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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

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- 30+ Programming Tools & Languages
- 30+ Industry Projects , 100 + Live assignments , 150+ coding Solutions.
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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.

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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