Data Science on Python

OrangeTree Global recognizes the importance of learning Python and analytics in today’s data-driven world. Python is a widely-used programming language that is versatile, easy to learn, and has a vast library of resources. It can be used for various tasks, including data analysis and visualization, making it an essential tool for professionals in various industries. Analytics, on the other hand, enables businesses to make informed decisions based on data insights. By learning how to use Python for analytics, individuals can unlock a wealth of valuable information that can help them make strategic business decisions. At Orangetree Global, we understand the significance of mastering these skills to stay competitive and drive success in today’s market.

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Data Science on Python

This is 10 day virtual live instructor led program spread over 1.5 months (on weekends)

Python Programming

In this session, we will focus on the essential features of Python required for you to know before you look at Data Science Applications. We will presume you have zero knowledge and no technical background.

Module I : Introduction to Python programming

  • Installation and setup
  • Data types
  • Pandas Introduction
  • Logical Operations
  • Numpy
  • Universal functions

Module II : Conditional Structures, File Handling, Encoding

  • Arrays, Lists, Tuples, Dictionary,
  • Arithmetic Operators, Comparison Operators, Logical Operators, Frozen Set
  • String Operation and Manipulation
  • Date Time Operation
  • Exception Handling
  • Assertions
  • Pandas Session II

Module III : Control Structures, Operations on List, Tuples, Dictionary and Set

  • Control Structure
  • Indexing 
  • Counting 
  • Add and Update 
  • Set Algebra
  • IF, nested IF, IF- ELIF, Shorthand IF-ELSE,
  • Logic Operation with IF-ELSE, Pass-Break and Continue
  • While Loops 
  • For loops  
  • Pandas Session III

Module IV : Functions and Classes

  • Why Functions and Classes
  • How to create a function
  • Create a recursion function
  • Inline function
  • Init function
  • Object & Class methods
  • Encapsulation 
  • Public Member, Private Member

Module V : Data Structure

  • Stack and its uses
  • Queue and its uses
  • Scientific approach to problem solving using case study 
  • Linear search
  • Binary search
  • Time and space complexity (Big O Notation)
  • Travelling Salesman Problem
Data Science Application (on Python)

In this session, we look at the application of key Analytical Techniques on Python which are predominant in the Data Science and Analytics Industry. These are some of the most widely used applications of Python.

Module I : Data Visualisation, Math for data science

  • Plots using matplotlib
  • Central Tendency
  • Dispersion
  • Data distributions
  • Percentiles and moments
  • Covariance and correlation
  • Bayes Theorem

Module II :  Feature Engineering

  • Replacing NaN values
  • Imputation techniques
  • Scaling
  • Identifying outliers
  • Trimming
  • Rank Transformation
  • Encoding techniques
  • Engineering dates, mixed variable and Transformation

Module III :  Unsupervised and Supervised Machine Learning for Predictive Modelling

  • Linear regression
  • Supervised and unsupervised ML methods
  • Understating confusion matrix
  • K Means Clustering
  • Performance Parameters for Regression

Module IV : Recommender systems - Classification Problem

  • KNN 
  • Decision trees
  • XG boost 
  • Bagging and boosting
  • Hands on Implementation of Bagging and Boosting techniques
  • Hyperparameter Tuning

Module V : Model Deployment

  • Deploying models
  • Hypothesis Testing
  • Showcase an end to end ML project implementation using Django framework
  • NLP introduction and hands on sentiment analysis