Call :+91 8055223360

Menu

5918 Ratings

1880 Learners

Radical Data Science PG program consists of Practical learning of Data Science with Python & R, Apache Spark & Scala, AI & Deep Learning with TensorFlow, Tableau. Consists of 25 Projects | 200 + Assignments and 50 hours of Mock interview session. You will acquire a knowledge of 4+ year experienced data scientist.

This program is good for freshers and up to 3 – 4 years of industry experienced professional . Those who have career gap

**Major courses are **Data Science with Python & R, Apache Spark & Scala, AI & Deep Learning with TensorFlow ,Tableau Along with this 30 Plus tools are covered as part of this training

0
+

Students Empowered

0
+

Hiring Partners

Batch Schedule

**About Ofqual Qualifications –** The Office of Qualifications and Examinations Regulation (Ofqual) regulates qualifications, examinations and assessments in England.

- Designed for Freshers to Working Professionals
- Eligible for Ofqual regulates qualifications – UK Certified and World Recognised programs
- 30+ Programming Tools & Languages
- 30+ Industry Projects , 100 + Live assignments , 150+ coding Solutions.
- Ofqual - UK validated PG Diploma from UK

- 360 Degree Career Support
- One-on-One with Industry Mentors
- Dedicated Student Mentor
- Job Assistance with Top Firms
- No Cost EMI Option

- othm
- rear view

- Post Graduate Diploma without Spedning full time in College
- Eligible for Ofqual regulates qualifications – UK Certified and World Recognised programs
- Level 7 Program recognised world wide for your Higher studies
- Get recognised by High Value world recognised UK equal Master Degree

What is the eligibility Criteria ?

Any One who is interested in statistics and programming . Those who drop out , Looking for Higher studies in UK and any other foreign countries

Is this is a Job Guaranteed program

Yes we give guaranteed interview calls until you find the Job . Minimum 5 interview calls and maximum until you get the satisfied Job .

What is RCITP ?

Radical certified IT Professional – After 6 month of completion of the training, you will be awarded masters Certificate from Radical – Masters in Data Science & AI – RCITP

.whether Master’s program is integrated with International PG Certificate program ?

Yes it is. After 6 Month of enrolling for the masters course , and successfully completing the RCITP program ,You needs to undergo more projects and Assessments to obtain the PG Program . By default , all Master programs are integrated with International PG Certificate Program . By default , it will be converted into International PG certificate Program.

Selection procedure

Once you enrolled for the course . Within one month , you have to complete the fee . If you need any loan facility , This should be informed earlier before enrolling to make necessary arrangements . Enrolment process of PG Diploma program will be starting after 1 month . Necessary documents should be provided to enrol UK PG Program .

Python Statistics for Data Science Course Curriculum

Understanding the Data

Goal: In this module, you will be introduced to data and its types and accordingly sample data and derive meaningful information from the data in terms different statistical parameters.

Objectives: At the end of this Module, you should be able to:

Understand various data types

Learn Various variable types

List the uses of variable types

Explain Population and Sample

Discuss sampling techniques

Understand Data representation

Topics:

Introduction to Data Types

Numerical parameters to represent data

Mean

Mode

Median

Sensitivity

Information Gain

Entropy

Statistical parameters to represent data

Hands-On/Demo

Estimating mean, median and mode using python

Calculating Information Gain and Entropy

Probability and its uses

Goal: In this module, you should learn about probability, interpret & solve real-life problems using probability. You will get to know the power of probability with Bayesian Inference.

Objectives: At the end of this Module, you should be able to:

Understand rules of probability

Learn about dependent and independent events

Implement conditional, marginal and joint probability using Bayes Theorem

Discuss probability distribution

Explain Central Limit Theorem

Topics:

Uses of probability

Need of probability

Bayesian Inference

Density Concepts

Normal Distribution Curve

Hands-On/Demo:

Calculating probability using python

Conditional, Joint and Marginal Probability using Python

Plotting a Normal distribution curve

Statistical Inference

Goal: Draw inferences from present data and construct predictive models using different inferential parameters (as a constraint).

Objectives: At the end of this Module, you should be able to:

Understand the concept of point estimation using confidence margin

Draw meaningful inferences using margin of error

Explore hypothesis testing and its different levels

Topics:

Point Estimation

Confidence Margin

Hypothesis Testing

Levels of Hypothesis Testing

Hands-On/Demo:

Calculating and generalizing point estimates using python

Estimation of Confidence Intervals and Margin of Error

Testing the Data

Goal: In this module, you should learn the different methods of testing the alternative hypothesis.

Objectives: At the end of this module, you should be able to:

Understand Parametric and Non-parametric Testing

Learn various types of parametric testing

Discuss experimental designing

Explain a/b testing

Topics:

Parametric Test

Parametric Test Types

Non- Parametric Test

Experimental Designing

A/B testing

Hands-On/Demo:

Perform p test and t tests in python

A/B testing in python

Data Clustering

**Goal:**Get an introduction to Clustering as part of this Module which forms the basis for machine learning.**Objectives:**At the end of this module, you should be able to:- Understand the concept of association and dependence
- Explain causation and correlation
- Learn the concept of covariance
- Discuss Simpson’s paradox
- Illustrate Clustering Techniques

**Topics:**- Association and Dependence
- Causation and Correlation
- Covariance
- Simpson’s Paradox
- Clustering Techniques

**Hands-On/Demo:**- Correlation and Covariance in python
- Hierarchical clustering in python
- K means clustering in python

Regression Modelling

**Goal:**Learn the roots of Regression Modelling using statistics.**Objectives:**At the end of this module, you should be able to:- Understand the concept of Linear Regression
- Explain Logistic Regression
- Implement WOE
- Differentiate between heteroscedasticity and homoscedasticity
- Learn the concept of residual analysis

**Topics:**- Logistic and Regression Techniques
- Problem of Collinearity
- WOE and IV
- Residual Analysis
- Heteroscedasticity
- Homoscedasticity

**Hands-On/Demo:**- Perform Linear and Logistic Regression in python
- Analyze the residuals using python

Pythn Statistics for Data Science Course Curriculum

Understanding the Data

Goal: In this module, you will be introduced to data and its types and will accordingly sample data and derive meaningful information from the data in terms of different statistical parameters.

