Python

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

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    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

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