INTRODUCTION TO CODING AND DATA MANAGEMENT-2

The goal of this course is to teach students how to approach problems with an algorithmic approach.
Students will learn some basic techniques for problem solving and how to use a programming language to provide a sound and formal description of a designed problem solution.

The course provides an introduction to the basics of computer science and to programming.
Programming is intended as a way to model real-world problems and to design algorithmic solutions to solve them.
This course teaches students problem solving techniques and algorithmic thinking.
Technical topics cover algorithms, data structures, and Python programming.

The students will achieve the following objectives:

Knowledge: i) learn how to use common library (from NumPy and Pandas) algorithms and complex data structures to address algorithmic problems; ii) understand common data visualizations techniques and how to use common library (Seaborn) objects to create data visualizations; iii) understand how to organize code into modules and classes.

Application of knowledge: i) use complex library structures to organize, cleanse and analyze data to solve formal algorithmic problems; ii) organize solution code into modules and classes.

Communication: i) generate various data visualizations for preliminary analysis.

Understanding of content in Introduction to Coding and Data Management - I

Data import, export and data cleansing. Basics of data processing, analysis and visualization with Panda and NumPy. Basics of data visualization with Seaborn. Topics include:

• Structuring code – modules, classes
• Working with Files – navigate, read, write
• Working with Files Format – txt, csv, tsv
• NumPy Basics – matrices, operations, statistical functions
• Pandas: series, dataframes, operation, mapping, join
• Data Preparation: file i/o, pivot tables, missing data, slicing
• Data Visualization: data dimensionality, graphs, charts, maps, pots, Seaborn
• Case Studies – Problem 1, Problem 2

Python for Data Analysis. O'Reilly. Wes McKinney.

Instructor notes.

The student will be evaluated based on their capability to analyze a problem, model a solution, and translate it into a computer program.

Students are evaluated based on an oral discussion of their team project design, project code and knowledge of course content.

Lectures and hands-on sessions.

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