Objectives: At the end of this Module, you should be able to:

Understand various data types

Learn Various variable types

List the uses of Variable types

Explain Population and Sample

Discuss Sampling techniques

Understand Data representation

Topics:

Introduction to Data Types

Numerical parameters to represent data

Mean

Mode

Median

Sensitivity

Information Gain

Entropy

Statistical parameters to represent data

Hands-On/Demo:

Estimating mean, median and mode using R

Calculating Information Gain and Entropy

Probability and its Uses

Goal: In this module, you will learn about probability, interpret & solve real-life problems using probability. You will get to know the power of probability with Bayesian Inference.

Objectives: At the end of this Module, you should be able to:

Understand rules of probability

Learn about dependent and independent events

Implement conditional, marginal and joint probability using Bayes Theorem

Discuss probability distribution

Explain Central Limit Theorem

Topics:

Uses of probability

Need of probability

Bayesian Inference

Density Concepts

Normal Distribution Curve

Hands-On/Demo:

Calculating probability using R

Conditional, Joint and Marginal Probability using R

Plotting a Normal distribution curve

Statistical Inference

Goal: In this module, you will be able to draw inferences from present data and construct predictive models using different inferential parameters (as the constraint).

Objectives: At the end of this Module, you should be able to:

Understand the concept of point estimation using confidence margin

Demonstrate the use of Level of Confidence and Confidence Margin

Draw meaningful inferences using margin of error

Explore hypothesis testing and its different levels

Topics:

Point Estimation

Confidence Margin

Hypothesis Testing

Levels of Hypothesis Testing

Hands-On/Demo:

Calculating and generalizing point estimates using R

Estimation of Confidence Intervals and Margin of Error

Testing the Data

Goal: In this module, you will learn the different methods of testing the alternative hypothesis.

Objectives: At the end of this module, you should be able to:

Understand Parametric and Non-Parametric testing

Learn various types of Parametric testing

Explain A/B testing

Topics:

Parametric Test

Parametric Test Types

Non- Parametric Test

A/B testing

Hands-On/Demo:

Perform P test and T tests in R

Data Clustering

Goal: In this module, you will get an introduction to Clustering which forms the basis for machine learning.

Objectives: At the end of this module, you should be able to:

Understand the concept of Association and Dependence

Explain Causation and Correlation

Learn the concept of Covariance

Discuss Simpson’s paradox

Illustrate Clustering TechniquesTopics:

Association and Dependence

Causation and Correlation

Covariance

Simpson’s Paradox

Clustering TechniquesHands-On/Demo:

Correlation and Covariance in R

Hierarchical clustering in R

K means clustering in R

Regression Modelling

Goal: In this module, you will be able to learn about the roots of Regression Modelling using statistics.

Objectives: At the end of this module, you should be able to:

Understand the concept of Linear Regression

Explain Logistic Regression

Implement WOE

Differentiate between heteroscedasticity and homoscedasticity

Learn concept of residual analysisTopics:

Logistic and Regression Techniques

Problem of Collinearity

WOE and IV

Residual Analysis

Heteroscedasticity

HomoscedasticityHands-On/Demo:

Perform Linear and Logistic Regression in R

Analyze the residuals using R

Calculation of WOE values using R

Pythn Statistics for Data Science Course Curriculum

Introduction to Data Science with Python

What is analytics & Data Science?

Common Terms in Analytics

Analytics vs. Data warehousing, OLAP, MIS Reporting

Relevance in industry and need of the hour

Types of problems and business objectives in various industries

How leading companies are harnessing the power of analytics?

Critical success drivers

Overview of analytics tools & their popularity

Analytics Methodology & problem solving framework

List of steps in Analytics projects

Identify the most appropriate solution design for the given problem statement

Project plan for Analytics project & key milestones based on effort estimates

Build Resource plan for analytics project

Python Essentials

Why Python for data science?

Overview of Python- Starting with Python

Introduction to installation of Python

Introduction to Python Editors & IDE’s(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)

Understand Jupyter notebook & Customize Settings

Concept of Packages/Libraries – Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)

Installing & loading Packages & Name Spaces

Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)

List and Dictionary Comprehensions

Variable & Value Labels – Date & Time Values

Basic Operations – Mathematical – string – date

Reading and writing data

Simple plotting

Control flow & conditional statements

Debugging & Code profiling

How to create class and modules and how to call them?

Scientific Distributions Used In Python For Data Science

NumPy, pandas, scikit-learn, stat models, nltk

Accessing/Importing And Exporting Data Using Python Modules

Importing Data from various sources (Csv, txt, excel, access etc)

Database Input (Connecting to database)

Viewing Data objects – subsetting Data, methods

Exporting Data to various formats

Important python modules: Pandas, beautiful soup

Data Manipulation – Cleansing – Munging using python modules

Cleansing Data with Python

Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)

Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)

Python Built-in Functions (Text, numeric, date, utility functions)

Python User Defined Functions

Stripping out extraneous information

Normalizing data

Formatting data

Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc)

Data Analysis – Visualization Using Python

Introduction exploratory data analysis

Descriptive statistics, Frequency Tables and summarization

Univariate Analysis (Distribution of data & Graphical Analysis)

Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)

Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)

Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, seaborn, Pandas and SciPy. Stats etc)

Introduction to Statistics

Basic Statistics – Measures of Central Tendencies and Variance

Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem

Inferential Statistics -Sampling – Concept of Hypothesis Testing Statistical Methods – Z/t-tests( One sample, independent, paired), Analysis of variance, Correlations and Chi-square

Important modules for statistical methods: NumPy, SciPy, Pandas

Introduction to Predictive Modelling

Concept of model in analytics and how it is used?

