3.3 Evaluate the use of the HADOOP platform for performing Big Data Analytics.

Unit DS08: Further Topics in Data Science

QUALIFI Level 7 Diploma in Data Science

Unit code: M/618/4977 RQF

level: 7


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.

Learning Outcomes and Assessment Criteria

Learning Outcomes. When awarded credit for this unit, a learner will be able to:

Assessment Criteria. Assessment of this learning outcome will require a learner to demonstrate that they can:

1. Perform text mining on social media data.

1.1 Appraise the concepts and techniques used in text mining.

1.2 Analyse unstructured data and perform sentiment analysis of Twitter data to identify the positive, negative or neutral tone of the text.

2. Develop web pages using the SHINY package.

2.1 Build interpretable dashboards using the SHINY package.

2.2 Host standalone applications on a web page to present the results of data analysis.

3. Apply the Hadoop framework in Big Data Analytics.




3.1 Evaluate core concepts of Hadoop.

3.2 Appraise applications of Big Data Analytics in various industries.

3.3 Evaluate the use of the HADOOP platform for performing Big Data Analytics.

4. Evaluate the fundamental concepts of artificial intelligence.

4.1 Build a simple AI model using common machine learning algorithms that support business analysis and decision- making. In comparison with traditional assumptions from business theory.

5. Use SQL programming for data analysis.

5.1 Evaluate core SQL for data analytics.

5.2 Carry out data wrangling and analysis in SQL to uncover insights in underutilized data.

Assessment Guidance

To demonstrate all learning outcomes and assessment criteria, each unit should follow the same assessment methodology:

  • Formative: Weekly assignments focussing on knowledge and understanding of technical skills using sample data sets over a period of 3 weeks and participation in weekly live classrooms and discussion groups;
  • Summative: 1. Formal timed exam testing technical knowledge 2. Component of two individual course projects based on real word data analytics

The standard price quoted for this assignment is for 2000 - 2500 words (Theory Only). Technical evaluation would be discussed upon assessment criteria of the assignment. For custom word count and written work, contact via Click here → Whatsapp UK, OR Whatsapp Middle East ← Click here OR Live Chat.


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