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PGDBI in General at Dhanalakshmi Srinivasan College of Arts & Science For Women, Perambalur
Perambalur, Tamil Nadu
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About the Specialization
What is General at Dhanalakshmi Srinivasan College of Arts & Science For Women, Perambalur Perambalur?
This Post Graduate Diploma in Business Intelligence (PGDBI) program at Dhanalakshmi Srinivasan College of Arts and Science for Women focuses on equipping students with advanced analytical skills crucial for data-driven decision-making. Addressing the growing demand for BI professionals in the Indian market, this program emphasizes practical application of tools like Python, Power BI, and big data technologies. It is designed to bridge the skill gap, providing a comprehensive understanding of business analytics and intelligence concepts.
Who Should Apply?
This program is ideal for fresh graduates from any discipline seeking entry into the rapidly expanding field of business intelligence and data analytics in India. It also suits working professionals aiming to upskill in modern analytical tools and methodologies or career changers transitioning into data-centric roles. Candidates with a foundational understanding of mathematics or statistics, eager to leverage data for strategic insights, will find this program highly beneficial.
Why Choose This Course?
Graduates of this program can expect to secure roles such as Business Intelligence Analyst, Data Analyst, BI Consultant, or Data Scientist in Indian companies across various sectors including IT, finance, retail, and healthcare. Entry-level salaries typically range from INR 3-6 LPA, with significant growth potential as experience increases. The curriculum aligns with industry demands, preparing students for impactful careers in India''''s booming data economy.
Student Success Practices
Foundation Stage
Master Core BI and Python Fundamentals- (Semester 1)
Focus intensively on understanding data warehousing, data mining, and core Python programming concepts taught in the initial semester. Practice regularly with data structures, basic algorithms, and data manipulation using Pandas to build a strong analytical base.
Tools & Resources
Official syllabus textbooks, Python documentation, W3Schools Python, Kaggle micro-courses, HackerRank
Career Connection
A robust understanding of fundamentals is critical for all entry-level data roles, enabling efficient problem-solving and coding during technical interviews for analyst positions.
Hands-on with BI Tools and Data Visualization- (Semester 1)
Actively engage with practical assignments involving Power BI, SSIS, SSAS, and SSRS. Work on creating interactive dashboards and reports using sample datasets. Experiment with different visualization techniques to effectively communicate insights.
Tools & Resources
Microsoft Power BI Desktop, SQL Server Developer Edition, Data.gov.in, UCI Machine Learning Repository, YouTube tutorials
Career Connection
Proficiency in industry-standard BI tools is a direct requirement for BI analyst roles and significantly enhances a candidate''''s portfolio for placements.
Collaborative Learning and Case Study Analysis- (Semester 1)
Form study groups to discuss complex topics, solve problems together, and work on mini-projects. Actively participate in analyzing business intelligence case studies from Indian and global companies to understand real-world application and decision-making.
Tools & Resources
Harvard Business Review case studies, Livemint, Economic Times, College library resources, Online forums
Career Connection
Teamwork and analytical thinking derived from case studies are highly valued in corporate environments, improving problem-solving skills for business roles.
Intermediate Stage
Apply Machine Learning Fundamentals to Business Problems- (Early Semester 2)
Focus on implementing foundational machine learning algorithms (e.g., regression, classification) using Python, specifically targeting simple business problems like prediction or segmentation. Understand the underlying math and interpret model outcomes in a business context.
Tools & Resources
Scikit-learn library, Jupyter Notebook, Faculty recommended ML textbooks, Introductory MOOCs on ML application for business
Career Connection
This early application of ML prepares students for junior data science or advanced analytics roles, where basic model building and interpretation are required.
Explore Cloud & Big Data Concepts Practically- (Early Semester 2)
Begin exploring the theoretical and practical aspects of cloud computing and big data technologies. Participate in initial lab sessions on Hadoop, Spark, or cloud services, grasping how large datasets are processed and stored.
Tools & Resources
AWS/Azure free tier trials, Databricks Community Edition, Apache Hadoop/Spark beginner tutorials, Cloud-specific online documentation
Career Connection
Gaining early exposure to these technologies is crucial for understanding modern data infrastructures, opening doors to data engineering and big data analyst roles.
Network Building and Industry Insights- (Semester 1 & Early Semester 2)
Actively engage in departmental seminars, guest lectures, and workshops focused on industry trends in BI and analytics. Start building a professional network through LinkedIn, connecting with alumni and professionals in the field.
