Masters Program – Data Science and Data Analytics

5918 Ratings
5/5
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

Avg. salary of a Data Scientists is goes to 20 Lakhs per annum

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

Overview of tools covered

Machine Learning, K-Means Clustering, Decision Trees, Data Mining, Python Libraries, Statistics, Scala, Spark Streaming, RDDs, MLlib, Regression Modelling , Scala Programming language, HDFS, Sqoop, FLume, Spark GraphX, Apache Cassandra, Spark, and Kafka

0 +
Students Empowered

Online / Classroom

Format

12 months

Recommended 12-15 hrs/week

0 +
Hiring Partners

Every Month 1st

Start Date

Certified By

OTHM–UK Recognised by ofqual.gov.uk

About the Program

Project Driven industry mentorship, dedicated career support, learn 30 + programming tools . Expertise in Software languages with multiple assignments .Dedicated Trainers with ample of Industry Experience . Project based IT Training and Certification programs . Certified from register.ofqual.gov.uk . Provide Level 7 Certification , which is equal to Master’s program in the rest of the world . Word Wide recognised certification from UK.

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

Program Overview

Key Highlights

  • 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

Top Skills You Will Learn

Predictive Analytics using Python, Machine Learning, Data Visualization, Big Data, Natural Language Processing

Job Opportunities

Data Analyst, Data Scientist, Data Engineer, Product Analyst, Machine Learning Engineer, Decision Scientist

Who Is This Program For?

Engineers, Marketing & Sales Professionals, Freshers, Domain Experts, Software & IT Professionals , Those who having career gap , those who do not completed Degree .

Minimum Eligibility

Any Bachelor’s degree. Completed or not completed . No coding experience required . If you have any Educational Gap or any other career gap , you can do this program to boost up your career . The qualifications provided by UK Regulatory board is equal to Level 7 Masters Degree .

Programming Languages and Tools Covered .

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INTERNATIONAL POSTGRADUATE DIPLOMA FROM othm

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INTERNATIONAL POSTGRADUATE DIPLOMA FROM othm

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How You Benefit From This Program

  • 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

COURSE RELATED FAQs

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

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

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 

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.

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 .

Our Process

Training
Work On Industry
Scenarios
(60+ )
Do Major Projects
( 8+ )
Google Data Engineer Certification Preparation
Register into Radical HR Portal - Start Interview
Complete Live
Assignments
( 150+ )
Do Mini Projects
( 25 + )
Give Mock Interviews
Profile Creation

Course Curriculum

Python Statistics for Data Science Course Classroom / Online​

Python Scripting allows programmers to build applications easily and rapidly. This course is an introduction to Python scripting, which focuses on the concepts of Python, it will help you to perform operations on variable types using Pycharm. You will learn the importance of Python in real time environment and will be able to develop applications based on Object Oriented Programming concept. End of this course, you will be able to develop networking applications with suitable GUI

Statistics For Data Science – Using Python

Python Scripting allows programmers to build applications easily and rapidly. This course is an introduction to Python scripting, which focuses on the concepts of Python, it will help you to perform operations on variable types using Pycharm. You will learn the importance of Python in real time environment and will be able to develop applications based on Object Oriented Programming concept. End of this course, you will be able to develop networking applications with suitable GUI

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

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

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

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

  • 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
    in python
  • 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

Statistics for Data Science – Using R

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

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

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

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

  • 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 Techniques

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

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

  • 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 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 R
    Analyze the residuals using R
    Calculation of WOE values using R

DATASCIENCE & MACHINE LEARNING 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

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

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

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)

  • 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)

  • 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

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

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

Need of Data preparation
Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values- Dummy creation – Variable Reduction
Variable Reduction Techniques – Factor & PCA Analysis

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

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

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

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

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 )

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)

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

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

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

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

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

Concept of Conditional Probability
Bayes Theorem and Its Applications
Naïve Bayes for classification
Applications of Naïve Bayes in Classifications

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.

DATASCIENCE 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?

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 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)

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)

  • 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

  • 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

Accordion Content

 Need of Data preparation
Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values- Dummy creation – Variable Reduction
Variable Reduction Techniques – Factor & PCA Analysis

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

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

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

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

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 )

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)

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

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

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

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

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

Concept of Conditional Probability
Bayes Theorem and Its Applications
Naïve Bayes for classification
Applications of Naïve Bayes in Classifications

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.

AI With ML & DL

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.

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?

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

What are Libraries, packages and Modules?
What are top Python libraries for ML in Python?

  • Why choosing Anaconda development environment?
    Setting up Anaconda development environment on Windows 10 PC.
    Verifying proper installation of Anaconda environment.

  • 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.

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.

Installation
CPU Software Requirements
CPU Installation of PyTorch
PyTorch with GPU on AWS
PyTorch with GPU on Linux
PyTorch with GPU on MacOSX

Matrix Basics
Seed for Reproducibility
Torch to NumPy Bridge
NumPy to Torch Bridge
GPU and CPU Toggling
Basic Mathematical Tensor Operations
Summary of Matrices

Variables
Gradients
Summary of Variables and Gradients

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

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

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

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

Introduction to RNN
RNN in PyTorch
More RNN Models in PyTorch
RNN From CPU to GPU in PyTorch
Summary of RNN

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

Apache Spark and Scala

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!

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

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

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
  • 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
  • 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

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

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

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

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
  • 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

Data Visualization And Analytics - Tableau

This course is designed to provide you with the skills required to become a
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.

  • 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
  • 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 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

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

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

Arithmetic Calculations

String Manipulation

Date Calculations

Quick Table Calculations

Custom Aggregations

Custom Calculated Fields

Logic and Conditional Calculations

 Conditional Filters

Understanding Scope and Direction

Calculate on Results of Table Calculations

Complex Calculations

Difference From Average

Discrete Aggregations

Index to Ratios

 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

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
  • 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 Excel Environment
  • Formatting and Conditional Formatting
  • Data Sorting, Filtering and Data Validation
  • Understanding Name Ranges
  • 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 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
  • 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
  • What is dashboard & Excel dashboard
  • Adding icons and images to dashboards
  • Making dashboards dynamic
  • 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
  • Complete Management Dashboard for Sales & Services
  • Best practices – Tips and Tricks to enhance dashboard designing
  • 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
  • What is SQL – A Quick Introduction
  • Installing MS SQL Server for windows OS
  • Introduction to SQL Server Management Studio
  • Understanding basic database concepts
  • 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
  • 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
  • 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
  • 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
  • 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)
  • Explain latitude and longitude
  • Default location/Edit locations
  • Building geographical maps
  • Using Map layers
  • Working with aggregate versus disaggregate data
  • Explain – #Number of Rows
  • Basic Functions (String, Date, Numbers etc)
  • Usage of Logical conditions
  • Explain scope and direction
  • Percent of Total, Running / Cumulative calculations
  • Introduction to LOD (Level of Detail) Expressions
  • User applications of Table calculations
  • Using Parameters in
    • Calculated fieldsBins
    • Reference Lines
    • Filters/Sets
  • Display Options (Dynamic Dimension/Measure Selection)
  • Create What-If/ Scenario analysis
  • Combining multiple visualizations into a dashboard (overview)
  • Making your worksheet interactive by using actions
    • Filter
    • URL
    • Highlight
  • Complete Interactive Dashboard for Sales & Services
  • Options in Formatting your Visualization
  • Working with Labels and Annotations
  • Effective Use of Titles and Captions
  • Multiple Table Joins
  • Data Blending
  • Difference between joining and blending data, and when we should do each
  • Toggle between to Direct Connection and Extracts
  • 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

Course Reviews

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
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