Python Basic Concepts and Programming
Keywords, Statements and Expressions, Variables, Operators, Precedence and Associativity, Data Types, Indentation, Comments, Reading Input, Print Output, Type Conversions, The type( ) Function and Is Operator, Control Flow Statements, The if Decision Control Flow Statement, The if…else Decision Control Flow Statement, The if…elif…else Decision Control Statement, Nested if Statement, The while Loop, The for Loop, The continue and break Statements, Built-In Functions, Commonly Used Modules, Function Definition and Calling the Function, The return Statement and void Function, Scope and Lifetime of Variables, Default Parameters, Keyword Arguments, *args and **kwargs, Command Line Arguments.
Python Collection Objects, Classes
Strings- Creating and Storing Strings, Basic String Operations, Accessing Characters in String by Index Number, String Slicing and Joining, String Methods, Formatting Strings, Lists-Creating Lists, Basic List Operations, Indexing and Slicing in Lists, Built-In Functions Used on Lists, List Methods. Sets, Tuples and Dictionaries. Files: reading and writing files. Class Definition – Constructors – Inheritance – Overloading
Introduction to Numpy and Pandas
Numpy:-Understanding datatypes in python, basics of NumPy arrays, computation on NumPy arrays: universal functions. (refer chapter 2 from python datascience handbook) Pandas:-Introducing to pandas data structures, essential functionality, summarizing and computing descriptive statistics, handling missing data. (refer chapter 5 from python for data Analysis)
Data Loading and Data Wrangling
Reading and writing data in text format, interacting with databases, combining and merging data sets, reshaping and pivoting, data transformation, string manipulation (refer chapter 6 and 7 from python for data Analysis
Visualization with Matplotlib and Seaborn
General Matplotlib tips, simple line plots, simple scatter plots, visualizing errors, density and contour plots, histograms, binning, and density, customizing plot legends and colorbars, customizing matplotlib, visualization with seaborn. (refer chapter 4 from python datascience handbook)
Text Books:
1. Allen B. Downey, “Think Python: How to Think Like a Computer Scientist‘‘, 2nd edition,Updated for Python 3, Shroff/O‘Reilly Publishers, 2016 (http://greenteapress.com/wp/thinkpython/)
2. Mark Lutz, “Programming Python”, O'Reilly Media, 4th edition, 2010.
3. Jake Vander plas, “Python Data Science Handbook: Essential tools for working with data”, O‘Reilly Publishers, I Edition.
4. Wes Mc Kinney, “Python for Data Analysis”, O'Reilly Media, 2012Mark Lutz, “Programming Python”, O'Reilly Media, 4th edition, 2010.
Reference books:
1. Tim Hall and J-P Stacey, “Python 3 for Absolute Beginners”, Apress, 1st edition, 2009.
2. Magnus Lie Hetland, “Beginning Python: From Novice to Professional”, Apress, Second Edition, 2005.
3. Shai Vaingast, “Beginning Python Visualization Crafting Visual Transformation Scripts”, Apress, 2nd edition, 2014
Course Outcomes:
• CO1: Demonstrate basic data analytics principles and techniques
• CO2: Apply control structures the concepts of inheritance and overloading for a given problem.
• CO3: Perform essential operations using Numpy and Pandas
• CO4: Structuring the data in the dataset for a given problem.
• CO5: Demonstrate the concepts of data visualization.