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Data Science with Python

  /    /  Data Science with Python

Data Science with Python

Categories:
Data Science
Reviews:

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

The intended audience for this course:

  • Application Developers
  • DevOps Engineers
  • Architects
  • System Engineers
  • Technical Managers
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
  • Simple Line Plots
  • Simple Scatter Plots
  • Customizing Plot Legends
  • Text and Annotation
Introduction to Statistics
  • Simulating Random Variables
  • Random Number Generation
  • Statistics Functions
  • Continuous Random Variables
  • Statistical Tests
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

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

Course Information

Duration

3 Days / 4 Days

Mode of Delivery

Instructor led/Virtual

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