Master of Science in Data Science

Learn how to apply the most in-demand tools such as Argus Oracle, Hadoop, SAS and more in the field of Data Science.


Why Data Science

There is great demand today for positions such as Data Scientist, Data Analyst, Business Intelligence Analyst, Systems Analyst and Data Engineer in a wide variety of fields and sectors – including but not limited healthcare, fraud and security intelligence, and economics and forecasting.

In This Course

The program offers strong preparation in statistical modeling, machine learning, optimization, management and analysis of massive data sets, and data acquisition. Build portfolios of increasingly complex projects using popular programming languages such as R and Python, which mirror the current experience and demands of the IT workplace.

The Sollers Advantage

The Master of Science in Data Science program by Sollers is taught by faculty experienced in the industry. The program is employer-backed and customized based on industry requirements. You will get to work with the latest tools in the industry such as SAS, Hadoop, R, Argus Oracle, Java, Python and more.

Learning Outcomes

  • Build statistical models and understand their power and limitations.
  • Design an experiment.
  • Use machine learning and optimization to make decisions.
  • Acquire, clean, and manage data.
  • Visualize data for exploration, analysis, and communication.
  • Collaborate within teams.
  • Deliver reproducible data analysis.
  • Manage and analyze massive data sets.
  • Assemble computational pipelines to support data science from widely available tools.
  • Conduct data science activities aware of and according to policy, privacy, security and ethical considerations.
  • Apply problem-solving strategies to open-ended questions.


  • This course provides students with the technical skills required to design and implement a database solution using a SQL server. Students use data definition language (DDL) to create and delete database objects and data manipulation language (DML) to access and manipulate those objects. Students gain hands-on experience with database design, data normalization, SQL sub-queries, creating and using views, understanding and working with data dictionaries, and loading and unloading databases. Practical activities focus on writing code that implements concepts discussed in the lecture course, specifically creating databases and SQL queries.
  • Programming with R and SAS
  • This is a basic subject on matrix theory and linear algebra. Emphasis is given to topics that will be useful in other disciplines, including linear algebra, mathematical analysis, and calculus of operations, line geometry, topology, and matrix theory, systems of equations, vector spaces, system determinants, eigenvalues, similarity, and positive definite matrices. The course also includes Graphs and Networks; Systems of Differential Equations; Markov processes, Least Squares and Projections; and Fourier Series and the Fast Fourier Transform, differential equations.
  • This course is intended primarily for mathematics, science, and engineering students. The goal of the course is to impart the concepts and techniques of modern linear algebra (over the real scalar field) with a significant level of rigor. Students write clearly about the concepts of linear algebra (definitions, counterexamples, simple proofs), and apply theory to examples. The course emphasizes the practical nature of solutions to linear algebra problems. Students implement some of these solutions, where appropriate, as computer programs.
  • The course will introduce the students to programming tools and languages which are used in data engineering domains. The programming and technical knowhow imparted for this course would include Core Java and Java Frameworks. The modules covered will include data structures, programming and analysis with Java Interface. Emphasis will laid on exposing the students to real life case studies and scenarios on application of Java for Data Engineering.
  • The course will introduce the students to programming tools and languages which are used in data engineering domains. The programming and technical knowhow imparted for this course would include Python. The modules covered will include data structures, programming and analysis with Python Interface. Emphasis will laid on exposing the students to real life case studies and scenarios on application of Python for Data Engineering.
  • Every student will be expected to submit a mini project covering the topics and will be making the presentation on the same. The credits will be provided for the content quality of the deliverable.
  • This course covers classical algorithms and data structures (algorithm design and analysis), with an emphasis on implementation and use to solve real-world problems. The course focuses on algorithms for sorting, searching, string processing, and graphs. Students learn basic strategies to characterize and evaluate greedy algorithms, divide-and-conquer, recursive backtracking, and dynamic programming. Practical activities that focus on writing code that implements concepts and focus on algorithm implementation techniques.
  • This course builds upon knowledge already acquired in the areas of system architecture and operating systems and focuses on the core issues of information security. Students learn fundamental aspects, security mechanisms, operational issues, security policies, attack types, security domains, forensics, information states, security services, threat analysis, and vulnerabilities with some case studies and practical scenarios.
  • Intro to MapReduce and Hadoop, different types of Mappers & Reducers, APIs and interfaces with sample programs, data manipulation, data flow models, parallel databases, parallel query processing, and in-database analytics, key-value stores and NoSQL systems, effectively write algorithms for systems including Hadoop, YARN and Spark, their limitations, design details, their relationship to databases, and their associated ecosystem of algorithms, extensions, and languages, landscape of specialized Big Data systems for graphs, arrays, streams, and cloud computing.
  • Intro to NoSQL, Hive – architecture, queries development, partitioning and bucketing, key functions and constructs, UDF, UDTF & UDAF, Java API based development of Hive queries, and similar approach with Pig, Sqoop, Impala, HBase, MongoDB, Dynamo, Cassandra, Neo4j, ETL using Hive, Pig and HBase, ETL using MapReduce, working with different file formats and working on platforms such as Cloudera, MapR etc.
  • This course introduces modern theories, design, and implementation models for large-scale text-based information systems. The information retrieval methodologies include Boolean, vector space, probabilistic, inference net, and language modeling. Students will acquire hands-on experience by implementing models such as clustering algorithms, automatic text categorization, and experimental evaluation. Students will experiment with cross-context retrieval algorithms, intelligent text summarization, topic detection, tagging, and tracking. The course also introduces to unstructured data with Big Data technology such as MongoDB. The hands-on activities focus on implementing techniques for efficiently managing and manipulating very large datasets residing in a distributed SQL database.


