• Demonstrate understanding of imperative, declarative and object-oriented language features of Python language and know when it is appropriate to use each.
• Demonstrate basic Python coding skills for data manipulation, data analysis, and data visualization on various data formats.
• Characterize the linkages between different data analytics operations such as business understanding, data understanding, data preparation data transformations, data modelling, data visualization and evaluation.
• Demonstrate an understanding of the underlying data mining and machine learning algorithms and differences between supervised and unsupervised approaches in data analytics
• Write programs in Python programming language that make use of external libraries, APIs, etc.
• Identify the key challenges and design goals for performing data analysis using Python programming
• Critically assess the advantages of using of Python programming for data analytics
• Design and implement interactive programs using Python programming language using its appropriate linguistic features.

The primary goal of this course is to introduce Python programming skills to the

students with a purpose to collect, transform, model, analyze and visualizes

broad range of datasets. The course uses the Python programming language

to learn how to work with numerical, string, and more complex data formats,

and to perform data analysis with basic data mining and machine algorithms

using both supervised and unsupervised approaches.

With a keep focus on open source technologies, the course will focus providing

hands-on experience with open source libraries in Python for data mining,

machine learning and data visualizations. Finally, students will develop

practical programming skills in problem solving by working on real-world

datasets as part of their final project.

Course content:

• Introduction to Python programming language constructs such as

programming basics, control flow, Operators, expressions, choice,

repetition

• Functions, data structures and collections in Python language

• Object oriented programming features of Python language such as

classes, methods.

• Exception handling, standard libraries, consuming external APIs and open

source libraries to develop programs

• Data transformations and text processing including reading and writing

files

• Data analysis with basic data mining and machine learning algorithms for

clustering, classification using unsupervised and supervised approaches

• Data visualizations using open source libraries in Python such as

matplotlib, ggplot, pygal etc.