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Diploma in ADDA

Advanced Diploma in Data Analytics

The Advanced Diploma in Data Analytics has been developed by London School of Business and Finance to provide a qualification for students who are seeking to work in the business analytics industry or in occupations where big data management will be of utility.

Programme Aims

This course is designed to guide participants to use data science tools to perform data analytics and data science techniques on a variety of data types and cyber-based fields.

This course uses Rapidminer primarily for its data flow needs and also uses GUI tools and cloud studios which is point-and-click interface, easy to use and understand (user-friendly).

The Advanced Diploma in Data Analytics aims to:

  • Develop students’ competence and practical skills in big data management e.g. quering data from Hadoop and perform machine learning.
  • To lay the foundation for future pathway and continuing professional development.
  • To provide students with the relevant knowledge and understanding of big data management as it relates to the wider business context
  • The knowledge and skills that will enable them to follow a career in all areas of business analytics and a wide range of careers in business, finance, marketing, logistics and administration. 
Learning Outcomes 

On successful completion of this programme, students should be able to:

  • Demonstrate relevant business analytics knowledge and understanding of principles of big data management using various platforms such as Hadoop and Rapidminer which will equip them with an awareness of the operation of analytics in all aspects of their careers
  • Engage in analytical issues and know how to use the their knowledge effectively across a range of problems
  • Interpret scenarios which require analytical input and contribute meaningful and calculated solutions in a timely manner
  • 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
  • Undertake further training, develop existing skills and acquire new competences that will enable them to assume significant responsibility within organisations
Course Duration

Full-time: 8 Months, Students attend lessons 3 hours per day plus directed/independent study for a period of 8 months from Monday to Friday.

Part-time : 12 Months, Students attend 3-hr lessons 2 or 3 nights a week for a period of 12 months.

Mode of Delivery

Lectures, tutorials, seminars, workshops

Mode Lectures / Tutorials / Seminars / Workshops Student Independent Total
Hours
FT 42 hrs. (14 Lessons of 3 hrs. each) 108 hrs. 150 hrs.
PT 36 hrs. (12 Lessons of 3 hrs. each) 114 hrs. 150 hrs.
Entry Requirements

Minimum Age: 19 years

Local students shall possess one of the following:

  • LSBF Diploma in Data Analytics
  • Local Polytechnic Diploma in a relevant field
  • Local Polytechnic Diploma in any field

International students shall possess one of the following:

  • LSBF Diploma in Data Analytics
  • Equivalent Local Polytechnic Diploma in a relevant field in respective home countries in English medium

AND

Both local and international students MUST fulfil the minimum English language entry requirement of one of the following (except Mandarin programmes):

  • Achieved grade C6 or better in English language in GCE O level;
  • Pass in English Language in Year 10 High School qualification or equivalent;
  • IELTS 5.5/TOEFL 550;
  • Completed LSBF Preparatory Course in English Upper Intermediate Level
Module Synopsis

Essential python with RapidMiner
This module will get the student familiar with Python which is the world’s most popular programming language, and use it in processing data and create modular functions which then can be put in regular RapidMiner workflow.

Querying databases with SQL
This module will get the student familiar with databases and how to query data using SQL and extract the desired dataset with conditions and filtering. Students will be familiar with SQL syntax and terms which can be applied across various databases (Postgres, MySQL, MS-SQL and Oracle) opensource and commercial.

Deep Learning with RapidMiner
This module is to prepare students for advanced deep learning with neural networks in RapidMiner for processing and predicting images and text. Keras framework is introduced and student learn the Keras module to perform deep learning tasks.

Radoop for RapidMiner
This module will get the student familiar with concepts of Hadoop and other technologies like Hive, Mahout and Spark. Students will learn the essentials of these technology platforms without the need to program. Using Radoop module extensions, students query data and perform analysis & visualizations on the data. Using machine learning modules, students create models and use to predict unlabelled data.

Networking basics
Students will be able to create a complete and secure network to be deployed at enterprise level using network tools. Students will learn basic and intermediate networking skills including VPN and LAN design and deployment.

Robot Process Automation
Students will be equipped to use popular RPA software UIpath and TagUI to create software robots to automate tasks with Excel and their browser. Students learn how to implement these robots to greatly automate daily tasks and thus reduce time and manpower in organizations.

Cybersecurity
This module will get the student familiar with concepts of cybersecurity, various concepts and tools are introduced for students to gather network data and cyber security data. Students learn various types and entry points of cyber attack. Students collect data and perform machine learning to predict cyber attacks.

Google Analytics
Students will be introduced to web analytics and data analysis using google analytics. Students generate and download timely reports which can be analysed in RapidMiner or Excel. Dashboard for quick analysis and marketing campaign and eCommerce tracking is taught as well.

Attendance Requirements

STP Holders: 90%
Non-STP Holders: 75%

Assessment, Graduation and Awards
  1. Assessment Outline
    Mid-Term Quiz 1                       20%
    Mid-Term Quiz 2 20%
    Final Project 60%
    Total: 100%
  2. Assessments Profile

    Student must achieve an overall passing grade of 40%. If students fail to achieve an overall passing grade of 40%, students will be permitted one retake attempt in each failed assessment and failure of this retake will require students to re-module the failed module(s) again in full prior to additional retake attempt.

    1. Quiz 1 & 2 (40%)
      • This will be in the form of an in-class quiz, individual basis.
      • Students to be communicated on quiz and assessment outlines/ parameters before course and unit commencement.
      • Students are tasked to submit the Quiz.
    2. Final Project (60%)
      • Students to be communicated on the final project and assessment outlines/ parameters before course and unit commencement.
      • Lecturer will go through with the students and highlight the importance of relevant topics during the final revision class.
      • Students are required to submit the final project.

Students must complete and pass all required modules in this course.

Students who for any reason are unable to complete the Advanced Diploma and who have successfully passed a minimum of 4 modules are eligible for an Exit Award of a Diploma in Data Analytics. 

Lecturer Student Ratio
1 Lecturer: 40 Students
Course Fee

Local Student: SGD$5750

International student: SGD$8500

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