Common terminology used in analytics & Modelling process

Popular modelling algorithms

Types of Business problems – Mapping of Techniques

Different Phases of Predictive Modelling

Data Exploration For Modelling

Need for structured exploratory data

EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)

Identify missing data

Identify outliers data

Visualize the data trends and patterns

Data Preparation

Need of Data preparation

Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values- Dummy creation – Variable Reduction

Variable Reduction Techniques – Factor & PCA Analysis

Segmentation: Solving Segmentation Problems

Introduction to Segmentation

Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)

Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)

Behavioural Segmentation Techniques (K-Means Cluster Analysis)

Cluster evaluation and profiling – Identify cluster characteristics

Interpretation of results – Implementation on new data

Linear Regression: Solving Regression Problems

Introduction – Applications

Assumptions of Linear Regression

Building Linear Regression Model

Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)

Assess the overall effectiveness of the model

Validation of Models (Re running Vs. Scoring)

Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)

Interpretation of Results – Business Validation – Implementation on new data

Logistic Regression : Solving Classification Problems

Introduction – Applications

Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models

Building Logistic Regression Model (Binary Logistic Model)

Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)

Validation of Logistic Regression Models (Re running Vs. Scoring)

Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)

Interpretation of Results – Business Validation – Implementation on new data

Time Series Forecasting : Solving Forecasting Problems

Introduction – Applications

Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition

Classification of Techniques(Pattern based – Pattern less)

Basic Techniques – Averages, Smoothening, etc

Advanced Techniques – AR Models, ARIMA, etc

Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc

Machine Learning : Predictive Modelling

Introduction to Machine Learning & Predictive Modelling

Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs. Forecasting

Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning

Different Phases of Predictive Modelling (Data Pre-processing, Sampling, Model Building, Validation)

Overfitting (Bias-Variance Trade off) & Performance Metrics

Feature engineering & dimension reduction

Concept of optimization & cost function

Overview of gradient descent algorithm

Overview of Cross validation(Bootstrapping, K-Fold validation etc)

Model performance metrics (R-square, Adjusted R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )

Unsupervised Learning : Segmentation

What is segmentation & Role of ML in Segmentation?

Concept of Distance and related math background

K-Means Clustering

Expectation Maximization

Hierarchical Clustering

Spectral Clustering (DBSCAN)

Principle component Analysis (PCA)

Supervised Learning :- Decision Trees

Decision Trees – Introduction – Applications

Types of Decision Tree Algorithms

Construction of Decision Trees through Simplified Examples; Choosing the “Best” attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees

Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness

Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules

Decision Trees – Validation

Overfitting – Best Practices to avoid

Supervised Learning :- Ensemble Learning

Concept of Ensembling

Manual Ensembling Vs. Automated Ensembling

Methods of Ensembling (Stacking, Mixture of Experts)

Bagging (Logic, Practical Applications)

Random forest (Logic, Practical Applications)

Boosting (Logic, Practical Applications)

Ada Boost

Gradient Boosting Machines (GBM)

XGBoost

Supervised Learning :- Artificial Neural Network – ANN

Motivation for Neural Networks and Its Applications

Perceptron and Single Layer Neural Network, and Hand Calculations

Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques

Neural Networks for Regression

Neural Networks for Classification

Interpretation of Outputs and Fine tune the models with hyper parameters

Validating ANN models

Supervised Learning :- Support Vector Machines

Motivation for Support Vector Machine & Applications

Support Vector Regression

Support vector classifier (Linear & Non-Linear)

Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)

Interpretation of Outputs and Fine tune the models with hyper parameters

Validating SVM models

Supervised Learning :-KNN

What is KNN & Applications?

KNN for missing treatment

KNN For solving regression problems

KNN for solving classification problems

Validating KNN model

Model fine tuning with hyper parameters

Supervised Learning :- Naive Bayes

Concept of Conditional Probability

Bayes Theorem and Its Applications

Naïve Bayes for classification

Applications of Naïve Bayes in Classifications

Text Mining And Analytics

Taming big text, Unstructured vs. Semi-structured Data; Fundamentals of information retrieval, Properties of words; Creating Term-Document (TxD);Matrices; Similarity measures, Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)

Finding patterns in text: text mining, text as a graph

Natural Language processing (NLP)

Text Analytics – Sentiment Analysis using Python

Text Analytics – Word cloud analysis using Python

Text Analytics – Segmentation using K-Means/Hierarchical Clustering

Text Analytics – Classification (Spam/Not spam)

Applications of Social Media Analytics

Metrics(Measures Actions) in social media analytics

Examples & Actionable Insights using Social Media Analytics

Important python modules for Machine Learning (SciKit Learn, stats models, scipy, nltk etc)

Fine tuning the models using Hyper parameters, grid search, piping etc.

Pythn Statistics for Data Science Course Curriculum

Introduction to Data Science With R

What is analytics & Data Science?

Common Terms in Analytics

Analytics vs. Data warehousing, OLAP, MIS Reporting

Relevance in industry and need of the hour

Types of problems and business objectives in various industries

How leading companies are harnessing the power of analytics?

Critical success drivers

Overview of analytics tools & their popularity

Analytics Methodology & problem solving framework

List of steps in Analytics projects

Identify the most appropriate solution design for the given problem statement

Project plan for Analytics project & key milestones based on effort estimates

Build Resource plan for analytics project

Why R for data science?

Data Importing / Exporting

Introduction R/R-Studio – GUI

Concept of Packages – Useful Packages (Base & Other packages)

Data Structure & Data Types (Vectors, Matrices, factors, Data frames, and Lists)

Importing Data from various sources (txt, dlm, excel, sas7bdata, db, etc.)

