DATASCIENCE WITH R TRAINING IN KOCHI

2546 Ratings
5/5

1880 Learners

Data Science R course effectively covers Data analytics, statistical predictive modelling and machine learning through various practical examples and projects This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median mode etc. and eventually covers all aspects of an analytics (or) data science career from analysing and preparing raw data to visualizing your findings. If you’re a programmer or a fresh graduate looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic to Advance techniques used by real-world industry data scientists.

Overview

Data Science R course effectively covers Data analytics, statistical predictive modelling and machine learning through various practical examples and projects This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median mode etc. and eventually covers all aspects of an analytics (or) data science career from analysing and preparing raw data to visualizing your findings. If you’re a programmer or a fresh graduate looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic to Advance techniques used by real-world industry data scientists.

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INTRO VIDEO

Why Radical Technologies

  • Highly practical oriented training
  • 25000+ Man-hours of Real-time projects & scenarios
  • 10 to 20+ year Experienced corporate trainers With Real Time Experience.
  • Building up professionals by highly experienced professionals
  • 100 % quality assurance in training .
  • 10000+ Placement Records and 180+ MNC’s and Consultancies Tie up

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Target audience?

Candidates from various quantitative backgrounds, like Engineering, Finance, Maths, Statistics, Business Management who want R training with detailed focus on Data Science and Machine Learning applications.

Engineering/Management Graduate or Post-graduate Fresher Students who want to make their career in the Data Science Industry or want to be future Data Scientists.
Engineers who want to use a distributed computing engine for batch or stream processing or both
Analysts who want to leverage Spark for analyzing interesting datasets
Data Scientists who want a single engine for analyzing and modelling data
MBA Graduates or business professionals who are looking to move to a heavily quantitative role.
Engineering Graduate/Professionals who want to understand basic statistics and lay a foundation for a career in Data Science
Working Professional or Fresh Graduate who have mostly worked in Descriptive analytics or not work anywhere and want to make the shift to being data scientists
Professionals who’ve worked mostly with tools like Excel and want to learn how to use R for statistical analysis.

Course Curriculum

Course description

This course Start with introduction to Data Science and Statistics using R Language. It covers both the aspects of Statistical concepts and the practical implementation using R Language. If you’re new to Programming, don’t worry – the course starts with a crash course to teach you all basic programming concepts. If you’ve done some programming before or you are new in Programming, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC’s; the sample code will also run on MacOS or Linux desktop systems.

Pre-requisites

No Pre-requisites

Course Content

DataScience with R Course Content

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)

ntroduction 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

Data Preparation

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.

Case Studies

Training Options

Live Online Training

  • Highly practical oriented training
  • Installation of Software On your System
  • 24/7 Email and Phone Support
  • 100% Placement Assistance until you get placed
  • Global Certification Preparation
  • Trainer Student Interactive Portal
  • Assignments and Projects Guided by Mentors
  • And Many More Features

Course completion certificate and Global Certifications are part of our all Master Program

Live Classroom Training

  • Weekend / Weekdays / Morning / Evening Batches
  • 80:20 Practical and Theory Ratio
  • Real-life Case Studies
  • Easy Coverup if you missed any sessions
  • PSI | Kryterion | Redhat Test Centers
  • Life Time Video Classroom Access ( coming soon )
  • Resume Preparations and Mock Interviews
  • And Many More Features

Course completion certificate and Global Certifications are part of our all Master Program

Exam & Certification

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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