Higher Diploma in Data Science and Analytics
The Higher Diploma in Data Science and Analytics has been developed by the London School of Business and Finance, School of Technology to provide a qualification for students who are seeking to work in the data science, business analytics industry, or in occupations where big data management will be of utility.
Throughout the course, students are fully supported, and their development is checked frequently by progress assessments. Student performance and satisfaction are monitored to ensure that the course meets students’ academic and personal development needs, and industry contacts ensure that the programme is relevant and suitable for the demands of a career in the industry.
The main aim of the Higher Diploma in Data Science and Analytics is to give students the knowledge and practical insight into the workings of the data science and business analytics industry. Students will get an insight into how big data is managed and useful information extracted to better make informed decisions. Holders of the Higher Diploma will be able to demonstrate detailed knowledge, practical applied experience, and critical understanding of the major concepts in big data management.
The aim of the Higher Diploma in Data Science and Analytics is to:
- Develop students’ competence and practical skills in big data management.
- Provide the foundation for future pathways and continuing professional development.
- Provide the relevant knowledge and understanding of big data management as it relates to the wider business context.
- Provide the technical, communication, and practical work skills that will enable graduates to follow a career in all areas of data science and analytics and a wide range of careers in IT, Retail, business, finance, marketing, logistics, and administration.
Graduates of the Higher Diploma in Data Science and Analytics should be able to provide pivotal support to various departments within an organisation across all industries. Their practical knowledge of big data management and its application in the business environment will make Graduates of the Higher Diploma in Data Science and Analytics valuable members of any organisation.
On successful completion of the Higher Diploma in Data Science and Analytics course, students should be able to:
- Demonstrate relevant data science and analytics knowledge and understanding of the basic principles of big data management which will equip them with an awareness of the operation of analytics in all aspects of their careers.
- Demonstrate proficiency in identifying analytical issues and know how to use their knowledge effectively across a range of problems.
- Interpret situations that require analytical input and contribute meaningful and calculated solutions in a timely manner.
- Effectively communicate information, arguments, and analysis in a variety of forms to specialist and non-specialist audience
- Demonstrate cognitive skills of critical thinking, analysis, and synthesis.
- Demonstrate effective problem solving and decision making using appropriate quantitative and qualitative skills including identifying, formulating, and solving problems.
- Take and demonstrate responsibility for their own learning and continuing personal and professional development.
- Undertake further training, develop existing skills, and acquire new competencies that will enable them to assume significant responsibility within organisations.
- Recognise the management, economic, legal, social, professional, and ethical issues relating to information technology.
- Learn and work both independently and within groups.
- Develop the necessary study skills and knowledge to pursue further study.
- Develop the professional skills necessary for a career in the IT industry.
- Possess effective communication, teamwork, and leadership skills.
- Present proposals and recommendations in a professional and efficient manner.
International Students: 90%
Local, PR, non-student’s pass: 75%
The course is successfully completed upon fulfilling the course and module duration. Students will attend the scheduled number of study terms for the timetable provided and successfully completing all the modules in the course.
40:1 for lecture
24:1 for Computer Lab
a. Minimum Academic Entry Requirement
Local students shall possess one of the following:
- Two passes in GCE ‘A’ Level Examinations
- International Baccalaureate (24 points)
- Local Polytechnic Diploma in any field
International Students shall possess one of the following:
- Completion of Year 12 High School Qualification or equivalent qualification from respective home countries
- Completed International Baccalaureate (24 points)
- Equivalent Local Polytechnic Diploma in any field in respective home countries
b. Minimum English Language Entry Requirement
Both local and international students MUST fulfill the minimum English language entry requirement of one of the following (except Mandarin programmes):
- Achieved grade C6 or better in the English language in GCE O level.
- Passed in the English Language in Year 10 High School qualification or equivalent.
- IELTS 5.5/TOEFL 550.
- Completed LSBF Preparatory Course in English Upper Intermediate Level.
Students with non-standard entry requirements will be assessed on a case-by-case basis subject to the approval of the Academic Board.
c. Minimum Age
18 years or above
BYOD- Bring Your Own Device
Students are expected to own software for MS Office Suite (Word, Excel, PowerPoint, and Outlook) and MS WIN 10 operating system.
