.png&w=1920&q=75)
MBA-DATA-SCIENCES-AND-DATA-ANALYTICS-SCIT in General at Symbiosis International University (SIU)
Pune, Maharashtra
.png&w=1920&q=75)
About the Specialization
What is General at Symbiosis International University (SIU) Pune?
This MBA (Data Sciences and Data Analytics) program at Symbiosis International University focuses on developing leaders skilled in leveraging data for strategic decision-making. The curriculum integrates core management principles with advanced data science techniques, addressing the growing demand for data-savvy professionals in the Indian market. It is designed to create a bridge between business acumen and analytical prowess.
Who Should Apply?
This program is ideal for engineering, statistics, or mathematics graduates seeking entry into the analytics field, as well as working professionals from IT or business domains looking to upskill. It also caters to career changers aiming to transition into data-driven roles, provided they possess strong analytical aptitude and a desire to lead data initiatives.
Why Choose This Course?
Graduates of this program can expect to secure roles such as Data Scientist, Business Analyst, Machine Learning Engineer, or Analytics Consultant in India. Entry-level salaries typically range from INR 6-12 LPA, with experienced professionals earning significantly more. The program aligns with industry certifications, fostering strong growth trajectories in Indian and multinational corporations.
Student Success Practices
Foundation Stage
Master Programming Fundamentals and Statistics- (Semester 1-2)
Dedicate significant time to mastering Python/R for data science and reinforcing statistical concepts. Utilize platforms like HackerRank, LeetCode, or DataCamp for daily coding challenges and practice problems to build a strong analytical foundation.
Tools & Resources
Python/R programming environments, HackerRank, DataCamp, Khan Academy (for statistics)
Career Connection
A solid grasp of programming and statistics is fundamental for almost all data science roles, making candidates immediately employable for entry-level positions.
Engage in Peer Learning and Collaborative Projects- (Semester 1-2)
Form study groups to discuss complex topics and work on small data analysis projects collaboratively. Participate actively in classroom discussions and tutorials to clarify doubts and consolidate learning.
Tools & Resources
GitHub (for version control), Google Colab, Discussion forums (e.g., Kaggle forums)
Career Connection
Develops teamwork, communication, and problem-solving skills crucial for corporate environments where data science is often a collaborative effort.
Build a Portfolio of Mini Data Projects- (Semester 1-2)
Start working on small, personal data projects using publicly available datasets (e.g., from Kaggle). Focus on cleaning, exploring, and visualizing data, and document your process on GitHub to showcase early skills.
Tools & Resources
Kaggle datasets, Jupyter Notebooks, GitHub
Career Connection
Demonstrates practical application of learned concepts and initiative, giving an edge in internship and early placement interviews.
Intermediate Stage
Seek Industry Internships and Live Projects- (Semester 3)
Actively apply for summer internships with companies, focusing on roles that offer hands-on experience in machine learning, big data, or business intelligence. Leverage SCIT''''s placement cell and alumni network.
Tools & Resources
LinkedIn, Naukri.com, SCIT Placement Cell
Career Connection
Internships provide invaluable real-world experience, leading to pre-placement offers and a deeper understanding of industry practices.
Specialize and Gain Certifications- (Semester 3-4)
Choose electives wisely to specialize in areas like Deep Learning, NLP, or Cloud Analytics. Pursue relevant industry certifications (e.g., AWS Certified Data Analytics, Google Cloud Professional Data Engineer) to validate specialized skills.
Tools & Resources
Coursera, Udemy (for certifications), AWS/Azure/GCP training platforms
Career Connection
Specialization and certifications make candidates highly attractive for specific niche roles and often command higher compensation.
Network with Industry Professionals and Alumni- (Semester 3-4)
Attend industry conferences, workshops, and guest lectures hosted by SCIT. Connect with alumni on LinkedIn and engage in informational interviews to understand career paths and gain insights.
Tools & Resources
LinkedIn, Industry events (e.g., Data Science Congress), SCIT Alumni Portal
Career Connection
Building a strong professional network opens doors to job opportunities, mentorship, and industry collaborations.
