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London School of Business and Finance Singapore

Diploma in Data Analytics - Part Time

London School of Business and Finance Singapore
In Singapore ()

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Important information

Typology Bachelor's degree
Duration 12 Months
  • Bachelor's degree
  • Duration:
    12 Months

Emagister adds to its catalogue a Diploma in Data Analytics, which is designed for those pursuing a successful career!

The main focus of the Diploma in Data Analytics is to give students the knowledge and practical insight into the workings of the business analytics industry. Students will get an insight into how big data is managed and useful information extracted to better make informed decisions.

If you want to enjoy all the benefits of the course, contact us through our website so we can provide you with the most relevant information.

To take into account

· Requirements

"Minimum Age: 18 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 Minimum English Language Entry Requirement Achieved a grade C6 or better in English language O level;Or 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;"

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What you'll learn on the course

Business Intelligence
Data Mining
Data analysis
Machine Learning
Social Media Analytics
IOT devices
Fuzzy System
Data Visualization

Course programme

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 perform 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 handle different data types from 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."

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