Students are expected to provide their own computer having the following configuration.
|Processor||1.6 GHz or faster, 2-core Intel Core i5 or equivalent||1.8 GHz, 2-core Intel Core i5 or equivalent|
|Memory||6 GB RAM||≥8 GB RAM|
|Hard Disk||256 GB disk size||Sufficient free space to install trial software to be downloaded|
|Display||280 x 768 screen resolution (32-bit requires hardware acceleration for 4K and higher)||N/A|
This module will get students familiar with basic statistical concepts andExpand
This module is to prepare students for data preparation and ETL data from differentExpand
This module will get the student familiar with concepts of machine learning and dataExpand
This module is to prepare students for performing tasks in the cloud. Students areExpand
applications for collection, analysis, and interpretation of data for decision making using data.
sources and combine them into a single dataset. Students perform more machine learning concepts and algorithms and apply them for various types of analytics
mining for predictive analytics. Students learn what are the algorithms available, what they do, and how to choose the best one and apply it to their data. Students learn to make predictions with the output of the model and compare them across different models. Students will be able to apply these concepts in business applications and other fields by studying patterns in the data and using them to make predictions.
introduced to the concepts of cloud computing such as data bases & storage, creating virtual servers, and security
This module is to prepare students for creating simple apps to be deployed.Expand
Internet of Things
This module will get the student familiar with concepts of IoT data for analysis.Expand
This module will get the student familiar with concepts of project management followingExpand
This module will get the student familiar with state-of-the-art visualizationExpand
Students will learn how to design the interface for their apps and store and manipulate data, have logic sequences, and making decisions (logic) as well as mobile features. Students will also learn how to automate processes in-app logic.
Students learn how to extract IOT data and store it in a cloud server. Students perform data extraction from IoT devices and display the data for the monitor on the web. The monitored data will be store in the cloud server. The student will learn to download data for data analysis
closely the PMBOK syllabus. Students will grasp the concepts of project management and will benefit greatly in their business & IT undertaking. Students will know the stages of each project from start to finish and all the various disciplines of the project management lifecycle.
capabilities. Students will use BI software to visualize data for insights and analysis. Create own calculated fields and parameters, as well as join and blend different data sources.
Database Management Systems
This module introduces students to the database which is fundamentalExpand
Python Data Science Essentials
This module introduces students to and gives a clear understanding of how networksExpand
Cloud Artificial Intelligence (AI)
This module introduces students to the big data capabilities of a choice of Cloud.Expand
Deep Learning with R Programming
This module introduces students to the main aspects of deep learning and usingExpand
to current business information system activities. A database typically provides features to enhance compactness, reliability, flexibility, and Internet applicability. Students will be introduced to the concepts and fundamentals of database systems. Students will be able to specify and create a database model, including the setting of various parameters that can be modified to suit different structured or unstructured data requirements, the design of data, as well as the development of mechanisms for maintenance, storage, and retrieval of data based on the business requirements. An open-source database tool is considered for verifying the features.
from in-home local area networks (LANs), to the massive and global Internet, are built and how they allow us to use computers to share information and communicate with one another.
Through a blend of lectures, presentations, demonstrations, and hands-on lab exercises, students get an overview of a choice of Cloud and a detailed view of the data processing and machine learning capabilities. This module allows the ease, flexibility, and power of big data solutions on a choice of Cloud.
R-based data science basics and techniques. Students will learn to apply R frameworks to real-life data, images, etc for classification and regression applications. Students will gain concepts to understand what algorithms and methods are best suited for the data. Students will be able to use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.
Hadoop Big Data Analytics
This module introduces students to learn, understand, and practice bigExpand
This module introduces students with practical knowledge and understanding ofExpand
Data Science Visualization
This module helps students to join the dots between mathematics, programming, andExpand
This module provides an opportunity for students to undertake a task of a suitable sizeExpand
data analytics approaches, which include the study of contemporary computing big data technologies and techniques focusing on industry applications. The module emphasizes efficient handling of data of any size, develops the pragmatics of managing data, alongside retrieval and analysis of information. Mainly the module focuses on conceptualization and summarization of big data trivial data versus big data, big data computing technologies, techniques, and approaches. Students will be exploring different publicly available datasets and select suitable applications.
web analytics and SEO fundamentals, web reporting visualization tools, web monitoring tools, and application of information to meet business requirements. Case scenarios, examples, exercises, and hands-on practical lab tasks will be used.
business analysis. This module provides an introduction as well as hands-on experience in data visualization, visual analytics, and visual data storytelling. If learners have no prior knowledge or learners with basic math skills but want to apply them in data science or learners have good programming skills but lack math, this module bridges the gap between mathematics and programming. It introduces design principles for creating meaningful displays of quantitative and qualitative data to facilitate managerial decision-making in the field of business analytics. Students will know how to structure and streamline data analysis assignments; collect, prepare, and “wrangle” data, and highlight implications using visualizations and storytelling.
that allows them to apply the appropriate skills and knowledge learned in the Advanced Diploma. It is normally expected that the project will involve, in addition to evidence of meeting targets in the area of self and time management, relevant background reading and research, and use of appropriate tools and techniques for design and development. Work produced here must be of professional competence suitable for the level of the Higher Diploma course. Students will undertake a significant piece of individual applied academic work in an area of their own interest relevant to, and demonstrating technical skills acquired in, their programme of study. Students will normally need to research one or more academic topic areas and then apply their findings to the construction and implementation of a computer-based system.
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