Advanced Stage
Excel in the Capstone Project for Real-World Impact- (Semester 4)
Treat the Capstone Project as a full-fledged industry assignment. Choose a complex, real-world problem, apply advanced data science techniques, and focus on delivering measurable business value. Document thoroughly.
Tools & Resources
Jupyter Notebooks, Git/GitHub, Cloud platforms (AWS/Azure/GCP)
Career Connection
A strong Capstone Project serves as a powerful portfolio piece, demonstrating end-to-end problem-solving ability and readiness for leadership roles.
Intensive Placement Preparation and Mock Interviews- (Semester 4)
Participate in mock interviews, aptitude tests, and group discussions organized by the college. Tailor your resume and cover letters to specific job descriptions and practice articulating your projects and skills effectively.
Tools & Resources
Placement cell resources, Online aptitude tests, Interview preparation websites
Career Connection
Enhances confidence and readiness for the rigorous placement process, significantly increasing chances of securing desirable job offers.
Develop Leadership and Business Acumen- (Semester 4)
Take on leadership roles in student clubs, college events, or group projects. Focus on understanding the business context of data science, aligning analytical solutions with organizational goals, and effective stakeholder communication.
Tools & Resources
Business case studies, Harvard Business Review articles, Leadership workshops
Career Connection
Prepares students for management and leadership positions within analytics teams, enabling them to drive strategic data initiatives.
Program Structure and Curriculum
Eligibility:
- Bachelor''''s degree with minimum 50% marks (45% for SC/ST category) from any recognised University/Institution. Successful completion of SNAP Test, Group Exercise, Personal Interview & Written Ability Test (GE-PIWAT).
Duration: 2 years / 4 semesters
Credits: 96 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| SCIT M101 | Business Communication | Core | 3 | Oral and Written Communication, Presentation Skills, Business Reports and Proposals, Professional Correspondence, Cross-Cultural Communication |
| SCIT M102 | Organizational Behaviour | Core | 3 | Individual Behavior, Group Dynamics and Teamwork, Leadership Theories, Motivation and Job Satisfaction, Organizational Culture and Change Management |
| SCIT M103 | Financial and Management Accounting | Core | 3 | Financial Statements Analysis, Costing Methods, Budgeting and Variance Analysis, Investment Decisions, Working Capital Management |
| SCIT D101 | Business Statistics | Core | 3 | Probability Theory, Hypothesis Testing, Regression Analysis, Sampling Methods, Data Distribution and Statistical Inference |
| SCIT D102 | Fundamentals of Data Science | Core | 3 | Data Science Ecosystem, Data Types and Sources, Data Lifecycle, Problem Scoping, Data Ethics and Privacy, Introduction to Big Data |
| SCIT D103 | Programming for Data Science (Python) | Core | 4 | Python Fundamentals, Data Structures (Lists, Dictionaries), Control Flow and Functions, NumPy and Pandas for Data Manipulation, File I/O and Error Handling |
| SCIT D104 | Database Management Systems | Core | 3 | Relational Database Concepts, SQL Queries and Operations, Database Design (ER Models), Normalization, Introduction to NoSQL Databases |
| SCIT D105 | Data Visualization | Core | 3 | Principles of Data Visualization, Data Storytelling, Tools (Tableau/Power BI), Chart Types and Dashboards, Interactive Visualization Techniques |
| SCIT P101 | Communication and Professional Development Lab | Lab | 2 | Public Speaking Practice, Group Discussion Techniques, Interview Skills Training, Resume and Cover Letter Writing, Professional Etiquette |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| SCIT M201 | Marketing Management | Core | 3 | Marketing Mix (4Ps), Consumer Behavior, Market Segmentation, Targeting, Positioning, Product and Brand Management, Digital Marketing Strategies |
| SCIT M202 | Human Resource Management | Core | 3 | HR Planning and Recruitment, Performance Management Systems, Training and Development, Employee Relations, HR Analytics and Metrics |
