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Diploma in Data Analytics

Diploma in Data Analytics

The 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.  

Throughout the course, students are fully supported and their development is checked frequently by progress assessments. Student performance and satisfaction is 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. With the Diploma in Data Analytics, graduates can pursue entry-level occupations across many industries, such as business analysts, web and social media analysts, and research analysts. 

Programme Aims

The main aim of the Diploma in Data Analytics is to give students the knowledge and practical insight into the workings of the business analytics industry. In particular, students will get an insight into how big data is managed and useful information extracted to better make informed decisions. Holders of the Diploma will be able to demonstrate detailed knowledge and critical understanding of the major concepts in big data management. 

The Diploma in Data Analytics aims to:

  • Develop students’ competence and practical skills in big data management.
  • 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 

Graduates of the Diploma in Data Analytics should be able to provide pivotal support to various departments within an organisation across all industries. In particular, their practical knowledge of the big data management and its application in the business environment will make Graduates of the Diploma in Data Analytics valuable members of any organisation.

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

  • demonstrate relevant business 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 the their knowledge effectively across a range of problems
  • interpret situations which 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 competences that will enable them to assume significant responsibility within organisations

 

Course Duration

Full-time: 8 Months, Students attend 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 

Course Intakes

Part-time: January, April, July, October

Mode of Delivery

Lectures, tutorials, seminars, workshops

Mode Lectures, tutorials, seminars, workshops Student Independent Learning 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: 18 years

Local students shall possess one of the following:

  • At least two passes in GCE ‘A’ Level
  • 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

 

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;

Students with non-standard entry requirements (e.g. other PEI qualification, lack of equivalent Year 12 in home country, etc.) will be assessed on a case by case basis subject to approval of the Academic Board.

 

Module Synopsis

Rapidminer Data Science Essentials
This module will get the student familiar with Rapidminer studio and the basic functions in Rapidminer process and modules. Students will use Rapidminer studio to import, handle and manipulate data and perform visual and statistical analysis on data.

Rapidminer Advanced Data Analysis
This module is to prepare students for advanced data preparation and ETL data from different sources and combine them into a single dataset. Students also preform necessary aggregations on data and do engineering on the data to create new columns using existing data. Student learn the basics of macros to program their own procedures and steps. In practical situations, students must handling different data types form different sources and merge them into a meaningful dataset that can be explored and utilized with machine learning and data mining.

Machine Learning
This module will get the student familiar with concepts of machine learning and data 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 application and other fields by studying patterns in the data and using them to make predictions.

Text Mining & Social Media Analytics
This module will get the student familiar with concepts of text mining and how to use the data to extract meaning insights. Students learn how to extract and pre-process text and used machine learning techniques to classify and cluster data. Documents with similar content and context are classified together. Students perform sentiment analysis on text data and social media data from twitter to get insights into political views and social media trends.

Internet of Things Data Analysis
This module will get the student familiar with concepts of IOT data for analysis. Students learn how to extract IOT data and store in a cloud server. Students perform data extraction from IOT devices and display the data for monitor in the web.The monitored data will be store in the cloud server. Student will learn to download data for data analysis.

Introduction to Fuzzy Logic

This module will get the student familiar with concepts of Fuzzy logic for analysis. Student will go through the complete design process of a Fuzzy Controller or Inference system - From fuzzification to inference methods up to and including defuzzification. 

This course includes implementing your Fuzzy System to solve your real world problem.

Deep Learning Analysis
Deep learning is highly effective for learning patterns form huge amounts of data, deep learning techniques are becoming mode popular and in-demand. This module teaches the essentials of deep learning for classification, regression, image recognition and text analysis. Students will be able to use pre-built models and improve and tune it to create their own models for specific image recognition. Students will be able to deploy their models for practical usage.

Data Visualization in Tableau
This module will get the student familiar with a popular business intelligence software Tableau with state-of-the-art visualization capabilities. Students will use the software to visualize data for insights and analysis. Create own calculated fields and parameters, as well as join and blend different data sources. Data science analytics is also covered as well as basic statistics and forecasting.

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
    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%)

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.

  • 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.
Teacher Student Ratio
1 : 40
Course Fee

Local Student: SGD$4980

International student: SGD$7500

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