This repo contains the content for IDMA part time program. The repo will remain a work in progress.
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Pre-requisites
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Install anaconda on your system. Visit here to download the software.
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After installation you should be able to see
anaconda prompt
in your search bar -
Revise basic ideas of programming such as variables, loops, conditionals.
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Readings (Some resources require Orielly Credentials)
Pre-reads
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Python 101: Mark Lutz, Learning Python, 5th Edition, Chapter 4
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Learning Objectives: Develop a basic familiarity with basic python datastructures, understand how to get help in a python interpretor.
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Oops for DS:Mark Lutz, Learning Python, 5th Edition, Chapter 26
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Learning Objectives: Understand the idea of classes, objects and inheritance.
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Pandas 101:Matt Harisson, Pandas 1.x Cookbook, Chapter1
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Learning Objectives: Develop familiarity with dataframe and series objects in pandas, develop comfort in working with chaining of operations.
Post Session Reads
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Python 101 Mark Lutz, Learning Python, 5th Edition, Chapter 10, 13, 14
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Learning Objectives: Be able to write for loops using list comprehension and zip constructs. Be confident in using conditionals and loops in python.
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Oops for DS: Mark Lutz, Learning Python, 5th Edition, Chapter 27. You can skim through the portion on operator overloading.
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Learning Objectives: Be able to write a python class, impliment inheritance and use double dunder methods.
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Pandas 101: Matt Harisson, Pandas 1.x Cookbook, Chapter2, Chapter3, Chapter4, Chapter5, Chapter6, Chapter7, Chapter9 You can complete these readings over a span of 2-3 weeks. As the course progresses you will find yourself using this book as a quick reference.
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Learning Objectives: Be able to decide how to reduce memory footprint by changing datatypes of columns. Be aware of different data import/export mehods in pandas. Understand the need for index alignment and be able change indices to align them properly.
- Lecture5: Regression
- Lecture6: Non linear classification
- Lecture7: Collaborative Filtering
- Lecture8: Introduction to Neural Networks
- Lecture9: Feed forward neural networks, backpropagation and stochastic gradient descent
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