The program aims to prepare for the demands of highly specialized skill sets for making intelligent decisions with information science and analytics.Students will receive real-world applications, through case studies, projects, a capstone experience and working with big data sets, to successfully get into the specialization of choice.

Work with the latest tools

Big Data Hadoop Certification & TrainingR Programming for Data Science TrainingSAS TrainingAWS Training and Certification Python Course TrainingSQL Server Training


Our instructors are not just highly experienced in the industry, they give you the personal attention you need and guide you every step of the way.

Course Duration

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For information regarding fee and/or reserving your spot, contact our Admissions Team.


Sollers partners with industry-leading corporations and provides them with ready-on-day-one employees. We record an 82% placement rate within three months of graduation.

Financial Options

Sollers has devised viable financial options for you to ensure tuition does not get in the way of your education. Now, you can focus your attention where it needs to be – in the classroom!

Career Guidance

After the completion of program, we assist our students with interview coaching, resume building sessions, conduct mock interviews, job readiness training and make them competent to venture into the corporate world.

Student Testimonials

  • Purvesh D.
    Sollers provided me a great opportunity to start my career in Big Data. The teaching faculty has very good experience and helped me out with any difficulties I faced during the course. I have worked on different Big Data technologies via projects. I recommend this course to aspiring students who want to kick start their careers in Big Data.
    Purvesh D.
  • Mehta M.
    My overall experience with Sollers was good. I got my first full-time job through their IAM training program. The faculty and Student Services are helpful and respond to your queries quickly. I got the opportunity to learn Active Directory and Microsoft Azure.
    Mehta M.

Campus Visit


It’s a full time two year program.

The program is suitable for working recent undergraduates wishing to make their career in data science domains, undergraduates or graduates having some years of experience in IT/software domain or working executives looking to enhance their skills or make a career shift in data science domain.

The prerequisites for the course is at least a bachelor’s degree in STEM discipline or bachelor’s degree in some other field with some years of experience in either a functional, IT or related field. The students should have some basic conceptual understanding of types of data, data structures, some analytical knowledge, preferably some programming language or tool.

Students will gain hands on experience on the domains of data science which will include knowledge of programming languages, analytical and visualization tools and the core statistical and applied concepts which forms part of the data science domain. These would include programming languages like Python and Java, Big data ecosystem, visualization tools (both open source as well as commercial) and applications like Machine Learning, Natural Language Processing, Artificial Intelligence, etc.

The growing demand and consumption of data have resulted in the surge for professionals who could analyze and work with different kinds of data. There exists a massive demand-supply gap in the domain of data science and big data. A research study conducted by Accenture found that more than 90% of clients planned to hire workers with DSA expertise, but 40% were confronted by a lack of available talent. Barring a few most position in Data Science domain demands a masters degree, thus this Masters Course is apt for professionals and students looking to make a career in data science domain.

Industry Facts

  • IDC estimates that by 2020, business transactions (including both B2B and B2C) via the internet will reach up to 450 billion per day. Source :
  • Gartner estimates that 90% of large companies will have a CDO in a year’s time — with most of them learning on the job, according to the research firm.
  • By 2020, Forrester predicts businesses that use data effectively will be collectively worth $1.2 trillion, up from $333 billion in 2015