## 3.2 Assess distribution using Box-Plot and Histogram.

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# Unit DS01: Exploratory Data Analysis

QUALIFI Level 7 Diploma in Data Science

Unit code: J/618/4970 RQF
level: 7

Aim
This unit provides learners with an in-depth understanding of R and Python programming and the fundamentals of statistics. This includes writing R and Python commands for data management and basic statistical analysis. The unit will help the learner to understand and perform descriptive statistics and present the data using appropriate graphs/diagrams and serves as a foundation for advanced analytics. Most industry analysis starts with Exploratory Data Analysis and a thorough study of this will help learners to perform data health checks and provide initial business insights.

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. Handle and manage multiple datasets within R and Python environments. 1.1 Work smoothly in R and Python development environments. 1.2 Import and export data sets and create data frames within R and Python in accordance with instructions. 1.3 Sort, merge, aggregate and append data sets in accordance with instructions. 2. Use measures of central tendency to summarize data and assess both the symmetry and variation in the data. 2.1 Differentiate between variable types and measurement scales. 2.2 Calculate the most appropriate (mean, median or mode etc.) measure of central tendency based on variable type. 2.3 Compare variation in two datasets using the coefficient of variation. 2.4 Assess symmetry of data using measures of skewness. 3. Present and summarise distributions of data and the relationships between variables graphically. 3.1 Select the most appropriate graph to present the data. 3.2 Assess distribution using Box-Plot and Histogram. 3.3 Visualize bivariate relationships using scatter-plots. 3.4 Present time-series data using motion charts.

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

Formative: Weekly assignments focusing 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

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