Database Input (Connecting to database)

Exporting Data to various formats)

Viewing Data (Viewing partial data and full data)

Variable & Value Labels – Date Values

Data Manipulation

Data Manipulation steps

Creating New Variables (calculations & Binning)

Dummy variable creation

Applying transformations

Handling duplicates

Handling missings

Sorting and Filtering

Subsetting (Rows/Columns)

Appending (Row appending/column appending)

Merging/Joining (Left, right, inner, full, outer etc)

Data type conversions

Renaming

Formatting

Reshaping data

Sampling

Data manipulation tools

Operators

Functions

Packages

Control Structures (if, if else)

Loops (Conditional, iterative loops, apply functions)

Arrays

R Built-in Functions (Text, Numeric, Date, utility)

Numerical Functions

Text Functions

Date Functions

Utilities Functions

R User Defined Functions

R Packages for data manipulation (base, dplyr, plyr, data.table, reshape, car, sqldf, etc)

Data Analysis – Visualization

introduction exploratory data analysis

Descriptive statistics, Frequency Tables and summarization

Univariate Analysis (Distribution of data & Graphical Analysis)

Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)

Creating Graphs- Bar/pie/line chart/histogram/boxplot/scatter/density etc)

R Packages for Exploratory Data Analysis(dplyr, plyr, gmodes, car, vcd, Hmisc, psych, doby etc)

R Packages for Graphical Analysis (base, ggplot, lattice,etc)

Introduction To Statistics

Basic Statistics – Measures of Central Tendencies and Variance

Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem

Inferential Statistics -Sampling – Concept of Hypothesis Testing

Statistical Methods – Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square

Predictive Modelling

Concept of model in analytics and how it is used?

Common terminology used in analytics & modelling process

Popular modelling algorithms

Types of Business problems – Mapping of Techniques

Different Phases of Predictive Modelling

Data Exploration For Modeling

Accordion Content

Data Preparation

Need of Data preparation

Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values- Dummy creation – Variable Reduction

Variable Reduction Techniques – Factor & PCA Analysis

Segmentation: Solving Segmentation Problems

Introduction to Segmentation

Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)

Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)

Behavioral Segmentation Techniques (K-Means Cluster Analysis)

Cluster evaluation and profiling – Identify cluster characteristics

Interpretation of results – Implementation on new data

Linear Regression: Solving Regression Problems

Introduction – Applications

Assumptions of Linear Regression

Building Linear Regression Model

Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)

Assess the overall effectiveness of the model

Validation of Models (Re running Vs. Scoring)

Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)

Interpretation of Results – Business Validation – Implementation on new data

Logistic Regression: Solving Classification Problems

Introduction – Applications

Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models

Building Logistic Regression Model (Binary Logistic Model)

Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)

Validation of Logistic Regression Models (Re running Vs. Scoring)

Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)

Interpretation of Results – Business Validation – Implementation on new data

Time Series Forecasting: Solving Forecasting Problems

Introduction – Applications

Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition

Classification of Techniques(Pattern based – Pattern less)

Basic Techniques – Averages, Smoothening, etc

Advanced Techniques – AR Models, ARIMA, etc

Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc

Machine Learning -Predictive Modeling – Basics

Introduction to Machine Learning & Predictive Modeling

Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs. Forecasting

Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning

Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)

Overfitting (Bias-Variance Trade off) & Performance Metrics

Feature engineering & dimension reduction

Concept of optimization & cost function

Overview of gradient descent algorithm

Overview of Cross validation(Bootstrapping, K-Fold validation etc)

Model performance metrics (R-square, Adjusted R-squre, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )

Unsupervised Learning: Segmentation

What is segmentation & Role of ML in Segmentation?

Concept of Distance and related math background

K-Means Clustering

Expectation Maximization

Hierarchical Clustering

Spectral Clustering (DBSCAN)

Principle component Analysis (PCA)

Supervised Learning: Decision Trees

Decision Trees – Introduction – Applications

Types of Decision Tree Algorithms

Construction of Decision Trees through Simplified Examples; Choosing the “Best” attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees

Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness

Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules

Decision Trees – Validation

Overfitting – Best Practices to avoid

Supervised Learning: Ensemble Learning

Concept of Ensembling

Manual Ensembling Vs. Automated Ensembling

Methods of Ensembling (Stacking, Mixture of Experts)

Bagging (Logic, Practical Applications)

Random forest (Logic, Practical Applications)

Boosting (Logic, Practical Applications)

Ada Boost

Gradient Boosting Machines (GBM)

XGBoost

Supervised Learning: Artificial Neural Networks (ANN)

Motivation for Neural Networks and Its Applications

Perceptron and Single Layer Neural Network, and Hand Calculations

Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques

Neural Networks for Regression

Neural Networks for Classification

Interpretation of Outputs and Fine tune the models with hyper parameters

Validating ANN models

Supervised Learning: Support Vector Machines

Motivation for Support Vector Machine & Applications

Support Vector Regression

Support vector classifier (Linear & Non-Linear)

Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)

Interpretation of Outputs and Fine tune the models with hyper parameters

Validating SVM models

Supervised Learning: KNN

What is KNN & Applications?

KNN for missing treatment

KNN For solving regression problems

KNN for solving classification problems

Validating KNN model

Model fine tuning with hyper parameters

Supervised Learning: Naïve Bayes

Concept of Conditional Probability

Bayes Theorem and Its Applications

Naïve Bayes for classification

Applications of Naïve Bayes in Classifications

Text Mining & Analytics

Taming big text, Unstructured vs. Semi-structured Data; Fundamentals of information retrieval, Properties of words; Creating Term-Document (TxD);Matrices; Similarity measures, Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)

Finding patterns in text: text mining, text as a graph

Natural Language processing (NLP)

Text Analytics – Sentiment Analysis using R

Text Analytics – Word cloud analysis using R

Text Analytics – Segmentation using K-Means/Hierarchical Clustering

Text Analytics – Classification (Spam/Not spam)

Applications of Social Media Analytics

Metrics(Measures Actions) in social media analytics

Examples & Actionable Insights using Social Media Analytics

Important R packages for Machine Learning (caret, H2O, Randomforest, nnet, tm etc)

Fine tuning the models using Hyper parameters, grid search, piping etc.

Pythn Statistics for Data Science Course Curriculum

Introduction with Artificial Intelligence.

What is AI (Artificial Intelligence) ?

What types of intelligences we are talking about?

Different definitions and Ultimate goal of AI.

What are application areas for AI?

History of AI and some real life examples of AI.

ML and other related terms to AI.

What is ML and How it is related with AI?

