Data Analysis with Python
The goal of this course is to give knowledge of how to use Python to extract knowledge and information from data. At the end of this course students will be competent in using Python libraries to work with and analyze offline as well as online data. Course will start from general python programming basics, data structures, and algorithm design with a heavy emphasis on applying data analysis and visualization techniques to solve real-world problems in different domains.
Data Analysis with Python - SENG 352
Course Objectives
Data analysis with Python course is designed as a holistic training to understand, study, extract, analyze, manipulate, and comprehend data to make conclusions and achieve specified data goals with the help of python programming language.
Learning Outcomes
The students will learn;
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Uncover hidden or unexpected connections, correlations, patterns, and trends to drive better decisions.
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Self-configure Python programming environment
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Code, compile, debug, and run Python programs
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Learn language syntax and fundamental programming concepts including variables, control statements, loops, functions, lists, and classes
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Use modules and tools to collect, reshape, analysis, and visualize data
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get data from files (csv, html, json, xml) and relational databases,
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rudiments of data cleaning,
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Develop programs for various real-world problems by applying machine learning (regression, classification, clustering), and data visualization packages (numpy, Pandas, Scikit-learn) available in Python.
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Evaluate data results and make optimal decisions
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Textbook(s)
Wes McKinney , Python for Data Analysis, 2nd Edition, O’reilly, 2017
ISBN: 9781491957660
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Course Outline
1 Introduction
2 Code with Python
3 Summarizing the Data Frame – Descriptive Statistics
4 Data Access, SQL
5 Data Preprocessing
6 Data visualization
7 Review
8 Model and Feature Selection
9 Regression: predict continuous labels
10 Classification: predict labels as two or more discrete categories
11 Clustering: detect and identify distinct groups in the data
12 Model Evaluation & Refinement
13 Web Analytics
14 Project Presentations
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