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Data Science Using Python

By Prof. Faisal Anwer, Prof. Mohammad Nadeem   |   Aligarh Muslim University
Learners enrolled: 17796   |  Exam registration: 2060
ABOUT THE COURSE:
This course is designed to provide participants with a strong foundation in both the theoretical concepts and practical applications of data science, using Python as the programming language. The course will be helpful for a wide audience, such as a beginner eager to explore the data science landscape or a professional aiming to enhance their skills. The course offers a structured and hands-on approach to equip the audience with the skills needed to navigate the data-driven world. Throughout the modules, participants will learn key data science concepts, including data collection and storage, data preprocessing, data analysis, modeling, machine learning, data visualization, and many more.

The hands-on exercises and projects will enable participants to apply their understanding of Python to solve real-world data extensive problems effectively.

INTENDED AUDIENCE: UG, PG and Industry professionals

PREREQUISITES: No pre-requisites required but prior knowledge of programming language would be helpful

INDUSTRY SUPPORT: IT (Information Technology) Industry
Summary
Course Status : Completed
Course Type : Elective
Language for course content : English
Duration : 12 weeks
Category :
  • Computer Science and Engineering
Credit Points : 4
Level : Undergraduate/Postgraduate
Start Date : 22 Jul 2024
End Date : 11 Oct 2024
Enrollment Ends : 05 Aug 2024
Exam Registration Ends : 16 Aug 2024
Exam Date : 25 Oct 2024 IST
ALERT ON EXAM DATE :

Exam date is subject to change.

Note: This exam date is subject to change based on seat availability. You can check final exam date on your hall ticket.


Page Visits



Course layout

Week 1: Introduction to Data Science
1. Motivation, popularity, objectives and outcomes of the course.
2. Difference between AI, Machine Learning and Data Science.
3. Basic introduction of python, Google Colab and their features
4. Popular Dataset Repositories along with discussion on some datasets

Week 2: Statistical concepts
1. Introduction to statistics, types of statistics, types of data and describing data.
2. Measures of centrality and variance.
3. Sampling and hypothesis testing.
4. Introduction to probability theory.

Week 3: Basic Python: Part-I
1. Data Types, Input/Output, Operators: Precedence and Associativity. 
2. Decision Making and Looping
3. Function and its syntax, Positional arguments, Keyword arguments etc.
4. Exercises on Loops, decision making and functions

Week 4: Basic Python: Part-II
1. String: Operations & Functions, List: Accessing List, Slicing, Cloning/Copy etc.
2. Set and Tuple: Accessing, Slicing, Operations & Functions.
3. Dictionary: Properties, Accessing Dictionary, Problems on Dictionary.
4. Exercises on String, Set, Tuple and Dictionary

Week 5: Advance Python: Part-I
1. Object Oriented Programming (OOP) concepts, Classes, Constructor, Inheritance and Polymorphism.
2. Introduction to Module, Package: Creation and Hierarchy
3. Error, Handling Exceptions and File Handling
4. Exercises on OOP concepts, module, exception and file handling

Week 6: Advance Python: Part-II
1. Numpy: Basics, Array Attributes, Slicing (1D, 2D and 3D), Copy and View.
2. Numpy Array Iteration, Linear algebra operations array, array operations.
3. Pandas: Basics, Series and DataFrames, Load files to DataFrames and Methods of Pandas
4. Exercises on Numpy and Pandas.

Week 7: Advance Python: Part-III
1. Data Visualization: Histogram, Barplot, Kernel density estimation curve, Correlation matrix plot, Scatter plot etc.
2. Sklearn library: Basics, Properties and functionalities.
3. Introduction to Deep Learning Frameworks: Tensorflow, Keras and PyTorch.
4. Exercises in data visualization and sklearn library.

Week 8: Data Preprocessing
1. Data Preprocessing, Handling missing values, Class Imbalance and its remedies
2. Feature Scaling, Transformation, Discretization, Image and Text Preprocessing
3. Dimensionality Reduction, Feature Ranking, Feature Selection and Feature Extraction
4. Exercises on Data Preprocessing and Dimensionality Reduction

Week 9: Supervised Learning
1. Machine Learning, Supervised Learning, Regression and Classification, Training and Testing data.
2. Linear Regression, K-Nearest Neighbor and Decision Tree algorithms.
3. Performance measures: Error, Accuracy, Precision, Recall, Confusion Matrix and AUC Score.
4. Exercises on supervised learning algorithms.

Week 10: Unsupervised Learning
1. Logistic Regression, Artificial Neural Networks: Perceptron and Multilayer Perceptron
2. Parameters, Hyper parameters, Underfitting, Overfitting, Regularization etc.
3. Unsupervised Learning: Clustering, K-Means Clustering and Hierarchical Clustering
4. Exercises on Logistic Regression, Artificial Neural Networks and Clustering

Week 11: Introduction to Deep Learning
1. Deep Learning: Introduction, Difference and Similarity between Machine Learning and Deep Learning
2. Image Classification and Convolutional Neural Networks.
3. Text Classification and Recurrent Neural Networks
4. Exercises on image and text classification using deep learning models.

Week 12: End to end project
1. Steps for end to end machine learning project.
2. End-to-end implementation of various real-life projects

Books and references

1. Kroese, D. P., Botev, Z., Taimre, T., & Vaisman, R. (2019). Data science and machine learning: mathematical and statistical methods. CRC Press.
2. Grus, J. (2019). Data science from scratch: first principles with python. O'Reilly Media.
3. Thareja, R. (2022) Data Science and Machine Learning using Python. McGraw Hill.

Instructor bio

Prof. Faisal Anwer

Aligarh Muslim University
Prof. Faisal Anwer is working as an Assistant Professor in Department of Computer Science, AMU, Aligarh since year 2014. He holds Ph.D in information security, Master degree in Computer Application from Jamia Millia Islamia, New Delhi. Prior to joining AMU, he worked as a Sr. Software Engineer in Computer Science Corporation (CSC), Noida. He has also worked with CSC, UK in 2009-2010 as a software developer and was located in Perth, Scotland. His research areas and special interest include data science, information Security, Program Robustness and Software Testing. He has also published several research papers in International/National conferences and Journals of international repute.


Prof. Mohammad Nadeem

Prof. Mohammad Nadeem is currently working as Assistant Professor in the Department of Computer Science, AMU, Aligarh. He joined the department in June 2016. He received his Ph.D in Computer Science from IIT(ISM), Dhanbad, Jharkhand in 2017. Before that, he has also worked as Assistant System Engineer at Tata Consultancy Services (TCS) from January 2012 to February 2013. He has completed his Masters and Graduation from AMU in 2011 and 2008 respectively. He was also awarded University Medal in Graduation. His research areas include Data Science, Machine Learning and Soft Computing.

Course certificate

The course is free to enroll and learn from. But if you want a certificate, you have to register and write the proctored exam conducted by us in person at any of the designated exam centres.
The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).
Date and Time of Exams: 25 October 2024 Morning session 9am to 12 noon; Afternoon Session 2pm to 5pm.
Registration url: Announcements will be made when the registration form is open for registrations.
The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published. If there are any changes, it will be mentioned then.
Please check the form for more details on the cities where the exams will be held, the conditions you agree to when you fill the form etc.

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.

Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

Certificate will have your name, photograph and the score in the final exam with the breakup.It will have the logos of AMU and INI.

Only the e-certificate will be made available. Hard copies will not be dispatched.

Once again, thanks for your interest in our online courses and certification. Happy learning.

- INI Team


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