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BACHELOR-OF-SCIENCE-DATA-ANALYTICS in Data Analytics at Delhi Skill and Entrepreneurship University, Okhla II Campus
Delhi, Delhi
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About the Specialization
What is Data Analytics at Delhi Skill and Entrepreneurship University, Okhla II Campus Delhi?
This Data Analytics program at Delhi Skill and Entrepreneurship University focuses on equipping students with advanced analytical skills crucial for extracting insights from complex datasets. Given India''''s burgeoning digital economy and data-driven industries, this program is designed to meet the high demand for skilled data professionals, distinguishing itself through a blend of theoretical knowledge and practical, industry-relevant applications.
Who Should Apply?
This program is ideal for fresh graduates from science, engineering, or commerce backgrounds with a strong aptitude for mathematics and problem-solving, seeking entry into the rapidly expanding data sector. It also caters to early-career professionals looking to upskill in cutting-edge data technologies or career changers aspiring to transition into roles like Data Analyst, Business Intelligence Developer, or Data Scientist within India''''s tech landscape.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India, including roles in IT services, e-commerce, banking, healthcare, and manufacturing, with entry-level salaries typically ranging from INR 4-7 lakhs per annum, growing significantly with experience. The curriculum prepares students for industry-standard certifications and provides a solid foundation for advanced studies or leadership positions in data strategy across Indian companies.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Consistently practice core programming concepts in Python, focusing on logical problem-solving and clean code. Engage with online coding challenges regularly to strengthen algorithmic thinking and understand data manipulation basics.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Python documentation
Career Connection
Strong programming skills are foundational for all data roles, making candidates highly employable in entry-level analyst and developer positions across industries.
Build a Strong Mathematical & Statistical Base- (Semester 1-2)
Dedicate extra time to understanding the underlying mathematical and statistical theories covered in coursework. Utilize online platforms and textbooks to clarify concepts and solve practice problems thoroughly.
Tools & Resources
Khan Academy, NPTEL Mathematics/Statistics courses, NCERT textbooks
Career Connection
Essential for understanding machine learning algorithms, model interpretation, and making robust data-driven decisions critical for advanced data science roles in India.
Engage in Peer Learning and Collaborative Projects- (Semester 1-2)
Form study groups to discuss complex topics, clarify doubts, and work on small collaborative projects using publicly available datasets. Actively participate in college workshops focused on data literacy and basic project development.
Tools & Resources
GitHub for version control, Kaggle for datasets, Google Meet for virtual study sessions
Career Connection
Develops teamwork, communication skills, and exposes students to diverse problem-solving approaches, highly valued by employers in corporate and startup environments.
Intermediate Stage
Develop Practical Data Science Skills with Tools- (Semester 3-4)
Beyond classroom learning, actively work on projects using Python libraries like Pandas, NumPy, and Scikit-learn, and visualization tools such as Tableau or Power BI. Focus on completing end-to-end data analysis workflows.
Tools & Resources
Kaggle competitions, Coursera/Udemy specialized courses, Tableau Public, Power BI Desktop
Career Connection
Hands-on experience with industry-standard tools is a primary requirement for data analyst and junior data scientist roles, directly translating to job readiness and better placement opportunities.
Seek Internships and Industry Mentorship- (Semester 4-5)
Actively look for summer internships or part-time projects in data analytics roles within startups or established companies in India. Network with industry professionals through LinkedIn and leverage the college''''s alumni network for guidance.
Tools & Resources
LinkedIn, Internshala, DSEU placement cell, Alumni connect platforms
Career Connection
Provides invaluable real-world experience, builds a professional network, and often leads to pre-placement offers, significantly boosting career prospects in the competitive Indian job market.
Participate in Data Hackathons and Competitions- (Semester 3-5)
Join national and international data hackathons or Kaggle competitions. This pushes boundaries, improves problem-solving under pressure, and helps build a strong, diversified portfolio demonstrating practical application of skills.
Tools & Resources
Kaggle, Analytics Vidhya, DataHack platform, DSEU innovation cell events
Career Connection
Demonstrates practical skills, resilience, and innovative thinking to potential employers, making resumes stand out during campus placements and direct hiring processes.
Advanced Stage
Specialize in an Advanced Data Domain- (Semester 5-6)
Based on interests and career goals, deep dive into areas like Deep Learning, NLP, Cloud Data Engineering, or Business Intelligence. Undertake a comprehensive capstone project in the chosen specialization to showcase expertise.
Tools & Resources
TensorFlow, PyTorch, AWS/Azure certifications, Specific online courses and research papers
Career Connection
Allows for focused career targeting (e.g., AI/ML Engineer, NLP Specialist) and makes candidates highly desirable for niche roles with potentially better compensation packages.
Build a Professional Portfolio and Resume- (Semester 5-6)
Compile all projects (academic, personal, hackathon) into a well-structured online portfolio (e.g., GitHub, personal website). Tailor resumes and LinkedIn profiles specifically for data-centric roles, highlighting key skills and achievements.
