J.C. Bose University of Science & Technology, YMCA, Faridabad, Haryana
(A Haryana State Government University)

(Established By Haryana Legislative Act no. 21 of 2009 & Recognized by UGC Act 1956 u/s 22 to Confer Degrees)

Accredited ‘A+’ Grade  by NAAC
Community College of Skill Development

About Post Graduate Diploma in Data Science and Analytics

The PG Diploma in Data Science and Analytics introduces students to the realm of decision-making fueled by big data and analytics. This course provides a comprehensive understanding of data analytics, blending statistics, technical expertise, and business acumen.

Developed in collaboration with industry leaders, the curriculum ensures alignment with the demands of employers. No prior coding experience in Python, R, or SQL is required as the program starts from the basics. By the program’s conclusion, candidates possess a profound grasp of statistical techniques crucial for data analysis. They can proficiently construct analytical models using real-world data to drive business impact.

The Post Graduate Program in Data Science spans interdisciplinary boundaries, aiming to extract insights from data in diverse forms. It draws upon theories and techniques from mathematics, statistics, operations research, information science, and computer science to equip students with versatile skills essential for the data-driven era.

Course Objectives

  • Master Data Analytics Fundamentals: Provide comprehensive knowledge of fundamental concepts, techniques, and tools used in data analysis, including statistical methods, data visualization, and machine learning.
  • Enhance Practical Skills: Offer hands-on learning experiences through industry projects, internships, and workshops to develop proficiency in data analytics tools and technologies.
  • Promote Flexibility in Learning: Provide a flexible curriculum structure with multiple entry and exit points, allowing students to earn certificates and diplomas based on their progress and career goals.
  • Prepare for Industry Roles: Train students for various roles in data analytics, such as data analyst, business analyst, data scientist, data engineer, and data visualization specialist.
  • Foster Collaboration and Teamwork: Encourage collaborative learning and teamwork through group projects, case studies, and industry-linked assignments.
  • Develop Problem-Solving Abilities: Enhance students’ problem-solving and analytical skills to effectively address complex data analysis challenges in real-world scenarios.
  • Focus on Data Visualization: Train students in data visualization techniques to communicate insights effectively and facilitate data-driven decision-making processes.
  • Encourage Continuous Learning and Adaptability: Foster a mindset of continuous learning and adaptability to keep pace with advancements in data analytics technologies and methodologies.
  • Develop Professional and Soft Skills: Enhance essential soft skills such as communication, critical thinking, and project management to ensure well-rounded professional development.
  • Ensure Career Readiness: Equip graduates with the practical skills and industry-relevant knowledge needed to excel in various roles within the field of data analytics.

Career Opportunities

Students are prepared for a wide range of career opportunities in the rapidly expanding field of data analytics. The comprehensive training in statistical methods, data visualization, machine learning, and big data technologies, combined with hands-on learning experiences, opens up diverse career paths, including:

  •  Data Analyst: Analyze data to uncover insights, trends, and patterns that inform business decisions and strategies.
  • Business Analyst: Interpret data to identify opportunities for process improvement, cost reduction, and revenue optimization within organizations.
  • Data Scientist: Apply advanced analytical techniques and machine learning algorithms to extract valuable insights from large datasets.
  • Data Engineer: Design, build, and maintain data pipelines and infrastructure to support the storage, processing, and analysis of big data.
  • Data Visualization Specialist: Create visual representations of data to communicate complex information effectively and drive data-driven decision-making.
  • Machine Learning Engineer: Develop and deploy machine learning models and algorithms to solve predictive analytics and pattern recognition problems.
  • Business Intelligence Analyst: Utilize data analytics tools and techniques to generate actionable insights and reports for business stakeholders.
  • Data Consultant: Provide expert advice on data analytics strategies, tools, and technologies to help organizations leverage data for competitive advantage.
  • Big Data Analyst: Analyze and interpret large volumes of structured and unstructured data to extract valuable insights and support business objectives.
  • Data Quality Analyst: Ensure the accuracy, completeness, and reliability of data through rigorous quality assurance processes and data validation techniques.
  • Data Mining Specialist: Employ data mining techniques to discover hidden patterns and relationships within datasets, enabling informed decision-making.
  • Database Administrator: Manage and optimize databases to ensure data availability, integrity, and security across organizational systems.
  • Freelance Data Analyst: Work independently on data analytics projects for clients across various industries, offering specialized skills and expertise on a contract basis.

Scheme and Syllabus

Computer Center: Computer center serves as the practical hub for students enrolled in the PG Diploma in Data Science and Analytics program.

  • Latest computing facilities equipped with high-performance workstations and servers for data analysis and processing tasks.
  • Specialized software tools and platforms for data analytics, including statistical analysis software (e.g., R, Python), data visualization tools (e.g., Tableau, Power BI), and big data technologies (e.g., Hadoop, Spark).
  • Access to large datasets and databases for hands-on experimentation and real-world projects in data analytics.
  • High-speed internet connectivity for accessing online resources, research articles, and data repositories relevant to data analytics.
  • Collaborative workspaces for group discussions, brainstorming sessions, and project presentations.
  • Technical support staff and faculty mentors available to assist students with software installation, troubleshooting, and project development.

Programming Languages covered in the syllabus: 

  1. R
  2. Python
  3. SQL (Structured Query Language)

These programming languages are essential for various aspects of data analysis, including data manipulation, statistical analysis, machine learning, and database management. Proficiency in these languages equips students with the necessary skills to tackle real-world data analytics challenges and excel in their careers in the field.

Placement Highlights

Course Coordinator Details (For Admission related queries)

NameContact NumberEmail ID
Dr. Disha