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

Topics Covered

  • Critical SAS programming skills.
  • Accessing, transforming and manipulating data.
  • Improving data quality for reporting and analytics.
  • Essential communication skills.
  • Fundamentals of statistics and analytics.
  • Working with Hadoop, Hive, Pig and SAS.
  • Exploring and visualizing data.

SAS software covered

  • Base SAS®
  • SAS® Enterprise Guide®
  • SAS® Enterprise Miner™
  • SAS® In-Memory Statistics
  • SAS® Studio
  • SAS® Visual Analytics
  • DataFlux® Data Management Server
  • DataFlux® Data M

Programming Review

SAS Fundamentals: Programming, SQL and Macro Language

This course focuses on data manipulation techniques using the DATA step and SQL procedure to access, transform, join and summarize SAS data sets. You’ll learn how to use components of the SAS macro facility to make text substitutions in SAS code and to write simple macro programs.

Topics Covered
  • Summarizing and presenting data.
  • Querying and subsetting data.
  • Transforming character, numeric and date variables.
  • Combining SAS data sets, including complex joins and merges.
  • Performing DO loop and SAS array processing.
  • Restructuring or transposing SAS data sets.
  • Performing text substitution in SAS code.
  • Using macro variables.
  • Creating simple macro definitions.

Big Data Preparation, Statistics and Visual Exploration

  • Big Data Challenges and Analysis-Driven Data
  • This course provides an overview of the challenges associated with big data and analysis-driven data.
Topics Covered
  • Reading external data files.
  • Storing and processing data.
  • Combining Hadoop and SAS.
  • Recognizing and overcoming big data challenges.

Exploring Data With SAS Visual Analytics

In this course, you’ll learn how to use SAS Visual Analytics Explorer to explore in-memory tables from the SAS® LASR™ Analytic Server and perform advanced data analyses 

Topics Covered
  • Finding previously unknown relationships and spotting trends in your data.
  • Visualizing data using charts, plots and tables.
  • Using the autocharting function to visualize data in the best possible way.
  • Using advanced graphs, such as network diagrams, Sankey diagrams and word clouds.
  • Easily adding analytics to your graphs, and including descriptions of the analytics results.
  • Navigating through your data using on-the-fly hierarchies.
  • Statistics 1: Introduction to ANOVA, Regression and Logistic Regression
  • This introductory SAS/STAT® course focuses on t-tests, ANOVA and linear regression, and includes a brief introduction to logistic regression.
Topics Covered
  • Generating descriptive statistics and exploring data with graphs.
  • Performing analysis of variance and applying multiple comparison techniques.
  • Performing linear regression and assessing the assumptions.
  • Using regression model selection techniques to aid in the choice of predictor variables in multiple regression.
  • Using diagnostic statistics to assess statistical assumptions and identify potential outliers in multiple regression.
  • Using chi-square statistics to detect associations among categorical variables.
  • Fitting a multiple logistic regression model.
  • Scoring new data using developed models.
  • Preparing Data for Analysis and Reporting
  • In this course, you’ll learn how to perform data management tasks, such as improving data quality, entity resolution and data monitoring.
Topics Covered
  • Creating and reviewing data explorations.
  • Creating and reviewing data profiles.
  • Creating data jobs for data improvement.
  • Establishing monitoring aspects for your data.
  • Understanding the QKB components.
  • Using the component editors.
  • Understanding various definition types.
  • Building a new data type (optional).
  • Crafting Compelling (and true) Data Stories
  • Storytelling is a necessary skill when talking to key stakeholders. Insights uncovered in your data can move mountains if the right people say yes. But how do you move someone from simply being curious, all the way to, “Let’s do this!” In this course, you’ll learn why storytelling is a skill you need to develop, when a story works and when it doesn’t, and how to communicate data in a meaningful way.

Big Data Programming and Loading

Introduction to SAS and Hadoop: Essentials

This course teaches you how to use SAS programming methods to read, write and manipulate Hadoop data. You’ll learn how to use Base SAS methods to read and write raw data with the DATA step, manage the Hadoop Distributed File System (HDFS) and execute MapReduce and Pig code from SAS via the HADOOP procedure. You’ll also learn how to use SAS/ACCESS® Interface to Hadoop methods that allow LIBNAME access and SQL pass-through techniques to read and write Hive or Impala table structures.

Topics Covered
  • Accessing Hadoop distributions using the LIBNAME statement and the SQL pass-through facility.
  • Creating and using SQL procedure pass-through queries.
  • Using options and efficiency techniques for optimizing data access performance.
  • Joining data using the SQL procedure and the DATA step.
  • Reading and writing Hadoop files with the FILENAME statement.
  • Executing and using Hadoop commands with PROC HADOOP.
  • Using Base SAS procedures with Hadoop.
  • DS2 Programming Essentials With Hadoop
  • This course focuses on DS2, a fourth-generation SAS proprietary language for advanced data manipulation, which enables parallel processing and storage of large data with reusable methods and packages.
Topics Covered
  • Identifying the similarities and differences between the SAS DATA step and the DS2 DATA step.
  • Converting a Base SAS DATA step to DS2.
  • Creating DS2 variable declarations, expressions and methods for data conversion, manipulation and conditional processing.
  • Creating user-defined and predefined packages to store, share and execute DS2 methods.
  • Creating and executing DS2 threads for parallel processing.
  • Using the SAS In-Database Code Accelerator to execute DS2 code outside of a SAS session.
  • Executing DS2 code in the SAS High-Performance Analytics grid using the HPDS2 procedure.

Hadoop Data Management With Hive, Pig and SAS

In this course, you will use processing methods to prepare structured and unstructured big data for analysis. You will learn to organize the data into structured tabular form using Apache Hive and Apache Pig. You will also learn SAS software technology and techniques that integrate with Hive and Pig, as well as how to use these open source capabilities by programming with Base SAS and SAS/ACCESS Interface to Hadoop, and with SAS Data Integration Studio.

Topics Covered
  • Moving data into the Hadoop ecosystem.
  • Using Hive to design a data warehouse in Hadoop, perform data analysis using the Hive query language (HiveQL) and join data sources.
  • Performing extract, transform and load (ETL).
  • Organizing data in Hadoop by usage.
  • Analyzing unstructured data using Pig.
  • Joining massive data sets using Pig.
  • Using user-defined functions (UDFs).
  • Analyzing big data in Hadoop using Hive and Pig.
  • Using SAS programming to submit Hive and Pig programs that execute in Hadoop, and store results in Hadoop or return results to SAS.
  • Using SAS programming to move data between the SAS server and the HDFS.
  • Constructing SAS Data Integration Studio jobs that integrate with Hive and Pig processes and the HDFS.

Getting Started With SAS In-Memory Statistics

This course focuses on accessing data on the SAS LASR Analytic Server and performing exploratory analysis and preparation. Topics include starting the server, loading data and manipulating data on the SAS LASR Analytic Server using the IMSTAT procedure. IMSTAT topics include deriving new temporary and permanent tables and columns, calculating summary statistics (e.g., mean, frequency and percentile), and creating filters and joins on in-memory data.

Topics Covered
  • Starting up a SAS LASR Analytic Server.
  • Loading tables into memory on the SAS LASR Analytic Server.
  • Processing in-memory tables with PROC LASR and PROC IMSTAT.
  • Accessing data more efficiently via intelligent partitioning.
  • Deriving new temporary and permanent tables and variables.
  • Creating filters and joins on in-memory data.
  • Exporting ODS result tables for client-side graphic development.
  • Producing descriptive statistics including counts, percentiles and means.
  • Creating multidimensional summaries including cross-tabulations and contingency tables.
  • Deriving kernel density estimates using normal functions.


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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