21CV581 Data Analysis with Python syllabus for CV

Introduction to scikit-learn Python package, Iris data set. Getting and processing data: CSV files, Pandas package, Feature selection, Online data sources.

Data visualization using Matplotlib, Plotly. Supervised and Unsupervised learning

Regression:

Simple linear regression, Multiple linear regression, Decision tree, Random forests.

Classification:

Logistic regression, K-nearest neighbours, Decision tree classification, Random forests classification.

Clustering:

Goals and uses of clustering, K-means clustering, Anomaly detection, Association rule learning.

Artificial neural networks:

Definition, Example, Potential and constraints.

Course outcome (Course Skill Set)

At the end of the course the student will be able to:

1. Use online data sources for solving problems

2. Solve statistical problems and interpretation of results

3. Data visualization and graphical representation for decision making

4. Solve problems using artificial neural networks

Assessment Details (both CIE and SEE)

• The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is 50%.
• The minimum passing mark for the CIE is 40% of the maximum marks (20 marks out of 50).
• A student shall be deemed to have satisfied the academic requirements and earned the credits allotted to each subject/ course if the student secures not less than 35% ( 18 Marks out of 50)in the semester-end examination(SEE), and a minimum of 40% (40 marks out of 100) in the sum total of the CIE (Continuous Internal Evaluation) and SEE (Semester End Examination) taken together

Continuous internal Examination (CIE)

Three Tests (preferably in MCQ pattern with 20 questions) each of 20 Marks (duration 01 hour)

1. First test at the end of 5th week of the semester

2. Second test at the end of the 10th week of the semester

3. Third test at the end of the 15th week of the semester

Two assignments each of 10 Marks

1. First assignment at the end of 4th week of the semester

2. Second assignment at the end of 9th week of the semester

Quiz/Group discussion/Seminar, any two of three suitably planned to attain the COs and POs for 20 Marks (duration 01 hours)

The sum of total marks of three tests, two assignments, and quiz /seminar/ group discussion will be out of 100 marks and shall be scaled down to 50 marks

Semester End Examinations (SEE)

• SEE paper shall be set for 50 questions, each of 01 mark.
• The pattern of the question paper is MCQ (multiple choice questions).
• The time allotted for SEE is 01 hour.
• The student has to secure minimum of 35% of the maximum marks meant for SEE.

Suggested Learning Resources:

Books

1. Peters Morgan, Data Analysis with Python, AI Sciences, 2016.

2. Wes McKinney, Python for Data Analysis, O’Reilly Media,