Master program in Data Science

Introduction to Python

• Overview of Python
• The Companies using Python
• Different Applications where Python is used
• Discuss Python Scripts on UNIX/Windows
• Values, Types, Variables
• Operands and Expressions
• Conditional Statements
• Loops
• Command Line Arguments
• Writing to the screen

Sequences and File Operations

• Python files I/O Functions
• Numbers
• Strings and related operations
• Tuples and related operations
• Lists and related operations
• Dictionaries and related operations
• Sets and related operations

Deep Dive – Functions, OOPs, Modules, Errors and Exceptions

• Functions
• Function Parameters
• Global Variables
• Variable Scope and Returning Values
• Lambda Functions
• Object-Oriented Concepts
• Standard Libraries
• Modules Used in Python
• The Import Statements
• Module Search Pat
• Package Installation Ways
• Errors and Exception Handling
• Handling Multiple Exceptions

Introduction to NumPy, Pandas and Matplotlib

• NumPy – arrays
• Operations on arrays
• Indexing slicing and iterating
• Reading and writing arrays on files
• Pandas – data structures & index operations
• Reading and Writing data from Excel/CSV formats into Pandas
• matplotlib library
• Grids, axes, plots
• Markers, colours, fonts and styling
• Types of plots – bar graphs, pie charts, histograms
• Contour plots

Data Manipulation

• Basic Functionalities of a data object
• Merging of Data objects
• Concatenation of data objects
• Types of Joins on data objects
• Exploring a Dataset
• Analysing a dataset

• What is Machine Learning?
• Machine Learning Use-Cases
• Relation between Machine Learning & AI Relation
• Machine Learning Process Flow
• Machine Learning Categories
• Machine Learning Future & Scope
• Linear regression
• Gradient descen

Supervised Learning & Classification

• What are Classification and its use cases?
• What is Decision Tree?
• Algorithm for Decision Tree Induction
• Creating a Perfect Decision Tree
• Confusion Matrix
• What is Random Forest?

Dimensionality Reduction

• Introduction to Dimensionality
• Why Dimensionality Reduction
• PCA
• Factor Analysis
• Scaling dimensional model
• LDA

Supervised Learning – Advance Part

• What is Naïve Bayes?
• How Naïve Bayes works?
• Implementing Naïve Bayes Classifier
• What is a Support Vector Machine?
• Illustrate how Support Vector Machine works?
• Hyperparameter Optimization
• Grid Search vs Random Search
• Implementation of Support Vector Machine for Classification

Unsupervised Learning

• What is Clustering & its Use Cases?
• What is K-means Clustering?
• How does the K-means algorithm work?
• How to do optimal clustering
• What is C-means Clustering?
• What is Hierarchical Clustering?
• How Hierarchical Clustering works?

Association Rules Mining and Recommendation Systems

• What are Association Rules?
• Association Rule Parameters
• Calculating Association Rule Parameters
• Recommendation Engines
• How does Recommendation Engines work?
• Collaborative Filtering
• Content-Based Filtering

Reinforcement Learning

• What is Reinforcement Learning
• Why Reinforcement Learning
• Elements of Reinforcement Learning
  Exploration vs
• Exploitation dilemma
• Epsilon Greedy Algorithm
• Markov Decision Process (MDP)
• Q values and V values
• Q – Learning
• α values

Time Series Analysis

• What is Time Series Analysis?
• Importance of TSA
• Components of TSA
• White Noise
• AR model
• MA model
• ARMA model
• ARIMA model
• Stationarity
• ACF & PACF

Model Selection and Boosting

• What is Model Selection?
• The need for Model Selection
• Cross-Validation
• What is Boosting?
• How Boosting Algorithms work?
• Types of Boosting Algorithms
• Adaptive Boosting

Ensemble Learning

• Ensemble Learning Methods
• Working of AdaBoost
• AdaBoost Algorithm and Flowchart
• Gradient Boosting
• XGBoost0
• XGBoost Parameters
• Pima Indians Diabetes
• Model Selection
• Common Splitting Strategies

Recommender Systems

• Purposes of Recommender Systems
• Paradigms
• Collaborative Filtering
• Association Rule
• Apriori Algorithm: Rule Selection