Zenitech Institute for Embedded and Software Training

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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