What is NLP and How it is related with AI?

What is DL and How it is related with ML and AI?

What are ANNs and DNNs and How are they related to AI?

A working example of AI and ML.

Project 1 – These simple tasks are to make you understand how AI and ML can find their applications in real life.

Python libraries for ML.

What are Libraries, packages and Modules?

What are top Python libraries for ML in Python?

Setting up Anaconda development environment.

Why choosing Anaconda development environment?

Setting up Anaconda development environment on Windows 10 PC.

Verifying proper installation of Anaconda environment.

Getting into core development of ML

What is a classifier in ML?

Important elements and flow of any ML projects.

Let’s develop our first ML program – explanations

Let’s develop our first ML program – development

Project – 2

These simple tasks are going to give you some great experience with Machine Learning introductory programs or better say, “Hello world” programs of Machine Learning.

Different ML techniques.

What all ML techniques are there?

Evaluation methods of all ML techniques.

(IRIS flower project) Developing complete project of ML.

Developing complete ML project – understanding data set

Developing complete ML project – understanding flow of project

Developing complete ML project – visualizing data set through Python

Developing complete ML project – development

Developing complete ML project – concepts explanations

–(Digit recognition project) Developing another project of ML.

Project 3

After completing these project, you have done and understood multiple complete projects of Machine Learning.

Introduction of Ai with Deep Learning

Installation

CPU Software Requirements

CPU Installation of PyTorch

PyTorch with GPU on AWS

PyTorch with GPU on Linux

PyTorch with GPU on MacOSX

PyTorch Fundamentals: Matrices

Matrix Basics

Seed for Reproducibility

Torch to NumPy Bridge

NumPy to Torch Bridge

GPU and CPU Toggling

Basic Mathematical Tensor Operations

Summary of Matrices

PyTorch Fundamentals: Variables and Gradients

Variables

Gradients

Summary of Variables and Gradients

Linear Regression with PyTorch

- Linear Regression Introduction
- Linear Regression in PyTorch
- Linear Regression From CPU to GPU in PyTorch
- Summary of Linear Regression

Logistic Regression with PyTorch

Logistic Regression Introduction

Linear Regression Problems

Logistic Regression In-depth

Logistic Regression with PyTorch

Logistic Regression From CPU to GPU in PyTorch

Summary of Logistic Regression

Feedforward Neural Network with PyTorch

Logistic Regression Transition to Feedforward Neural Network

Non-linearity

Feedforward Neural Network in PyTorch

More Feedforward Neural Network Models in PyTorch

Feedforward Neural Network From CPU to GPU in PyTorch

Summary of Feedforward Neural Network

Convolutional Neural Network (CNN) with PyTorch

Feedforward Neural Network Transition to CNN

One Convolutional Layer, Input Depth of 1

One Convolutional Layer, Input Depth of 3

One Convolutional Layer Summary

Multiple Convolutional Layers Overview

Pooling Layers

Padding for Convolutional Layers

Output Size Calculation

CNN in PyTorch

More CNN Models in PyTorch

CNN Models Summary

Expanding Model’s Capacity

CNN From CPU to GPU in PyTorch

Summary of CNN

Recurrent Neural Networks (RNN)

Introduction to RNN

RNN in PyTorch

More RNN Models in PyTorch

RNN From CPU to GPU in PyTorch

Summary of RNN

Long Short-Term Memory Networks (LSTM)

- Introduction to LSTMs
- LSTM Equations
- LSTM in PyTorch
- More LSTM Models in PyTorch
- LSTM From CPU to GPU in PyTorch
- Summary of LSTM

Pythn Statistics for Data Science Course Curriculum

Introduction to Big Data Hadoop and Spark

**Learning Objectives:** Understand Big Data and its components such as HDFS. You will learn about the Hadoop Cluster Architecture and you will also get an introduction to Spark and you will get to know about the difference between batch processing and real-time processing.

**Topics:**

- What is Big Data?
- Big Data Customer Scenarios
- Limitations and Solutions of Existing Data Analytics Architecture with Uber Use Case
- How Hadoop Solves the Big Data Problem?
- What is Hadoop?
- Hadoop’s Key Characteristics
- Hadoop Ecosystem and HDFS
- Hadoop Core Components
- Rack Awareness and Block Replication
- YARN and its Advantage
- Hadoop Cluster and its Architecture
- Hadoop: Different Cluster Modes
- Big Data Analytics with Batch & Real-time Processing
- Why Spark is needed?
- What is Spark?
- How Spark differs from other frameworks?
- Spark at Yahoo!

Introduction to Scala for Apache Spark

**Learning Objectives:** Learn the basics of Scala that are required for programming Spark applications. You will also learn about the basic constructs of Scala such as variable types, control structures, collections such as Array, ArrayBuffer, Map, Lists, and many more.

**Topics:**

- What is Scala?
- Why Scala for Spark?
- Scala in other Frameworks
- Introduction to Scala REPL
- Basic Scala Operations
- Variable Types in Scala
- Control Structures in Scala
- Foreach loop, Functions and Procedures
- Collections in Scala- Array
- ArrayBuffer, Map, Tuples, Lists, and more

**Hands-on:**

- Scala REPL Detailed Demo

Functional Programming and OOPs Concepts in Scala

**Learning Objectives:** In this module, you will learn about object-oriented programming and functional programming techniques in Scala.

**Topics:**

- Functional Programming
- Higher Order Functions
- Anonymous Functions
- Class in Scala
- Getters and Setters
- Custom Getters and Setters
- Properties with only Getters
- Auxiliary Constructor and Primary Constructor
- Singletons
- Extending a Class
- Overriding Methods
- Traits as Interfaces and Layered Traits

**Hands-on:**

- OOPs Concepts
- Functional Programming

Deep Dive into Apache Spark Framework

**Learning Objectives:** Understand Apache Spark and learn how to develop Spark applications. At the end, you will learn how to perform data ingestion using Sqoop.

