Python

Data Science

Syllabus

 

  1. Introduction to Python                                                                                 

What is python?, python identifiers, keywords, statement, operators, literals, data types, if statement, if else statement, if elif else statement, nested if statement, for loop, for loop with else, while loop, while loop with else, break, continue and pass, conditional statement, python string, list, tuple, set, dictionary operations, python functions and modules: lambda function, class creation, object creation, object functions, numpy, pandas

  1. Basic Statistical methods                                                                             

What is Statistics?, Descriptive and Inferential methods, some distributions and applications, estimation and testing procedures, accuracy, precision, variance and bias, estimator, least squares, residuals, mean square errors, cost or loss function

  1. Introduction to Data Science and Machine Learning                                    

What is Data Science? Importance, Uses, data science life cycle, components, What is Machine learning?, supervised learning and unsupervised learning.

  1. Supervised Learning methods                                                                     

Linear regression and regularization, implementation of linear regression, lasso regression, ridge regression and their implementations, Multiple linear regression, polynomial regression and implementations, Logistic regression: Normal distribution, Random distribution, Logistic Function, Logit function, Implementations, confusion matrix, K-fold cross validation, leave one out, gradient descent, feature extraction and preprocessing.

  1. Unsupervised Learning methods                                                                   

Classification methods: KNN classification, Naive Bayes classification, and implementations(Iris data set), Clustering methods: K means clustering, Hierarchical clustering, Dimensionality reduction: Principal Component Analysis(PCA), PCA for face recognition, Singular Value Decomposition(SVD), applications.

  1. Artificial Intelligence(AI) and Deep Learning(DL)                                     

Types, ways of achieving AI, applications, case study: if a person has diabetes or not, Deep learning: introduction, neural networks: Artificial Neural Network(ANN), Convolutional Neural Network(CNN), Recurrent Neural Network(RNN)