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

Design and Tailor this course

As per your team needs

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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 Anaconda
  • Python Notebooks
  • Installing Python Scientific libraries Numpy, Scipy, Scikit-learn, Matplotlib,pandas
  • Introduction to Python
  • The Basics of NumPy Arrays
  • Universal Functions
  • Arrays Broadcasting
  • Comparisons, Masks, and Boolean Logic
  • Fancy Indexing
  • Simulating Random Variables
  • Random Number Generation
  • Statistics Functions
  • Continuous Random Variables
  • Statistical Tests
  • Simulating Random Variables
  • Random Number Generation
  • Statistics Functions
  • Continuous Random Variables
  • Statistical Tests
  • Different types of machine learning
  • Machine Learning Applications
  • Data Representation in Scikit-Learn
  • Scikit-Learn’s Estimator API
  • Lab: Exploring Handwritten Digits
  • Simple Linear Regression
  • Multiple Linear Regression
  • Hands-on Exercise
  • An Overview of Classification
  • Logistic Regression
  • Multiple Logistic Regression
  • Bayes’ Theorem for Classification
  • Linear Discriminant Analysis
  • Quadratic Discriminant Analysis
  • KNN
  • Hands-on Exercises
  • 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
  • What is Feature Engineering?
  • Why Feature Engineering?
  • How to apply Feature Engineering?
  • Discussions on various scenarios
  • Hands-on Exercises
  • 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
  • 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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