ADVANCED PROGRAMMING FOR ANALYTICS

Paper Code:

MBB421

Credits:

Contact Hours:

60.00

Max. Marks:

100.00

Objective:

The course will enable students to proficiently utilize Python libraries for data manipulation, visualization, database management, and machine learning, fostering skills in numpy, pandas, matplotlib, seaborn, MySQL, SQLite, Tkinter, scikit-learn, TensorFlow, and Keras.

Course Outcomes:

Course

Learning outcome

(at course level)

Learning and teaching

strategies

Assessment Strategies

Course Code

Course Title

24MBB421

Advanced Programming For Analytics

(Practical)

CO725: Categorize basic libraries of python

with their utility in different business problems.

CO726: Build data frame, import data set and perform pre-processing, descriptive and predictive analysis on business datasets.

CO727: Communicate results by designing charts and plots like bar chart, line charts and ROC curve using python libraries.

CO728: Create MySQL database and access data through MySQL queries for business domain.

CO729: Design model based on advance machine learning algorithms using python libraries for business problems like retail management, Customer relationship management etc.

CO730: Contribute effectively in course-specific interaction

Approach in teaching:

Interactive Lectures, Group Discussion, Tutorials, Case Study

Learning activities for the students: Self- learning assignments, presentations

Class test, Semester end examinations, Quiz, Assignments, Presentation

12.00

Importing numpy library, creating numpy arrays, indexing and slicing arrays,performing numerical operations on arrays, converting data frames to numpy arrays, creating multidimensional arrays, numpy data type object (dtype), concatenating, reshaping and flattening multidimesional arrays, repeating patterns uisng “tile” method. Reading and writing data files using functions: savetxt, loadtxt, tofile ,fromfile,save ,load, genfromtxt

12.00

Importing pandas library, Usingseries and Data Frames, indexing, Grouping, aggregating, MergingdataFrames, dealing with missing values using dropna method ,filtering or filling in missing data, creating dataframes from dictionaries or nested dictionaries, accessing and changing values of data frame using loc,at,replace methods,reading and writing csv,excel files

12.00

Importing Visualisation libraries: Matplotlib: format parameter of pylpot.plot ,subplots method, checking and defining ranges of axes, using linspace and linstyle, specifying legend, title Style, creating Scatter plots ,Bar charts, histogram, Stack charts, Saving plots. Importing seaborn library:Style functions, color palettes, Distribution plots ,categorical plots

12.00

Creating databases using MYSQL and SQLite: Importing the modules, creating connection object, creating tables, performing database operations (insert, update, delete), closing
connection. GUI programming with Tkinter: widgets (label, text, radio button, check boxes, entry, canvas, dialogs, menus)

12.00

Implementing Machine Learning with scikit-learn: loading and Visualizing datasets (sample sklearn datasets), splitting train and test data. Implementing deep learning with tensorflow and keras

*Case studies related to entire topics are to be taught.

Essential Readings:

Paul Gries, Jennifer Campbell and Jason Montojo, “Practical Programming: An Introduction to Computer Science using Python 3”, Second edition, Pragmatic Programmers, LLC,2013.
Robert Sedgewick, Kevin Wayne, Robert Dondero, “Introduction to Programming in
Python: An Inter-disciplinary Approach, Pearson India Education Services Pvt. Ltd., 2016.
Timothy A. Budd, “Exploring Python”, Mc-Graw Hill Education (India) Private Ltd.,2015.

References: