Project Description


About Data Science

Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data.It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning, classification, cluster analysis, data mining, databases, and visualization.

Either it is structured or unstructured data, Data Science is a field that encompasses anything related to data cleansing, preparation, and analysis.

Job Responsibilities of Data Science

  • Collecting large amounts of unruly data and transforming it into a more usable format.
  • Solving business-related problems using data-driven techniques.
  • Working with a variety of programming languages, including SAS, R and Python.
  • Having a solid grasp of statistics, including statistical tests and distributions.
  • Staying on top of analytical techniques such as machine learning, deep learning and text analytics.
  • Communicating and collaborating with both IT and business.
  • Looking for order and patterns in data, as well as spotting trends that can help a business’s bottom line


Data visualization:

The presentation of data in a pictorial or graphical format so it can be easily analyzed.

Machine learning

A branch of artificial intelligence based on mathematical algorithms and automation.

Deep learning

An area of machine learning research that uses data to model complex abstractions.

Pattern recognition

Technology that recognizes patterns in data (often used interchangeably with machine learning)

Data preparation

The process of converting raw data into another format so it can be more easily consumed.

Text analytics

The process of examining unstructured data to glean key business insights.

Database Querying Language

SQL, or Structured Query Language, is a special-purpose programming language for managing data held in relational database management systems. Almost all structured data is stored in such databases, so, if you want to play with data, chances are you’ll want to know some SQL

Database management systems
  • Hadoop
  • MongoDB
  • SQL Server
  • Oracle
  • MySQL
Statistical Programming Languages:
  • Python
  • Java
  • C++
  • PERL
  • Ruby
  • C#
Statistical Analysis Tools
  • R Tool
  • SAS
  • Matlab
  • SPSS
  • STtata
  • Minitab

DataScience Course

This data science course will provide you a strong foundation to understand Machine Learning Algorithms like Clustering, Random Forest, Decision Trees, Naive Bayes using R and Concepts of Statistics, Time Series, Text Mining.

At the end of this Data Science training, you should be prepared to take up an exciting job opportunity in the field of Data Science

On successful completion of the course, candidates will be able to:

  • Analyze Big Data using R, Hadoop and Machine Learning.
  • Understand the responsibilities of a Data Scientist
  • Understand the use of machine learning algorithms in R
  • Learn about the processes involved in the Data Analysis Life Cycle
  • Learn how to use data formats including XML, CSV and SAS, SPSS
  • Transform data using best practices and tools
  • Learn to implement various Data Mining techniques
  • Analyse data using Hadoop Mappers and Reducers
  • Follow best practices in data visualization and optimization techniques

Following Professionals are recommended for Data Science Training.

  • Systems Analysts and programmers interested in expanding their role as a Data Scientist
  • ‘R’ professionals who want to captivate and analyze Big Data
  • Hadoop Professionals who want to learn R and ML techniques
  • Entry-level Data Analysts wanting to understand Data Science methodologies
  • Hadoop Professionals who want to learn R and ML techniques
  • Non-IT professionals aspiring to get into Data Analytics.
  • SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
  • Business and data analysts looking to add big data analytics skills, & to understand Machine Learning (ML) Techniques
  • Managers of business intelligence, analytics, or big data groups
  • College graduates considering data science as a career field
  • Information Architects who want to gain expertise in Predictive Analytics

Data Science is a booming demand for skill across industries, which is suited for all individuals at all levels of experience.


Numaware Trainings provide completely practical and real time DataScience Training starts from basics to advanced modules. Get an introduction to the fundamentals of DataScience and gain proficiency in identifying terminologies and concepts in the DataScience environment

Give Miss Call to +91-9916-566-300 for further more details on DataScience Training

Detailed Course Content

Module 1 -Introduction to Business Analytics

Learning Objectives – This module tells you what Business Analytics is and how R can play an important role in solving complex analytical problems. It tells you what is R and how it is used by the giants like Google, Facebook, Bank of America, etc.

  • Understand Business Analytics and R
  • Knowledge on the R language
  • Community and ecosystem
  • Understand the use of ‘R’ in the industry
  • Compare R with other software in analytics
  • Install R and the packages useful for the course
  • Perform basic operations in R using command line
  • Learn the use of IDE R Studio and Various GUI
  • Use the ‘R help’ feature in R
  • Knowledge about the worldwide R community collaboration

Module 2 -Introduction to R Programming

Learning Objectives –This module starts from the very basics of R programming like datatypes and functions. We present a scenario and let you think about the options to resolve it. E.g. which datatype would you use to store the variable or which R function can help you in this scenario.

  • The various kinds of data types in R and its appropriate uses
  • The built-in functions in R like: seq(), cbind (), rbind(), merge()
  • Knowledge on the various Subsetting methods
  • Summarize data by using functions like: str(), class(), length(), nrow(), ncol()
  • Use of functions like head(), tail(), for inspecting data
  • Indulge in a class activity to summarize data

Module 3 -Data Manipulation in R

Learning Objectives – In this module, we start with a sample of a dirty data set and perform Data Cleaning on it, resulting in a data set, which is ready for any analysis. Thus using and exploring the popular functions required to clean data in R.

  • The various steps involved in Data Cleaning
  • Functions used in Data Inspection
  • Tackling the problems faced during Data Cleaning
  • Uses of the functions like grepl(), grep(), sub()
  • Coerce the data
  • Uses of the apply() functions

Module 4 -Data Import Techniques in R

Learning Objectives – This module tells you about the versatility and robustness of R which can take-up data in a variety of formats, be it from a csv file to the data scraped from a website. This module teaches you various data importing techniques in R.

  • Import data from spreadsheets and text files into R
  • Import data from other statistical formats like sas7bdat and spss
  • Packages installation used for database import
  • Connect to RDBMS from R using ODBC and basic SQL queries in R
  • Basics of Web Scraping

Module 5 -Exploratory Data Analysis

Learning Objectives – In this module, you will learn that exploratory data analysis is an important step in the analysis. EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis. You will also learn about the various tasks involved in a typical EDA process.

  • Understanding the Exploratory Data Analysis(EDA)
  • Implementation of EDA on various datasets
  • Boxplots ? Understanding the cor() in R
  • EDA functions like summarize(), llist()
  • Multiple packages in R for data analysis
  • The Fancy plots like Segment plot
  • HC plot in R

Module 6 -Data Visualization in R

Learning Objectives – In this module, you will learn that visualization is the USP of R. You will learn the concepts of creating simple as well as complex visualizations in R.

  • Understanding on Data Visualization
  • Graphical functions present in R
  • Plot various graphs like tableplot, histogram, boxplot
  • Customizing Graphical Parameters to improvise the plots
  • Understanding GUIs like Deducer and R Commander
  • Introduction to Spatial Analysis.

Module 7 -Data Mining: clustering techniques

Learning Objectives – This module lets you know about the various Machine Learning algorithms. The two Machine Learning types are Supervised Learning and Unsupervised Learning and the difference between the two types. We will also discuss ‘K-means Clustering’ and implement it in this module.

  • Introduction to Data Mining
  • Understanding Machine Learning
  • Supervised and Unsupervised Machine Learning Algorithms
  • K-means Clustering

Module 8 -Data Mining: Association Rule Mining and Sentiment Analysis

Learning Objectives – This module discusses the very popular ‘Association Rule Mining’ Technique. The algorithm and various aspects of the same have been discussed in this module. We will also discuss what ‘Sentiment Analysis’ is and how we can fetch, extract and mine live data from twitter to find out the sentiment of the tweets.

  • Association Rule Mining
  • Sentiment Analysis

Module 9 -Linear and Logistic Regression

Learning Objectives – This module touches the base with the ‘Regression Techniques’. Linear and logistic regression is explained from the very basics with the examples and it is implemented in R using two case studies dedicated to each type of Regression discussed.

  • Linear Regression
  • Logistic Regression

Module 10 -Anova and Predictive Analysis

Learning Objectives – This module tells you about the Analysis of Variance (Anova) Technique. Another topic that is discussed in this module is Predictive Analysis.

  • Anova
  • Predictive Analysis

Module 11 -Data Mining: Decision Trees and Random Forest

Learning Objectives – This module covers the concepts of Decision Trees and Random Forest. The Algorithm for creation of trees and forests is discussed in a step wise approach and explained with examples. At the end of the class, these are the concepts implemented on a real-life data set. The case studies are present in the LMS.

  • Decision Trees
  • Algorithm for creating Decision Trees
  • Greedy Approach: Entropy and Information Gain
  • Creating a Perfect Decision Tree
  • Classification Rules for Decision Trees
  • Concepts of Random Forest
  • Working of Random Forest
  • Features of Random Forest

Module 12 -Project

Learning Objectives – T This module discusses the concepts taught throughout the course and their implementation in a Project.

  • Analyze Census Data to predict insights on the income of the people based on the factors like: Age, education, work-class, occupation, etc


Module 1  -Introduction to Data Science

Learning Objectives – This module will give you an understanding of Big Data and the Roles and Responsibilities of a Data Scientist. You will learn how Hadoop and R are used in Big Data Analytics and what are the methodologies used in the Analysis. This module will cover common Big Data as well as non-Big Data problems and available methods in Data Science to solve these problems. We will also solve few real-life data sets a Data Scientist encounter in his day to day work using R Hadoop and Mahout.

  • Introduction to Big Data
  • Roles played by a Data Scientist
  • Analysing Big Data using Hadoop and R
  • Methodologies used for analysis
  • The Architecture and Methodologies used to solve the Big Data problems, For example
  • Data Acquisition from various sources, Data preparation, Data transformation using Map Reduce (RMR)
  • Application of Machine Learning Techniques
  • Data Visualization etc.
  • Problem statement of few data science problems, which we shall solve during the course.

Module 2  -Basic Data Manipulation using R

Learning Objectives – In this module, you will learn the various data manipulation techniques using Fl.

  • Understanding vectors in R
  • Reading Data
  • Combining Data
  • Sub setting data
  • Sorting data and some basic data generation functions.

Module 3-Machine Learning Techniques Using R Part-1

Learning Objectives – In this module, you will get an overview of the Machine learning Algorithms, and Supervised and Unsupervised Learning Techniques.

  • Machine Learning Overview
  • ML Common Use Cases
  • Understanding Supervised and Unsupervised Learning Techniques
  • Clustering, Similarity Metrics, Distance Measure
  • Types: Euclidean, Cosine Measures, Creating predictive models.

Module 4 -Machine Learning Techniques Using R Part-2

Learning Objectives – In this module, you will learn Unsupervised Machine Learning Techniques and the implementation of different algorithms, for example, K-Means Clustering, TF-IDF and Cosine Similarity.

  • Understanding K-Means Clustering
  • Understanding TF-IDF and Cosine Similarity and their application to Vector Space Model
  • Implementing Association rule mining in R

Module 5

Machine Learning Techniques Using R Part-3

Learning Objectives –In this module, you will learn the Supervised Learning Techniques and the implementation of various Techniques, for example, Decision Trees, Random Forest Classifier etc.

  • Understanding Process flow of Supervised Learning Techniques Decision Tree Classifier
  • How to build Decision trees
  • Random Forest Classifier
  • What is Random Forests
  • Features of Random Forest
  • Out of Box Error Estimate and Variable Importance
  • Naive Bayes Classifier.

Module 6 -Introduction to Hadoop Architecture

Learning Objectives –In this module, you will learn the HDFS Architecture, MapReduce Paradigm and few data acquisition techniques in Hadoop.

  • Hadoop Architecture, Common Hadoop commands
  • MapReduce and Data loading techniques (Directly in R and in Hadoop using
  • SQOOP, FLUME, and other Data Loading Techniques)
  • Removing anomalies from the dat

Module 7 -Integrating R with Hadoop

Learning Objectives – In this module, you will learn the methods to integrate two popular open source software for Big Data analytics: R and Hadoop. You will also learn techniques to write your own Mappers and Reducers.

  • Integrating R with Hadoop using R Hadoop and RMR package
  • Exploring RHIPE (R Hadoop Integrated Programming Environment)
  • Writing MapReduce Jobs in R and executing them on Hadoop.

Module 8 -Mahout Introduction And Algorithm Implementation

Learning Objectives – In this module, you will understand Apache Mahout Machine Learning Library and will also gain an insight into the methods to achieve Parallel Processing using Algorithms in Mahout.

  • Implementing Machine Learning Algorithms on larger Data Sets with Apache

Module 9 -Additional Mahout Algorithms and Parallel Processing using R

Learning Objectives – In this module, you will learn how to implement Random Forest Classifier with Parallel Processing Library in R

  • Implementation of different Mahout algorithms
  • Random Forest Classifier with parallel processing Library in R

Module 10 -Project discussion

Learning Objectives – In this module, you will learn various approaches to solve a Data Science problem and how different technologies and Tools (R Hadoop, and Mahout) work together in a typical Data Science Project.

  • Project Discussion
  • Problem Statement and Analysis
  • Various approaches to solve a Data Science Problem
  • Pros and Cons of different approaches and algorithms.


Role : Data Science Solution lead
Experience : 15+ Yrs of IT Experience across MNC Companies
Technologies : Data Science, SAS, R , Python, Machine Learning, Advanced Analytics, Bigdata..etc.
About Trainer :

Data scientist solution lead with a demonstrated history in leading development of data science solutions and nearly 15 years of experience in Solution Architecture, Business Analysis, Data Analysis, Software Design, Analysis & Development. Skilled in Machine Learning statistical data analysis, predictive modeling, neural networks and text mining. Plays key role in Analytics team to help the business in improving customer satisfaction and reduce operation costs through data driven techniques.

Certifications : SAS Certified Data Scientist
Certified R Programmer from Advancer

Role : Senior Technical Analyst
Experience : 9+ Yrs of IT Experience across MNC Companies
Technologies : Data Analytics, Data Science, R, SQL, Hive SharePoint Server, Azure Machine Learning
About Trainer : Data Scientist with 8 years of hands on experience in Machine Learning and data analytics tools SAS, R, MySQL, Python, Hadoop and  Tableau. Currently working with various data owners and data stewards from Finance, Healthcare Quality, Customer Quality, Markets and IT to understand and drive delivery of data management objectives. Training on Various intermediate and advanced levels topics in data science analytics using R for corporate Companies and Clients. Passion about data analytics and solving the complex issues in business using advanced data analytics.
Certifications : MCSA Machine Learning
MCTS- Microsoft Office SharePoint Server 2007 Application Development
Microsoft Office Specialist – Excel 2010


We support! You Certify

Numaware Technologies provides certification trainings and also support you with getting certified in desired skill sets.

Imp Note: There are no universally required or accepted certifications in the world of data science and/or analytics. 

Data Science is a combination of technical skill and Soft Skill to turn data in to actionable sight. Data science is a “concept to unify statistics, data analysis and their related methods” in order to “understand and analyze actual phenomena” with data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning, classification, cluster analysis, data mining, databases, and visualization. The best way to become a data scientist or analyst is to gain the requisite skills and develop a history of showing how you added value with those skills.

Languages or skills like SQL, Python, R, SAS are elementary to become a data analyst or data scientist and below are the few some data science certifications that are widely recognized by industry:

Cloudera Certified Professional Data Scientist

The CCP Data Scientist is geared toward data scientists who can design and develop scalable and robust solutions for production environments. Candidates need to pass three exams: Descriptive and Inferential Statistics on Big Data, Advanced Analytical Techniques on Big Data, and Machine Learning at Scale. Each exam is a challenge scenario, and you are given eight hours to complete it. All three exams must be taken within 365 days of each other.,

CCP certifications are valid for three years.

For more info, click on following link

SAS Certified Data Scientist

The following list of exams are srerequisite to complete the SAS Certified Data Scientist.

  • SAS Certified Big Data Professional
  • SAS Certified Advanced Analytics Professional

Candidates for the Data Scientist certification should have deep knowledge of and skills in manipulating big data using SAS and open source tools, using complex machine learning models, making business recommendations, and deploying models. Candidates must pass five exams to earn the SAS Certified Data Scientist credential. The data science certification program comprises the focus areas of both the SAS Certified Big Data Professional and the SAS Certified Advanced Analytics Professional programs, including:

SAS “versioned” Certificates, such as the SAS Certified Data Scientist Using SAS 9, do not expire.

Dell EMC Data Science Associate

The Dell EMC Data Scientist Associate (EMCDSA) is a foundational certification that exposes you to the basics of big data and data analytics. Topics for this certification include an introduction to data analytics, characteristics of big data and the role of data scientists. Also covered are a variety of big data theories and methods, including linear regression, time-series analysis and decision trees.

This exam focuses on the practice of data analytics, the role of the Data Scientist, the main phases of the Data Analytics Lifecycle, analyzing and exploring data with R, statistics for model building and evaluation, the theory and methods of advanced analytics and statistical modeling, the technology and tools that can be used for advanced analytics, operationalizing an analytics project, and data visualization techniques. Successful candidates will achieve the EMC Proven Professional – Data Science Associate credential.

Trainings and Batches

Mode of Training

Numaware provides the following list of trainings according to Trainee or Colleges or Organization preference

  • Classroom Training
  • Online Training
  • Corporate Training
  • Campus Training
  • University Training
  • Virtual Instructor-Led Training
  • Instructor-led Live Classroom Training

Batches Available

We are Flexible with following list of batches as per the student requirements and availability.

  • Regular Batch
  • Weekend Batch
  • Weekday Batch
  • Fast-Track Batch
  • One to One Batch
  • Customized Batch

Flexible Timings

Numaware providing Flexible timings to schedule the Batches according to student-preferred timings at either Morning or Evening

  • Morning : 6.00 AM to 12.00PM
  • Evening : 3.00 PM to 10.00PM

Affordable Fees

We Charge very nominal, least and best price for all trainings when compared to Market or any other institutes with good quality standards and no compromise on commitment of providing Quality of Training.

Digital and Flexible Payment Options are available with Numaware Technologies Pvt. Ltd

  • Cash with Invoice
  • Credit-Card Pay
  • Debit-Card Pay
  • Any Digital-Pay
  • Account Transfer
  • Pay-Tm Transfer

Note: Fee will be finalized after demo session as per the Trainer suggestions and Student requirement.

Numaware Benefits

Numaware Technologies Pvt. Ltd is one of the best training institutes in Bangalore, offering Job demanding IT courses, Niche skills for working professionals, fresher’s, and students to ensure a successful future. We offer 100% placement support, cost-effective courses, real-time project experience, resume support, interview support and more. Our courses will equip you to get jobs in top MNCs and launch a successful career.

TRAINING BENEFITS in Numaware Technologies :

  • Training with IT Industry experts and Certified professional s working in MNC Companies.
  • Importance given to both theory and practice
  • Hands-on experience in real-time projects
  • Assistance in all stages of getting a job
  • Proven track record
  • Limited students in a batch
  • Flexible timings
  • Certification support

STUDENT BENEFITS in Numaware Technologies:

  • Post-training and on-job support
  • Backup classes for missed sessions
  • Remote lab facility, Wi-Fi access and LED TV projection
  • Mock exams and interviews for real-life simulation experience
  • Affordable fees with 2 easy installments

PLACEMENT BENEFITS in Numaware Technologies:

  • Our recruitment team will send you for interviews till you get placed
  • Frequently asked interview Q & A will be shared
  • Resume build support from industry professionals
  • We train you with real cases studies for interviews
  • Emphasis on practical knowledge in everything

Job Demanding Courses

Numaware Trainings is a Platform for Learning Technologies

Learn what really matters

Just work hard and focus on your job… because luck truly favors the prepared!!
All the best for your career

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