This course introduces fundamental data science process and attendant concepts. Students will be provided with hands-on experience applying several techniques to extract, transform and analyze data and elicit meaning. The course will also introduce ethical considerations when implementing data science solutions.
On successful completion of this course, a student should be able to:
Apply techniques to acquire and organize data from different sources
Implement algorithms for techniques to clean and prepare data for analysis
Apply exploratory data analysis methods on small data sets
Apply the data science process to simple data analysis problems
Describe ethical issues that may arise in data science applications
Applying the Data Science Process
Data Wrangling: extractions, parsing, joining, standardizing, augmenting,
cleansing, consolidating and filtering
Data Cleaning (ETL): Data Auditing: Analysis (mean, standard deviation, range),
Eliminating Duplicates, Translation and Normalization – Data Smoothing
Techniques
Describing data: Exploratory Data Analysis (EDA) + Data Visualization:
Summaries, aggregation, smoothing, distributions
Building structure from a variety of data forms to enable an analysis
Data Modeling (Linear and Stochastic)
Ethics in Data Science
The course will be assessed by coursework only which will consist of both individual and group assignments. The assignments should allow students to demonstrate that the specific learning outcomes have been achieved.
Coursework. 60%
Final Examination. 40%
Students will be required to pass both the coursework and the final examination to pass the course.
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