**Topics:**

- Spark’s Place in Hadoop Ecosystem
- Spark Components & its Architecture
- Spark Deployment Modes
- Introduction to Spark Shell
- Writing your first Spark Job Using SBT
- Submitting Spark Job
- Spark Web UI
- Data Ingestion using Sqoop

**Hands-on:**

- Building and Running Spark Application
- Spark Application Web UI
- Configuring Spark Properties
- Data ingestion using Sqoop

Playing with Spark RDDs

**Learning Objectives:**Get an insight of Spark – RDDs and other RDD related manipulations for implementing business logics (Transformations, Actions and Functions performed on RDD).**Topics:**- Challenges in Existing Computing Methods
- Probable Solution & How RDD Solves the Problem
- What is RDD, It’s Operations, Transformations & Actions
- Data Loading and Saving Through RDDs
- Key-Value Pair RDDs
- Other Pair RDDs, Two Pair RDDs
- RDD Lineage
- RDD Persistence
- WordCount Program Using RDD Concepts
- RDD Partitioning & How It Helps Achieve Parallelization
- Passing Functions to Spark

**Hands-on:**- Loading data in RDDs
- Saving data through RDDs
- RDD Transformations
- RDD Actions and Functions
- RDD Partitions
- WordCount through RDDs

Data Frames and Spark SQL

**Learning Objectives:**In this module, you will learn about SparkSQL which is used to process structured data with SQL queries, data-frames and datasets in Spark SQL along with different kind of SQL operations performed on the data-frames. You will also learn about the Spark and Hive integration.**Topics:**- Need for Spark SQL
- What is Spark SQL?
- Spark SQL Architecture
- SQL Context in Spark SQL
- User Defined Functions
- Data Frames & Datasets
- Interoperating with RDDs
- JSON and Parquet File Formats
- Loading Data through Different Sources
- Spark – Hive Integration

**Hands-on:**- Spark SQL – Creating Data Frames
- Loading and Transforming Data through Different Sources
- Stock Market Analysis
- Spark-Hive Integration

Machine Learning using Spark MLlib

**Learning Objectives:** Learn why machine learning is needed, different Machine Learning techniques/algorithms, and SparK MLlib.

**Topics:**

- Why Machine Learning?
- What is Machine Learning?
- Where Machine Learning is Used?
- Face Detection: USE CASE
- Different Types of Machine Learning Techniques
- Introduction to MLlib
- Features of MLlib and MLlib Tools
- Various ML algorithms supported by MLlib

Deep Dive into Spark MLlib

**Learning Objectives:** Implement various algorithms supported by MLlib such as Linear Regression, Decision Tree, Random Forest and many more.

**Topics:**

- Supervised Learning – Linear Regression, Logistic Regression, Decision Tree, Random Forest
- Unsupervised Learning – K-Means Clustering & How It Works with MLlib
- Analysis on US Election Data using MLlib (K-Means)

**Hands-on:**

- Machine Learning MLlib
- K- Means Clustering
- Linear Regression
- Logistic Regression
- Decision Tree
- Random Forest

Understanding Apache Kafka and Apache Flume

**Learning Objectives:** Understand Kafka and its Architecture. Also, learn about Kafka Cluster, how to configure different types of Kafka Cluster. Get introduced to Apache Flume, its architecture and how it is integrated with Apache Kafka for event processing. At the end, learn how to ingest streaming data using flume.

**Topics:**

- Need for Kafka
- What is Kafka?
- Core Concepts of Kafka
- Kafka Architecture
- Where is Kafka Used?
- Understanding the Components of Kafka Cluster
- Configuring Kafka Cluster
- Kafka Producer and Consumer Java API
- Need of Apache Flume
- What is Apache Flume?
- Basic Flume Architecture
- Flume Sources
- Flume Sinks
- Flume Channels
- Flume Configuration
- Integrating Apache Flume and Apache Kafka

**Hands-on:**

- Configuring Single Node Single Broker Cluster
- Configuring Single Node Multi Broker Cluster
- Producing and consuming messages
- Flume Commands
- Setting up Flume Agent
- Streaming Twitter Data into HDFS

Apache Spark Streaming - Processing Multiple Batches

**Learning Objectives:** Work on Spark streaming which is used to build scalable fault-tolerant streaming applications. Also, learn about DStreams and various Transformations performed on the streaming data. You will get to know about commonly used streaming operators such as Sliding Window Operators and Stateful Operators.

**Topics:**

- Drawbacks in Existing Computing Methods
- Why Streaming is Necessary?
- What is Spark Streaming?
- Spark Streaming Features
- Spark Streaming Workflow
- How Uber Uses Streaming Data
- Streaming Context & DStreams
- Transformations on DStreams
- Describe Windowed Operators and Why it is Useful
- Important Windowed Operators
- Slice, Window and ReduceByWindow Operators
- Stateful Operators

Apache Spark Streaming - Data Sources

**Learning Objectives:**In this module, you will learn about the different streaming data sources such as Kafka and flume. At the end of the module, you will be able to create a spark streaming application.**Topics:**- Apache Spark Streaming: Data Sources
- Streaming Data Source Overview
- Apache Flume and Apache Kafka Data Sources
- Example: Using a Kafka Direct Data Source
- Perform Twitter Sentimental Analysis Using Spark Streaming

**Hands-on:**- Different Streaming Data Sources

Tableau power user. The course is designed for the professional who has solid working experience with Tableau and wants to take it to the next level. You should have a deep understanding of all the fundamental concepts of building worksheets and dashboards, but may scratch your head when working with more complex issues.

