Python for Data Science (3150713)

Unit-1:  Overview of Python and Data Structures

Basics of Python including data types, variables, expressions, objects and functions. Python data structures including String, Array, List, Tuple, Set, Dictionary and operations them.

Unit-2:  Data Science and Python

Discovering the match between data science and python: Considering the emergence of data science, Outlining the core competencies of a data scientist, Linking data science, big data, and AI , Understanding the role of programming, Creating the Data Science Pipeline, Preparing the data, Performing exploratory data analysis, Learning from data, Visualizing, Obtaining insights and data products Understanding Python's Role in Data Science: Introducing Python's Capabilities and Wonders:Why Python?, Grasping Python's Core Philosophy, Contributing to data science, Discovering present and future development goals, Working with Python, Getting a taste of the language, Understanding the need for indentation, Working at the command line or in the IDE

Unit-3:  Getting Your Hands Dirty With Data

Using the Jupyter Console, Interacting with screen text, Changing the window appearance, Getting Python help, Getting IPython help, Using magic functions, Discovering objects, Using Jupyter Notebook, Working with styles, Restarting the kernel, Restoring a checkpoint, Performing Multimedia and Graphic Integration, Embedding plots and other images, Loading examples from online sites, Obtaining online graphics and multimedia.

Unit-4:  Data Visulization

Visualizing Information: Starting with a Graph, Defining the plot, Drawing multiple lines and plots, Saving your work to disk, Setting the Axis, Ticks, Grids, Getting the axes, Formatting the axes, Adding grids, Defining the Line Appearance, Working with line style, Using colors, Adding markers, Using Labels, Annotations, and Legends, Adding labels, Annotating the chart, Creating a legend. Visualizing the Data: Choosing the Right Graph, Showing parts of a whole with pie charts, Creating comparisons with bar charts, Showing distributions using histograms, Depicting groups using boxplots, Seeing data patterns using scatterplots, Creating Advanced Scatterplots, Depicting groups, Showing correlations, Plotting Time Series, Representing time on axes, Plotting trends over time, Plotting Geographical Data, Using an environment in Notebook, Getting the Basemap toolkit, Dealing with deprecated library issues, Using Basemap to plot geographic data, Visualizing Graphs, Developing undirected graphs, Developing directed graphs.

Unit-5:  Data Wrangling

Wrangling Data: Playing with Scikit-learn, Understanding classes in Scikit-learn, Defining applications for data science, Performing the Hashing Trick, Using hash functions, Demonstrating the hashing trick, Working with deterministic selection, Considering Timing and Performance, Benchmarkin, with,timeit, Working with the memory profiler, Running in Parallel on Multiple Cores, Performing multicore parallelism, Demonstrating multiprocessing. Exploring Data Analysis: The EDA Approach, Defining Descriptive Statistics for Numeric Data, Measuring central tendency,Measuring variance and range ,Working with percentiles, Defining measures of normality, Counting for Categorical Data, Understanding frequencies, Creating contingency tables, Creating Applied Visualization for EDA ,Inspecting boxplots

Sr.	Title	Author	Publication	Amazon Link
1	Python for data science for dummies	John Paul Mueller, Luca Massaron	Wiley
2	Programming through Python	M. T. Savaliya, R. K. Maurya, G. M. Magar	STAREDU Solutions
3	Pandas for everyone :Python Data Analysis	Daniel Y. Chen	Pearson
4	Introducing Data Science: Big Data, Machine Learning, and More, Using Python Tools	Davy Cielen, Arno D.B. Meysman, Mohamed Ali
5	Applied Data Science with Python and Jupyter	Alex Galea	Packt
6	Data Analytics Paperback	Anil Maheshwari	McGrawHill
7	Data Science From Scratch: First Principles with Python	Joel Grus	O'REILLY
8	Star Data Science Specialist		STAR CERTIFICATION

Sr.	CO Statement	Marks Weightage(%)
CO-1	Apply various Python data structures to effectively manage various types of data.	20
CO-2	Explore various steps of data science pipeline with role of Python.	15
CO-3	Design applications applying various operations for data cleansing and transformation.	30
CO-4	Use various data visualization tools for effective interpretations and insights of data.	15
CO-5	Perform data Wrangling with Scikit-learn applying exploratory data analysis.	20