Curriculum of Python

We are deeply involved in curriculum design ensuring that it meets the current industry requirements for data science professionals.

Core Python

• Introduction

    History
    Features
    Setting up path
    Basic Syntax
    Variable and Data Types
    Operator
  

• Conditional Statements

    If
    If- else
    Nested if-else
  

• Looping

    For
    While
    Nested loops
  

• Control Statements

    Break
    Continue
    Pass
  

• String Manipulation

    Accessing Strings
    Basic Operations
    String slices
    Function and Methods
  

• Lists

    Introduction
    Accessing list
    Operations
    Working with lists
    Function and Methods
  

• Tuple

    Introduction
    Accessing tuples
    Operations
    Working
    Function and Methods
  

• Dictionaries

    Introduction
    Accessing values in dictionaries
    Operations
    Working
    Functions
  

• Functions

    Defining a function
    Calling a function
    Types of functions
    Function Arguments
    Anonymous functions
    Global and local variables
  

• Modules

    Importing module
    Math module
    Random module
    Packages
    Composition
  

• Input-Output

    Printing on screen
    Reading data from keyboard
    Opening and closing file
    Reading and writing files
    Functions
  

• Exception Handling

    Exception
    Exception Handling
    Except clause
    Try ? finally clause
    User Defined Exceptions
  

• Statistical Methods for Decision Making

    Probability distribution
    Normal distribution
    Poisson's distribution
    Bayes’ theorem
    Central limit theorem
    Hypothesis testing
    One Sample T-Test
    Anova and Chi-Square
  

Advance Python

• Linear and Logistic Regression

    Multiple linear regression
    Fitted regression lines
    AIC, BIC, Model Fitting, Training and Test Data
    Introduction to Logistic regression, interpretation, odds ratio
    Misclassification, Probability, AUC, R-Square
  

• Supervised Learning Classification

    CART
    KNN (classifier, distance metrics, KNN regression)
    Decision Trees (hyper parameter, depth, number of leaves)
    Naive Bayes
  

• Unsupervised Learning

    Clustering - K-Means & Hierarchical
    Distance methods - Euclidean, Manhattan, Cosine, Mahalanobis
    Features of a Cluster - Labels, Centroids, Inertia
    Eigen vectors and Eigen values
    Principal component analysis
  

• Ensemble Techniques

    Bagging & Boosting
    Random Forest
    AdaBoost & Gradient boosting
    Hackathon