Pythn Statistics for Data Science Course Curriculum

Learning Objectives: At the end of this class, the student will be able to:

- Build advanced chart types and visualizations
- Build complex calculations to manipulate your data
- Work with statistics and statistical techniques
- Work with parameters and input controls
- Implement advanced geographic mapping techniques and use custom images to build spatial visualizations of non-geographic data
- Implement all options in working with data: Joining multiple tables, data blending, performance considerations and working with the Data Engine, and understand when to implement which connection method.
- Build better dashboards using techniques for guided analytics, interactive dashboard design and visual best practices
- Implement many efficiency tips and tricks
- Understand the basics of Tableau Server and other options for sharing your results

Introduction and Getting Started

- Why Tableau? Why Visualization?
- The Tableau Product Line
- Level Setting – Terminology
- Getting Started – creating some powerful visualizations quickly
- Review of some Key Fundamental Concepts

Filtering, Sorting & Grouping

Filtering, Sorting and Grouping are fundamental concepts

when working with and analyzing data. We will briefly review these topics as they apply to Tableau

- Advanced options for filtering and hiding
- Understanding your many options for ordering and grouping your data: Sort, Groups, Bins, Sets
- Understanding how all of these options inter-relate

Working with Data

In the Advanced class, we will understand the difference between joining and blending data, and when we should do each. We will also consider the implications of working with large data sets, and consider options for when and how to work with extracts and the data engine. We will also investigate best practices in “sharing” data sources for Tableau Server users.

- Data Types and Roles
- Dimension versus Measures
- Data Types
- Discrete versus Continuous
- The meaning of pill colors
- Database Joins
- Data Blending
- Working with the Data Engine / Extracts and scheduling extract updates
- Working with Custom SQL
- Adding to Context
- Switching to Direct Connection

Working with Calculated Data and Statistics

In the Fundamentals Class, we were introduced to some basic calculations: basic string and arithmetic calculations and ratios and quick table calculations. In the Advanced class, we will extend those concepts to understand the intricacies of manipulating data within Tableau

A Quick Review of Basic Calculations

Arithmetic Calculations

String Manipulation

Date Calculations

Quick Table Calculations

Custom Aggregations

Custom Calculated Fields

Logic and Conditional Calculations

Conditional Filters

Advanced Table Calculations

Understanding Scope and Direction

Calculate on Results of Table Calculations

Complex Calculations

Difference From Average

Discrete Aggregations

Index to Ratios

Working with Parameters

In the Fundamentals class, we were introduced to parameters – How to create a parameter and use it in a calculation. In the Advanced class, we will go into more details on how we can use parameters to modify our title, create What-If analysis, etc

Parameter Basics

Data types of parameters

Using parameters in calculated fields

Inputting parameter values and parameter control options

Advanced Usage of Parameters

Using parameters for titles, field selections, logic statements, Top X

Building Advanced Chart Types and Visualizations / Tips & Tricks

This topic covers how to create some of the chart types and visualizations that may be less obvious in Tableau. It also covers some of the more common tips & tricks / techniques that we use to assist customers in solving some of their more complex problems.

- Bar in Bar
- Box Plot
- Bullet Chart
- Custom Shapes
- Gantt Chart
- Heat Map
- Pareto Chart
- Spark Line
- KPI Chart

Best Practices in Formatting and Visualizing

- Formatting Tips
- Drag to Legend
- Edit Legend
- Highlighting
- Labeling
- Legends
- Working with Nulls
- Table Options
- Annotations and Display Options
- Introduction to Visualization Best Practices

Introduction to XL Data Handling

- Introduction to Excel Environment
- Formatting and Conditional Formatting
- Data Sorting, Filtering and Data Validation
- Understanding Name Ranges

Data Manipulation Using Functions

- Descriptive functions: sum, count, min, max, average, counta, countblank
- Logical functions: IF, and, or, not
- Relational operators > >= < <= = !=
- Nesting of functions
- Date and Time functions: today, now, month, year, day, weekday, networkdays, weeknum, time, minute, hour
- Text functions: left, right, mid, find, length, replace, substitute, trim, rank, rank.avg, upper, lower, proper
- Array functions: sumif, sumifs, countif, countifs, sumproduct
- Use and application of lookup functions in excel: Vlookup, Hlookup
- Limitations of lookup functions
- Using Index, Match, Offset, concept of reverse vlookup

Data Analysis And Reporting

- Data Analysis using Pivot Tables – use of row and column shelf, values and filters
- Difference between data layering and cross tabulation, summary reports, advantages and limitations
- Change aggregation types and summarisation
- Creating groups and bins in pivot data
- Concept of calculated fields, usage and limitations
- Changing report layouts – Outline, compact and tabular forms
- Show and hide grand totals and subtotals
- Creating summary reports using pivot tables

Data Visualization In Excel

- Overview of chart types – column and bar charts, line and area charts, pie charts, doughnut charts, scatter plots
- How to select right chart for your data
- Chart formatting
- Creating and customizing advance charts – thermometer charts, waterfall charts, population pyramids

Overview Of Dashboards

- What is dashboard & Excel dashboard
- Adding icons and images to dashboards
- Making dashboards dynamic

Create Dashboards In Excel - Using Pivot Controls

- Concept of pivot cache and its use in creating interactive dashboards in Excel
- Pivot table design elements – concept of slicers and timelines
- Designing sample dashboard using Pivot Controls
- Design principles for including charts in dashboards – do’s and don’t’s

Business Dashboard Creation

- Complete Management Dashboard for Sales & Services
- Best practices – Tips and Tricks to enhance dashboard designing

SQL: Understanding RDBMS

- Schema – Meta Data – ER Diagram
- Looking at an example of Database design
- Data Integrity Constraints & types of Relationships (Primary and foreign key)
- Basic concepts – Queries, Data types & NULL Values, Operators and Comments in SQL

SQL: Utilising The Object Explorer

- What is SQL – A Quick Introduction
- Installing MS SQL Server for windows OS
- Introduction to SQL Server Management Studio
- Understanding basic database concepts

SQL: Data Based Objects Creation (DDL Commands)

- Creating, Modifying & Deleting Databases and Tables
- Drop & Truncate statements – Uses & Differences
- Alter Table & Alter Column statements
- Import and Export wizard to get the data in SQL server from excel files or delimited files

SQL: Data Manipulation (DML Commands)

- Insert, Update & Delete statements
- Select statement – Subsetting, Filters, Sorting. Removing Duplicates, grouping and aggregations etc
- Where, Group By, Order by & Having clauses
- SQL Functions – Number, Text, Date, etc
- SQL Keywords – Top, Distinct, Null, etc
- SQL Operators – Relational (single valued and multi valued), Logical (and, or, not), Use of wildcard operators and wildcard characters, etc