Tools & Resources
GitHub, Personal website builders (e.g., Google Sites), LinkedIn profile optimization guides
Career Connection
A strong portfolio is crucial for showcasing practical abilities to recruiters, leading to interview shortlists and providing tangible proof of skills sought by top Indian and MNC data teams.
Focus on Placement Preparation and Soft Skills- (Semester 6)
Actively prepare for technical interviews covering coding, machine learning concepts, and SQL, along with behavioral rounds. Practice communication, presentation, and negotiation skills, often provided through the college''''s placement cell.
Tools & Resources
Mock interviews, Aptitude test platforms, DSEU placement cell workshops, Group discussion practice
Career Connection
Crucial for converting interview opportunities into job offers, ensuring a smooth transition from academics to a successful professional career in data analytics roles across various sectors.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 or an equivalent examination with a minimum of 50% marks in aggregate and must have studied Mathematics as a compulsory subject. (Relaxation of 5% in marks for SC/ST/PwBD candidates).
Duration: 3 years / 6 semesters
Credits: 116 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSDAT101 | Mathematical Foundations for Data Analytics | Core | 4 | Set Theory and Relations, Probability and Statistics Basics, Matrices and Determinants, Differential Calculus, Integral Calculus, Linear Algebra Fundamentals |
| BSDAT102 | Introduction to Programming | Core | 4 | Programming Fundamentals, Data Types and Variables, Control Structures, Functions and Modules, Object-Oriented Programming Concepts, File Handling |
| BSDAT103 | Database Management Systems | Core | 4 | Database Concepts and Architecture, Entity-Relationship (ER) Model, Relational Model and Algebra, Structured Query Language (SQL), Normalization Techniques, Transaction Management |
| BSDAT104 | Professional Communication | Ability Enhancement Course | 3 | Fundamentals of Communication, Verbal and Non-Verbal Communication, Listening Skills, Presentation Skills, Group Discussion Techniques, Business Correspondence |
| BSDAT105 | Basics of Business Economics | Generic Elective | 3 | Introduction to Microeconomics, Demand and Supply Analysis, Market Structures, Introduction to Macroeconomics, National Income Accounting, Inflation and Unemployment |
| BSDAT151 | Programming Lab | Lab | 2 | Python Programming Basics, Conditional Statements and Loops, Functions and Modules in Python, Object-Oriented Programming in Python, Data Structures Implementation, File I/O Operations |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSDAT201 | Statistical Foundations for Data Analytics | Core | 4 | Descriptive Statistics, Probability Distributions, Sampling and Estimation, Hypothesis Testing, Correlation and Regression Analysis, ANOVA |
| BSDAT202 | Data Structures and Algorithms | Core | 4 | Introduction to Data Structures, Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting Algorithms, Searching Algorithms |
| BSDAT203 | Operating Systems Concepts | Core | 4 | Introduction to Operating Systems, Process Management, CPU Scheduling, Memory Management, File Systems, I/O Management |
| BSDAT204 | Environmental Science | Ability Enhancement Course | 3 | Natural Resources, Ecosystems and Biodiversity, Environmental Pollution, Global Environmental Issues, Waste Management, Environmental Protection Acts |
| BSDAT205 | Principles of Management | Generic Elective | 3 | Fundamentals of Management, Planning and Decision Making, Organizing and Staffing, Directing and Leading, Controlling, Managerial Ethics |
| BSDAT251 | Data Structures Lab | Lab | 2 | Implementation of Linear Data Structures, Implementation of Non-Linear Data Structures, Graph Traversal Algorithms, Sorting and Searching Techniques, Hashing Techniques, Algorithm Complexity Analysis |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSDAT301 | Introduction to Data Science | Core | 4 | Data Science Lifecycle, Data Collection and Preprocessing, Exploratory Data Analysis (EDA), Data Visualization Principles, Introduction to Machine Learning, Ethics in Data Science |
| BSDAT302 | Object Oriented Programming using Python | Core | 4 | OOP Concepts: Classes and Objects, Inheritance and Polymorphism, Encapsulation and Abstraction, Exception Handling, File Handling in Python, Using Python Libraries for OOP |
| BSDAT303 | Big Data Technologies | Core | 4 | Introduction to Big Data, Hadoop Ecosystem (HDFS, MapReduce), Apache Spark Framework, NoSQL Databases, Data Ingestion and Processing, Big Data Analytics Tools |
| BSDAT304 | Universal Human Values | Ability Enhancement Course | 3 | Understanding Human Values, Harmony in the Self, Harmony in Family and Society, Harmony in Nature, Professional Ethics, Holistic Development |
| BSDAT305 | Entrepreneurship | Generic Elective | 3 | Concepts of Entrepreneurship, Business Idea Generation, Market Research and Analysis, Business Plan Development, Funding and Legal Aspects, Startup Ecosystem |