| SCIT M203 | Operations Management | Core | 3 | Supply Chain Management, Inventory Control Models, Quality Management (TQM, Six Sigma), Project Planning and Scheduling, Process Improvement and Lean Operations |
| SCIT D201 | Research Methodology | Core | 3 | Research Design, Data Collection Methods, Questionnaire Design, Statistical Data Analysis, Report Writing and Presentation |
| SCIT D202 | Applied Machine Learning | Core | 4 | Supervised and Unsupervised Learning, Classification Algorithms (Logistic Regression, SVM, Decision Trees), Regression Models, Clustering (K-Means, Hierarchical), Model Evaluation and Hyperparameter Tuning |
| SCIT D203 | Big Data Technologies | Core | 4 | Hadoop Ecosystem (HDFS, MapReduce), Apache Spark for Big Data Processing, Hive and Pig for Data Warehousing, NoSQL Databases (Cassandra, MongoDB), Distributed Computing Concepts |
| SCIT D204 | Data Warehousing & Data Mining | Core | 3 | Data Warehouse Architecture, ETL Processes, OLAP Cubes and Operations, Association Rule Mining, Classification and Clustering Techniques, Predictive Modeling |
| SCIT D205 | Business Analytics Tools (R/SAS) | Core | 3 | R/SAS Programming Fundamentals, Statistical Modeling in R/SAS, Data Manipulation and Cleaning, Visualization with R/SAS, Introduction to Predictive Analytics |
| SCIT P201 | Machine Learning Lab | Lab | 2 | Implementing ML Algorithms in Python, Data Preprocessing and Feature Engineering, Model Training and Evaluation, Ensemble Methods Application, Practical Scenarios for ML Deployment |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| SCIT M301 | Project Management | Core | 3 | Project Lifecycle and Methodologies (Agile, Waterfall), Project Planning and Scheduling (Gantt, PERT), Risk Management and Mitigation, Resource Allocation and Budgeting, Project Monitoring and Control |
| SCIT M302 | Strategic Management | Core | 3 | Strategic Analysis (SWOT, PESTEL), Strategy Formulation and Implementation, Competitive Advantage, Corporate Governance, Global Strategy and Diversification |
| SCIT D301 | Data Governance and Ethics | Core | 3 | Data Privacy Regulations (GDPR, DPB 2023), Data Quality Management, Data Security and Compliance, Ethical AI Principles, Responsible Data Practices |
| SCIT D302 | Cloud Analytics | Core | 3 | Cloud Computing Models (IaaS, PaaS, SaaS), AWS/Azure/GCP Analytics Services, Data Lake and Data Lakehouse Architectures, Serverless Analytics, Cloud Data Security and Cost Management |
| SCIT DE3A | Deep Learning | Elective | 3 | Artificial Neural Networks, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Generative Adversarial Networks (GANs), Deep Learning Frameworks (TensorFlow, PyTorch) |
| SCIT DE3B | Natural Language Processing | Elective | 3 | Text Preprocessing, Sentiment Analysis, Topic Modeling, Word Embeddings (Word2Vec, GloVe), Transformers and Attention Mechanisms |
| SCIT P301 | Summer Internship | Project | 6 | Real-world Project Application, Industry Problem Solving, Report Writing and Documentation, Presentation of Findings, Professional Networking |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| SCIT M401 | Leadership & Change Management | Core | 3 | Leadership Styles and Theories, Organizational Change Models, Conflict Resolution, Team Building and Motivation, Ethical Leadership and Decision Making |
| SCIT D401 | Capstone Project | Project | 8 | End-to-End Data Science Project Execution, Problem Definition and Data Acquisition, Model Development and Validation, Deployment Strategies, Comprehensive Project Reporting and Presentation |
| SCIT DE4A | Time Series Analysis & Forecasting | Elective | 3 | Time Series Components (Trend, Seasonality), ARIMA and SARIMA Models, Exponential Smoothing, Forecasting Techniques, Evaluation of Forecast Models |
| SCIT DE4B | IoT Analytics | Elective | 3 | IoT Architecture and Components, Sensor Data Collection and Processing, Edge Analytics, Real-time Data Streams, Applications of IoT Analytics |
| SCIT DE4C | Marketing Analytics | Elective | 3 | Customer Segmentation, Churn Prediction, Campaign Optimization, Web Analytics, A/B Testing and Experimentation |
