Data Science with Python

Learn how to build Exploratory models, predictive models, gain practical experience in the application area of Data Science

Duration

3 Days

Level

Intermediate Level

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Python is a very powerful programming language used for many different applications. Over time, the huge community around this open source language has created quite a few tools to efficiently work with Python. In recent years, a number of tools have been built specifically for data science. As a result, analyzing data with Python has never been easier.

The course aims to give an understanding of Data Science. This course will help the student to build Exploratory models, predictive models, gain practical experience in the application area of Data Science.

Understand Python language basics and apply to data science
Practice iterative data science using Jupyter notebooks
Analyze data using Python libraries like pandas and numpy
Create stunning data visualizations with matplotlib and seaborn
Build machine learning models using scikitlearn

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The intended audience for this course:

Application Developers
DevOps Engineers
Architects
System Engineers
Technical Managers

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Setting up the python environment for machine learning

Setting up Anaconda
Python Notebooks
Installing Python Scientific libraries Numpy, Scipy, Scikit-learn, Matplotlib,pandas
Introduction to Python

Introduction to NumPy

The Basics of NumPy Arrays
Universal Functions
Arrays Broadcasting
Comparisons, Masks, and Boolean Logic
Fancy Indexing

Introduction to Pandas

Simulating Random Variables
Random Number Generation
Statistics Functions
Continuous Random Variables
Statistical Tests

Introduction to Statistics

Introduction to Machine Learning

Different types of machine learning
Machine Learning Applications

Introducing Scikit-Learn

Data Representation in Scikit-Learn
Scikit-Learn’s Estimator API
Lab: Exploring Handwritten Digits

Introduction to Linear Regression

Simple Linear Regression
Multiple Linear Regression
Hands-on Exercise

Introduction to Classification

An Overview of Classification
Logistic Regression
Multiple Logistic Regression
Bayes’ Theorem for Classification
Linear Discriminant Analysis
Quadratic Discriminant Analysis
KNN
Hands-on Exercises

Acquiring & Preparing Data

Content Acquisition Overview
Working with Beautiful Soup
Acquiring data using Rest Based APIs
Data Cleaning & Wrangling using Pandas
Missing Values and Outlier
Cleansing Twitter Data
Performing Twitter Sentiment Analysis
Hands-on Exercises

Feature Engineering

What is Feature Engineering?
Why Feature Engineering?
How to apply Feature Engineering?
Discussions on various scenarios
Hands-on Exercises

Resampling Methods

Cross-Validation
Leave-One-Out Cross-Validation
K fold Cross-Validation
Bias-Variance Trade-Off for k-Fold Cross-Validation
The Bootstrap
Hands-on Exercises

Model Selection and Regularization

Best Subset Selection
Stepwise Selection
Choosing the Optimal Model
Ridge Regression
The Lasso
Selecting the Tuning Parameter
Principal Components Regression
Regression in High Dimensions
Hands-On Exercises

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Participants should preferably have some hands-on experience in Python Programming.

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Data Science with Python

Duration

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