| BSDAT351 | Data Science Lab | Lab | 2 | Data Manipulation with Pandas, Numerical Computing with NumPy, Data Visualization with Matplotlib/Seaborn, Basic Machine Learning Model Building, Feature Engineering, Exploratory Data Analysis Projects |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSDAT401 | Machine Learning | Core | 4 | Supervised Learning Algorithms, Unsupervised Learning Algorithms, Regression and Classification Techniques, Clustering Methods, Model Evaluation and Selection, Ensemble Methods |
| BSDAT402 | Data Visualization | Core | 4 | Principles of Data Visualization, Exploratory vs Explanatory Visualization, Dashboard Design and Storytelling, Interactive Visualizations, Data Visualization Tools (Tableau/Power BI), Advanced Chart Types |
| BSDAT403 | Cloud Computing for Data Analytics | Core | 4 | Introduction to Cloud Computing, Cloud Service and Deployment Models, Big Data Services on Cloud (AWS/Azure/GCP), Cloud Storage and Databases, Serverless Computing, Cloud Security |
| BSDAT404 | Cyber Security | Ability Enhancement Course | 3 | Introduction to Cyber Security, Common Cyber Threats and Attacks, Cryptography Basics, Network Security, Web Application Security, Cyber Laws and Ethics |
| BSDAT405 | Artificial Intelligence | Generic Elective | 3 | Introduction to AI, Problem Solving by Search, Knowledge Representation, Machine Learning Concepts, Natural Language Processing Fundamentals, Robotics Basics |
| BSDAT451 | Machine Learning Lab | Lab | 2 | Implementing Supervised Learning Models, Implementing Unsupervised Learning Models, Scikit-learn for ML Tasks, Model Evaluation and Hyperparameter Tuning, Introduction to Deep Learning Frameworks, Machine Learning Projects |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSDAT501 | Deep Learning | Core | 4 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders and GANs, Transfer Learning, Deep Learning Frameworks (TensorFlow, PyTorch) |
| BSDAT502 | Natural Language Processing | Core | 4 | Text Preprocessing (Tokenization, Stemming, Lemmatization), Word Embeddings (Word2Vec, GloVe), Sentiment Analysis, Text Classification, Named Entity Recognition (NER), Language Models |
| BSDAT503 | Data Warehousing and Data Mining | Core | 4 | Data Warehousing Concepts and Architecture, ETL Process, OLAP and OLTP, Data Mining Techniques, Association Rule Mining, Classification and Clustering |
| BSDAE501 | Computer Vision | Discipline Specific Elective | 3 | Image Processing Fundamentals, Feature Detection and Extraction, Object Recognition and Detection, Image Segmentation, Deep Learning for Computer Vision, Applications of Computer Vision |
| BSDAE502 | Reinforcement Learning | Discipline Specific Elective | 3 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal Difference Learning (Q-Learning, SARSA), Deep Reinforcement Learning (DQN), Policy Gradient Methods |
| BSDAE503 | Time Series Analysis | Discipline Specific Elective | 3 | Time Series Components (Trend, Seasonality, Cycle), Stationarity and ARIMA Models, Forecasting Techniques, Spectral Analysis, ARCH/GARCH Models, Time Series in Financial Data |
| BSDAE504 | Ethical Hacking | Discipline Specific Elective | 3 | Introduction to Ethical Hacking, Footprinting and Reconnaissance, Scanning Networks, System Hacking, Web Application Hacking, Wireless Network Hacking |
| BSDAT551 | Deep Learning Lab | Lab | 2 | Implementation of Feedforward Networks, Building CNNs for Image Tasks, Developing RNNs for Sequence Data, Working with TensorFlow/PyTorch, Transfer Learning Applications, Hyperparameter Tuning for Deep Models |
| BSDAT552 | Data Mining Lab | Lab | 2 | Data Preprocessing using Tools, Implementing Classification Algorithms, Implementing Clustering Algorithms, Discovering Association Rules, Predictive Modeling Projects, Using Data Mining Software (e.g., Weka) |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSDAT601 | Internet of Things | Core | 4 | IoT Architecture and Components, Sensors, Actuators, and Microcontrollers, IoT Communication Protocols, Data Processing and Analytics in IoT, Edge and Fog Computing, IoT Security and Privacy |
| BSDAE601 | Business Intelligence | Discipline Specific Elective | 4 | Introduction to Business Intelligence, Data Warehousing for BI, OLAP and Reporting, Dashboards and Scorecards, Data Storytelling, BI Tools and Applications |
| BSDAE602 | Geospatial Data Analytics | Discipline Specific Elective | 4 | Geographic Information Systems (GIS) Basics, Spatial Data Models and Databases, Geocoding and Map Projections, Spatial Analysis Techniques, Remote Sensing Fundamentals, Location Intelligence Applications |
| BSDAE603 | Financial Analytics | Discipline Specific Elective | 4 | Financial Markets and Instruments, Risk Measurement and Management, Portfolio Optimization, Time Series in Finance, Algorithmic Trading Strategies, Econometric Models for Finance |
| BSDAT603 | Project | Project | 6 | Project Proposal and Planning, Literature Review, System Design and Architecture, Implementation and Development, Testing and Evaluation, Documentation and Presentation |
| BSDAT604 | Internship | Internship | 2 | Industry Exposure and Practical Application, Problem Solving in Real-World Scenarios, Professional Skill Development, Teamwork and Collaboration, Reporting and Documentation, Networking and Career Exploration |
