data using text mining. The focus will be on sentiment analysis of text data, including data available on social media. For building interactive web apps straight from R, the concept of the “SHINY” package will be introduced. Big Data concepts and artificial Intelligence will be covered in the unit, as well as an introduction to SQL programming and how it is used to handle data.

DS709 - Contemporary Themes in Business Strategy (10 credits)

The convergence of Cloud computing, Big Data, Artificial Intelligence and The Internet of Things will see organisations of all shapes and sizes either survive and thrive or face extinction. New operational and strategic norms, types of organisations, the nature of work and employment are changing fundamentally across vast parts of the global economy. This unit introduces learners to the strategic and managerial challenges generated by the impact of digital technology on business and organisations.

• Gain the mathematical and statistical knowledge and understanding required to carry out basic and advanced data analysis.
• Develop sufficient skill in the R, Python and SQL programming languages to use them to successfully carry out data analysis to an advanced level.
• Develop a strong understanding of data management, including evaluation, structuring and cleaning of data for analysis.
• Become familiar with and use the tools and techniques used in data visualisation.
• Develop a comprehensive knowledge of classical data analytics, including statistical inference, predictive modelling, time series analysis and data reduction.
• Become familiar with and apply common machine learning techniques to business and other problems in order to uncover options and solutions for them.
• Develop an understanding of essential concepts from contemporary themes in business.
• Understand, evaluate and apply data science and analytics within business and organisational contexts.

Entry to Level 7 Diploma in Data Science

• A minimum of a Level 6 qualification in a related sector or Bachelor's degree.
• Should be at least 22 years of age.

Or

• A minimum of 3 years’ work experience which demonstrates current and relevant industry knowledge.

Standard duration of the Level 7 programme is 8 months. The student is required to submit 9 assignments to complete Level 7.

Recognition of Prior Learning is the recognition of non-certified learning towards a full unit or a qualification. You are able to gain credits using your previous regulated or unregulated qualifications or work experience. You can submit your CV and supporting documents for an evaluation.

The academic courses at the London Institute of Business & Technology are delivered using a "Blended Learning" format, which allows students to pursue their desired qualifications at their own pace. Our feature-rich Learning Management System provides easy access to Virtual Class recordings, textbooks, multimedia content, and other resources, as well as unlimited on-demand tutoring free of charge. Our tutors can be reached via email, phone, Google Meet, or any other preferred channel of your choice.

Credits and Total Qualification Time (TQT)
Level 7 is made up of 120 credits which equates to 1200 hours of TQT and includes 480 hours of GLH.

Total Qualification Time (TQT): is an estimate of the total amount of time that could reasonably be expected to be required for a learner to achieve and demonstrate the achievement of the level of attainment necessary for the award of a qualification. Examples of activities that can contribute to Total Qualification Time include: guided learning, independent and unsupervised research/learning, unsupervised compilation of a portfolio of work experience, unsupervisede-learning, unsupervised e-assessment, unsupervised coursework, watching a prerecorded podcast or webinar, unsupervised work based learning.

Guided Learning Hours (GLH): are defined as the time when a tutor is present to give specific guidance towards the learning aim being studied on a programme. This definition includes lectures, tutorials, and supervised study in, for example, open learning centres and learning workshops, live webinars, telephone tutorials or other forms of e-learning supervised by a tutor in real time. Guided learning includes any supervised assessment activity; this includes invigilated examination and observed assessment and observed work based practice.

The Chartered Management Institute (CMI) is a professional institution for management based in the United Kingdom. CMI supports managers and leaders worldwide with the tools, resources and community support to take on any professional challenge. All student at The London Institute of Business and Technology receives free CMI membership. CMI membership empowers you with tailored support, opportunities and resources to help you reach your management and leadership potential.

Upon completing this Level 7 diploma, learners can join a university to complete a project or dissertation to then receive a full master’s degree. Learners can also step directly into employment in the rapidly growing data science and analytics profession.