**PYTHON, R, DATASCIENCE**

Get trained by the industry experts. We are leading Best Python Training Institute in Delhi offering state-of-art facilities and project assistance.

## Data Science Basics

Data, Data Types, Meaning of Variables, Central Tendency, Measures of Dispersion, Data Distribution,

## Predictive Modelling

Decision Trees, Neural Networks, Predictive Modeling with Decision Trees,

## Neural Networks,

Perceptron, MLP, Back Propagation, Revision of Key Concepts,

## ANOVA/ Regression Analysis,

Analysis of Variance & Covariance, Analysis of Variance, ANOVA Results, Examine Regression Results, Regression Analysis, Linear and Logistic Regression,

## Tree and Bayesian Network Models

Decision Trees, Bagging, Random Forests, Boosted Trees, Bayesian Classification Models,

## R Programming

R Basics, , R Base Software, Understanding CRAN, R Studio The IDE, Sequence of Numbers, Vectors, Basic Operations, Operators and Types, R Functions,

## Logistic Regression in R

Reason for Logistic Regression, The Logistic Transform, Logistic Regression Modelling, Model Optimisation, Understanding ROC Curve, Default Modelling using Logistic Regression in R,

## Decision Trees

Theory of Entropy & Information Gain, Stopping Rules, Cross Validations for Overfitting Problem, Pruning as a Solution for Overfitting, Ensemble Learning, Bootstrap Aggregation, Random Forests, Intrusion Detection in IT Network,

## Linear Regression in R

Covariance and Correlation, Multivariate Analysis, Hypothesis Testing, Limitations of Regression, Business Case: Managing Credit Risk, Loss Given Default using Linear Regression,

## Support Vector Machine

Classification as a Hyper Plane Location Problem, Motivation for Linear Support Vectors, Quadratic Optimization, Non Linear SVM, Kernel Functions, Default Modelling using SVM in R,

## Python Programming

Python Basics, , What is Python?, Installing Anaconda, Understanding the Spyder Integrated Development Environment (IDE), Lists, Tuples, Dictionaries, Variables,

## Data Frame Manipulation

Data Acquisition, Indexing, Filtering, Sorting & Summarizing, Descriptive Statistics, Combining and Merging Data Frames, Discretization and Binning, String Manipulation,

## Data Structures in Python

Intro to Numpy Arrays, Creating ndarrays, Indexing, Data Processing using Arrays, File Input and Output, Getting Started with Pandas,

## Other Predictive Modelling Tools

Intro to Machine Learning, Random Forests, Sklearn Library and Statsmodels,

## SAS Programming

SAS Basics, , Key Features, Submitting a SAS Program, SAS Program Syntax, Examining SAS Datasets Accessing SAS Libraries, Sorting and Grouping, Reporting Data, Using SAS Formats,Data Transformations, , Writing Observations, Writing to Multiple Datasets, Accumulating Total, Creating Accumulating Total for a Group of Data, Data Transformations,

## SQL

SQL & RDBMS, SQL Procedures, Presenting & Summarizing Data, Join Queries using SQL, Subqueries, Indexes and Views, Set Operators, Creating Tables and Views using Proc SQL,

## Reading and Manipulating Data

Reading SAS Datasets, Reading Excel Data, Reading Raw Files, Reading Database Data, Creating Summary Reports, Combining Datasets, Macros, , Automatic Macro Variables, User Defined Macro Variables, Macro Variable Reference, Defining and Calling Macros, Macro Parameters, Global and Local Symbol Tables, Macro Variables in the Data Step,

## Tableau and Job Readiness

Tableau Basic, , Introduction to Visualization, Working with Tableau, Visualization in Depth, Data Organisation, Advanced Visualization, Mapping, Enterprise Dashboards, Data Presentation,

## Final Project

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