Tools & Resources
LinkedIn, College career services, Industry-specific webinars, Tech meetups (online or local)
Career Connection
Networking provides valuable career guidance, potential internship leads, and insights into current industry demands, which are vital for successful placements.
Advanced Stage
Master Advanced ML, Cloud, and Big Data Implementation- (Late Semester 2)
Progress to implementing complex machine learning models, fine-tuning them for performance, and deploying solutions within cloud and big data environments. Focus on scalability, efficiency, and real-time processing using advanced tools and techniques from the curriculum.
Tools & Resources
Advanced Scikit-learn, TensorFlow/PyTorch, AWS/Azure advanced services (e.g., SageMaker, Data Factory), Spark streaming, Industry-specific datasets
Career Connection
High-level proficiency in these areas is critical for senior data scientist, machine learning engineer, and big data architect roles, attracting top-tier companies in India.
Execute a Comprehensive Capstone Project- (Late Semester 2)
Dedicate significant effort to the PGDBI project, selecting a real-world business problem, designing an end-to-end BI/Analytics solution, implementing it with learned tools, and presenting robust findings. This project should be a culmination of all acquired skills.
Tools & Resources
All tools learned throughout the program, Academic mentors, Project management software, Industry reports, Peer reviews
Career Connection
The capstone project is the cornerstone of a professional portfolio, serving as concrete proof of skill and problem-solving ability, directly influencing placement success.
Intensive Placement Preparation and Portfolio Refinement- (Late Semester 2)
Engage in rigorous mock interviews (technical and HR), aptitude test practice, and resume/portfolio refinement sessions. Tailor your project portfolio and communication skills to specific job roles and company requirements. Utilize college placement cells actively.
Tools & Resources
College placement cell resources, Online aptitude test platforms (e.g., Indiabix), Glassdoor for company interview insights, LinkedIn for recruiter engagement, Professional resume builders
Career Connection
Dedicated preparation significantly increases the likelihood of securing desirable placements in leading Indian and multinational companies, ensuring a strong start to your career.
Program Structure and Curriculum
Eligibility:
- Any Degree
Duration: 2 semesters
Credits: 32 Credits
Assessment: Internal: 31%, External: 69%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20PBI101 | FUNDAMENTALS OF BUSINESS INTELLIGENCE | Core | 4 | Introduction to Business Intelligence, Data Warehousing, Data Mining, Business Analytics, Decision Support Systems, Big Data Technologies |
| 20PBI102 | DATA ANALYTICS USING PYTHON | Core | 4 | Introduction to Python, Data Structures in Python, Data Manipulation with Pandas, Data Visualization with Matplotlib/Seaborn, Statistical Analysis in Python, Machine Learning Concepts |
| 20PBI103 | BUSINESS INTELLIGENCE TOOLS | Core | 4 | Introduction to BI Tools, Power BI Fundamentals, SQL Server Integration Services (SSIS), SQL Server Analysis Services (SSAS), SQL Server Reporting Services (SSRS), Data modeling and Dashboards |
| 20PBI1P1 | DATA ANALYTICS USING PYTHON LAB | Lab/Practical | 4 | Python Programming Exercises, Data Cleaning and Preprocessing, Exploratory Data Analysis, Implementing Statistical Models, Building Visualization Dashboards, Working with Libraries (NumPy, Pandas, Matplotlib) |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 20PBI201 | MACHINE LEARNING FOR BUSINESS INTELLIGENCE | Core | 4 | Foundations of Machine Learning, Supervised Learning Algorithms, Unsupervised Learning Algorithms, Ensemble Methods, Model Evaluation and Selection, Real-world ML Applications in Business |
| 20PBI202 | CLOUD AND BIG DATA ANALYTICS | Core | 4 | Cloud Computing Fundamentals, Big Data Ecosystem (Hadoop, Spark), Data Storage in Cloud (AWS S3, Azure Blob), Data Processing in Cloud (EMR, Databricks), Real-time Analytics, Data Security in Cloud |
| 20PBI2P2 | BIG DATA ANALYTICS LAB | Lab/Practical | 4 | Hadoop Ecosystem Implementation, Spark Programming, Data Ingestion and Processing, NoSQL Databases (MongoDB, Cassandra), Cloud Analytics Services Exercises, Real-time data stream processing |
| 20PBI2PR | PROJECT | Project | 4 | Problem Identification and Scoping, Data Collection and Preprocessing, Model Development and Implementation, Analysis and Interpretation of Results, Reporting and Presentation, Deployment Considerations |