SQL: Accessing Data From Multiple Tables Using SELECT

- Append and JoinsUnion and Union All – Use & constraints
- Intersect and Except statements
- Table Joins – inner join, left join, right join, full join
- Cross joins/cartesian products, self joins, natural joins etc
- Inline views and sub-queries
- Optimizing your work

Tableau: Getting Started

- What is Tableau? What does the Tableau product suite comprise of? How Does Tableau Work?
- Tableau Architecture
- Connecting to Data & Introduction to data source concepts
- Understanding the Tableau workspace
- Dimensions and Measures
- Data Types & Default Properties
- Tour of Shelves & Marks Card
- Using Show Me
- Saving and Sharing your work-overview

Tableau: Data Handling & Summaries

- Date Aggregations and Date parts
- Cross tab & Tabular charts
- Totals & Subtotals
- Bar Charts & Stacked Bars
- Line Graphs with Date & Without Date
- Tree maps
- Scatter Plots
- Individual Axes, Blended Axes, Dual Axes & Combination chart
- Parts of Views
- Sorting
- Trend lines/ Forecasting
- Reference Lines
- Filters/Context filters
- Sets
- In/Out Sets
- Combined Sets

- Grouping
- Bins/Histograms
- Drilling up/down – drill through
- Hierarchies
- View data
- Actions (across sheets)

Tableau: Building Advanced Reports/ Maps

- Explain latitude and longitude
- Default location/Edit locations
- Building geographical maps
- Using Map layers

Tableau: Calculated Fields

- Working with aggregate versus disaggregate data
- Explain – #Number of Rows
- Basic Functions (String, Date, Numbers etc)
- Usage of Logical conditions

Tableau: Table Calculations

- Explain scope and direction
- Percent of Total, Running / Cumulative calculations
- Introduction to LOD (Level of Detail) Expressions
- User applications of Table calculations

Tableau: Parameters

- Using Parameters in
- Calculated fieldsBins
- Reference Lines
- Filters/Sets

- Display Options (Dynamic Dimension/Measure Selection)
- Create What-If/ Scenario analysis

Tableau: Building Interactive Dashboards

- Combining multiple visualizations into a dashboard (overview)
- Making your worksheet interactive by using actions
- Filter
- URL
- Highlight

- Complete Interactive Dashboard for Sales & Services

Tableau: Formatting

- Options in Formatting your Visualization
- Working with Labels and Annotations
- Effective Use of Titles and Captions

Tableau: Working With Data

- Multiple Table Joins
- Data Blending
- Difference between joining and blending data, and when we should do each
- Toggle between to Direct Connection and Extracts

MS VBA

**Introducing VBA**- What is Logic?
- What Is VBA?
- Introduction to Macro Recordings, IDE

**How VBA Works with Excel**- Working In the Visual Basic Editor
- Introducing the Excel Object Model
- Using the Excel Macro Recorder
- VBA Sub and Function Procedures

**Key Components of Programming language**- Essential VBA Language Elements
- Keywords & Syntax
- Programming statements
- Variables & Data types
- Comments
- Operators
- Working with Range Objects

**A look at some commonly used code snippets**

**Programming constructs in VBA**- Control Structures
- Looping Structures
- The With- End with Block

**Functions & Procedures in VBA – Modularizing your programs**- Worksheet & workbook functions
- Automatic Procedures and Events
- Arrays

**Objects & Memory Management in VBA**- The NEW and SET Key words
- Destroying Objects – The Nothing Keyword

**Error Handling**

**Controlling accessibility of your code – Access specifiers**

**Code Reusability – Adding references and components to your code**

**Communicating with Your Users**- Simple Dialog Boxes
- User Form Basics
- Using User Form Controls
- Add-ins
- Accessing Your Macros through the User Interface
- Retrieve information through Excel from Access Database using VBA

I had a wonderful experience in Radical technologies where i did training in Hadoop development under the guidance of Shanit Sir. He started from the very basic and covered and shared everything he knew in this field. He was brilliant and had a lot of experience in this field. We did hands on for every topic we covered, and that’s the most important thing because honestly theoretical knowledge cannot land you a job.

Rohit Agrawal
Hadoop
I have recently completed Linux course under Anand Sir and can assuredly say that it is definitely the best Linux course in Pune.
Since most of the Linux courses from other sources are strictly focused on clearing the certification, they will not provide an insight into real-world server administration, but that is not the case with Anand Sir’s course. Anand Sir being an experienced IT infrastructure professional has an excellent understanding of how a data center works and all these information is seamlessly integrated into his classes.

Manu Sunil
Linux
I had undergone oracle DBA course under Chetan sir’s Guidance an it was a very good learning experience overall since they not only provide us with theoretical knowledge but also conduct lot of practical sessions which are really fruitful and also the way of teaching is very fine clear and crisp which is easier to understand , overall I had a great time for around 2 months , they really train you well.also make it a point to clear all your doubts and provide you with clear and in-depth concepts hence hope to join sometime again

Reema banerjee
Oracle DBA
I have completed Oracle DBA 11g from Radical technology pune. Excellent trainer (chetna gupta ). The trainer kept the energy level up and kept us interested throughout. Very practical, hands on experience. Gave us real-time examples, excellent tips and hints. It was a great experience with Radical technologies.

Mrudul Bhokare
Oracle DBA
Linux learning with Anand sir is truly different experience… I don’t have any idea about Linux and system but Anand sir taught with scratch…He has a great knowledge and the best trainer…he can solve all your queries related to Linux in very simple way and giving nice examples… 100 🌟 to Anand Sir.

Harsh Singh Parihar
Linux
Prev

Next

- Non-biased career guidance
- Counselling based on your skills and preference
- No repetitive calls, only as per convenience
- Rigorous curriculum designed by industry experts
- Complete this program while you work

Creative

0%

Innovative

0%

Student Friendly

0%

Practical Oriented

0%

Valued Certification

0%