Data Science With Python

Module 1:Python Essentials


Introduction

  • What is Python..?
  • A Brief history of Python
  • Why Should I learn Python..?
  • Installing Python
  • How to execute Python program
  • Write your first program

Variables & Data Types

  • Variables
  • Numbers
  • String
  • Lists ,Tuples & Dictionary

Conditional Statements & Loops

  • if...statement
  • if...else statement
  • elif...statement
  • The while...Loop
  • The for....Loop

Control Statements

  • continue statement
  • break statement
  • pass statement

Functions

  • Define function
  • Calling a function
  • Function arguments
  • Built-in functions

Modules & Packages

  • Modules
  • How to import a module...?
  • Packages
  • How to create packages

Classes & Objects

  • Introduction about classes & objects
  • Creating a class & object
  • Inheritance
  • Methods Overriding
  • Data hiding

Files & Exception Handling

  • Writing data to a file
  • Reading data from a file
  • Read and Write data from csv file
  • try...except
  • try...except...else
  • finally
  • os module

Module 2:Introduction to Machine Learning(ML)


  • What is Machine learing?
  • Overview about sci-kit learn and tensorflow
  • Types of ML
  • Some complementing fields of ML
  • ML algorithms
  • Machine learning examples

NumPy Arrays


  • Creating multidimensional array
  • NumPy-Data types
  • Array attributes
  • Indexing and Slicing
  • Creating array views and copies
  • Manipulating array shapes
  • I/O with NumPy

Working with Pandas


  • Installing pandas
  • Pandas dataframes
  • Pandas Series
  • Data aggregation with Pandas DataFrames
  • Concatenating and appending DataFrames
  • Manipulating array shapes
  • Handling missing data
  • Joining DataFrames

Python Regular Expressions


  • What are regular expressions?
  • The match Function
  • The search Function
  • Matching vs searching
  • Search and Replace
  • Extended Regular Expressions
  • Wildcard

Python Oracle Database Access


  • Install the cx_Oracle and other Packages
  • Create Database Connection
  • CREATE, INSERT, READ, UPDATE and DELETE Operation
  • DML and DDL Operation with Databases
  • Performing Transactions
  • Handling Database Errors
  • Disconnecting Database

Regression based learning


  • Simple regression
  • Multiple regression
  • Logistic regression

Data mining


  • Introducing data mining
  • Decision Tree
  • Affiity Analysis
  • Clustering

Introducing matplotlib


  • Bar Charts
  • Line Charts
  • Scatter plots
  • Bubble charts

Clustering based learning


  • Defnition
  • Types of clustering
  • The k-means clustering algorithm
  • Predicting house prices with regression

Working with openCV


  • Setting up opencv
  • Loading and displaying images
  • Applying image filters
  • Tracking faces
  • Face recognition
  • Module 10:Performing predictions with Linear Regression
  • Simple linear regression
  • Multiple regression
  • Training and testing model

Introduction to Python

Installation of Python framework and packages: Anaconda & pip Writing/ Running python programs using Spyder Command Prompt Working with Jupyter, notebooks, Creating Python variables Numeric , string and logical operations Data containers : Lists , Dictionaries, Tuples & sets Practice assignment

Iterative Operations & Functions in Python

Writing for loops in Python

While loops and conditional blocks List/Dictionary comprehensions with loops.

Writing your own functions in Python Writing your own classes and functions.


Data Handling in Python using NumPy & Pandas

Introduction to NumPy arrays, functions & properties Introduction to Pandas & data frames

Importing and exporting external data in Python Feature engineering using Python


Data Handling in Python using NumPy & Pandas

Introduction to NumPy arrays, functions & properties Introduction to Pandas & data frames

Importing and exporting external data in Python Feature engineering using Python


Data Science & Machine Learning in Python


Machine Learning Basics

Converting business problems to data problems
Understanding supervised and unsupervised learning with examples
Understanding biases associated with any machine learning algorithm
Ways of reducing bias and increasing generalisation capabilites Drivers of machine learning algorithms Cost functions
Brief introduction to gradient descent
Importance of model validation
Methods of model validation
Cross validation & average error


Generalised Linear Models in Python

Linear Regression
Regularisation of Generalised Linear Models
Ridge and Lasso Regression
Logistic Regression
Methods of threshold determination and performance measures for classification score models


Tree Models using Python

Introduction to decision trees
Tuning tree size with cross validation Introduction to bagging algorithm Random Forests
Grid search and randomized grid search ExtraTrees (Extremely Randomised Trees) Partial dependence plots


Support Vector Machines (SVM) & kNN in Python

Introduction to idea of observation based learning Distances and similarities k Nearest Neighbours (kNN) for classi cation Brief mathematical background on SVM/li> Regression with kNN & SVM


Unsupervised learning in Python

Need for dimensionality reduction
Principal Component Analysis (PCA) Di erence between PCAs and Latent
Factors Factor Analysis
Hierarchical, K-means & DBSCAN Clustering


Text Mining in Python

Gathering text data using web scraping with urllib
Processing raw web data with BeautifulSoup
Interacting with Google search using urllib with custom user agent Collecting twitter data with Twitter API
Naive Bayes Algorithm
Feature Engineering with text data
Sentiment analysis