Introduction to Data Science
The objective is to provide an understanding of Linux concepts. Understanding of Python. Throughout the course you will learn different types of sequence structures, related operations and their usage. Comprehensive course focused towards giving you a head start in Machine Learning and applying them to make beautiful machine learning models to solve problems. We also then give you an introduction to Deep Learning with a basic implementation of an ANN.
- Darshan DV
- Mohit Basi
- Priyam Kumar
- Sankarshan Guru
- Shushant Kumar
- Rahul Kumaresan
- Suraj Hegde
9 weeks , 2-3 Hours a week
- Weekly assignments, Final are also part of the SMP.
Ubuntu Installation, Jupyter notebook, Python3 (+ numerous Modules), Google Collab
9 Weeks
Week 1:
- What is Linux? How is it different from other operating systems?
- Features of Linux environment
- Basic commands like pwd,ls,cd,mkdir,rm,cat,cp,grep,sed
- File permissions and wildcards
- Shell script
Week 2:
- Why Python? How is it different from other languages?
- Operators and loops
- List - properties, related operations
- Tuple - properties, related operations, comparison with list
- Dictionary - properties, related operations, comparison with list
- Set - properties, related operations, comparison with dictionary
- Functions - syntax, arguments, keyword arguments, return values, anonymous functions
Week 3:
- Sorting - sequences, dictionaries, limitations of sorting
- Errors and exceptions - types of issues, remediation
- Packages and module - modules, import options, sys path
- File handling - modes, operations
- Classes - classes and objects, access modifiers, instance and class members
- OOPS paradigm - Inheritance, Polymorphism and Encapsulation in Python
Week 4:
- Introduction to numpy
- Introduction to pandas
- What is DataFrame?
- Basic functions intro using .csv files
Week 5:
- Intro to the raw data that will be used.
- Structuring the data in the required format
- Processing of the data for further analysis
- Assignment based on the same.
Week 6:
- Intro to ML, Applications of ML and various topics in it
- Regression vs Classification basics
- Installing scikit learn and working on Google Collab
- Building a basic regression model
- Assignment based on the same
Week 7:
- Building a basic classification model
- Other classification techniques using classifiers in scikit learn
- Assignment based on the same
Week 8:
- Introduction to Neural Networks and Deep Learning
- What is Deep Learning?
- About Neuron
- How do Neural Network work and learn?
- Activation Function
- Backpropagation
Week 9:
- Building an ANN
- Installing Theano, Keras, Tensorflow
- Building a simple ANN with Python