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BSC-DATA-ANALYTICS in Data Analytics at Delhi Skill and Entrepreneurship University
Delhi, Delhi
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About the Specialization
What is Data Analytics at Delhi Skill and Entrepreneurship University Delhi?
This Data Analytics program at Champs Delhi Skill and Entrepreneurship University Okhla II Campus focuses on equipping students with a robust foundation in statistics, programming, and machine learning essential for modern data-driven decision-making. In the rapidly evolving Indian industry, marked by digital transformation across sectors like e-commerce, finance, and healthcare, this program stands out by offering a practical, hands-on approach to data challenges, ensuring graduates are industry-ready from day one.
Who Should Apply?
This program is ideal for recent 10+2 graduates with a strong aptitude for mathematics and a keen interest in technology and problem-solving. It also caters to individuals looking to launch their careers in the high-demand field of data science and analytics, offering a comprehensive curriculum that builds skills from the ground up, making it suitable for those without prior advanced programming experience but with a logical mindset.
Why Choose This Course?
Graduates of this program can expect to pursue dynamic career paths as Data Analysts, Business Intelligence Developers, Machine Learning Engineers, or Jr. Data Scientists within India''''s thriving tech ecosystem. Entry-level salaries typically range from INR 4-7 LPA, with experienced professionals potentially earning INR 10-20+ LPA. The program''''s focus on practical skills and industry tools aligns with roles in major Indian startups and multinational corporations.
Student Success Practices
Foundation Stage
Master Programming Fundamentals Early- (Semester 1-2)
Dedicate significant time to Python programming (BADSC102, BADSC105, BADSC203, BADSC206) and data structures (BADSC201, BADSC204). Consistently solve coding problems on platforms to solidify logic and syntax.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Python documentation, Jupyter Notebook
Career Connection
Strong programming skills are the bedrock for any data analytics role, crucial for data manipulation, algorithm implementation, and scripting tasks during internships and initial jobs.
Build a Strong Statistical & Mathematical Base- (Semester 1-2)
Pay close attention to Discrete Mathematics (BADSC103) and Probability and Statistics (BADSC202). Regularly practice problem-solving to internalize concepts, as these form the theoretical backbone for machine learning and statistical modeling.
Tools & Resources
Khan Academy, NPTEL courses on probability and statistics, Statistical software (R/Python libraries)
Career Connection
A solid grasp of statistics is essential for interpreting data, validating models, and making informed business decisions, highly valued by analytics firms.
Engage in Peer Learning and Group Projects- (Semester 1-2)
Form study groups to discuss complex topics and work collaboratively on assignments and labs. Participating actively in group projects fosters teamwork, communication, and problem-solving skills, mirroring real-world work environments.
Tools & Resources
Google Meet, Microsoft Teams, GitHub for collaborative coding, DSEU''''s internal project platforms
Career Connection
Collaboration and communication are soft skills highly sought after by employers, crucial for working effectively in data teams.
Intermediate Stage
Dive Deep into Machine Learning Applications- (Semester 3-5)
For Introduction to Machine Learning (BADSC401) and Deep Learning (BADSC501), implement algorithms from scratch and apply them to diverse datasets. Participate in Kaggle competitions to gain practical experience and showcase skills.
Tools & Resources
Kaggle, Google Colab, Scikit-learn, TensorFlow, Keras, PyTorch, UCI Machine Learning Repository
Career Connection
Demonstrating hands-on ML expertise is critical for roles as ML Engineers, Data Scientists, and AI Specialists, directly impacting placement success in Indian tech companies.
Master Big Data Technologies and Tools- (Semester 3-5)
Focus heavily on Big Data Analytics (BADSC403), Big Data Tools & Technologies (BADSC502), and their respective labs. Acquire certifications in Hadoop, Spark, or cloud platforms (AWS, Azure, GCP) if possible.
Tools & Resources
Apache Hadoop, Apache Spark, Google Cloud Platform (GCP), Amazon Web Services (AWS) certifications, Coursera courses on Big Data
Career Connection
Proficiency in Big Data technologies is a key differentiator for roles in data engineering, data architecture, and large-scale data processing within e-commerce, finance, and telecommunications sectors.
Seek Industry Internships and Live Projects- (Semester 4-5)
Actively apply for internships after Semester 4 or 5, leveraging DSEU''''s industry connections (BADSC507 Industrial Training / Project). Even short-term live projects with startups can provide invaluable real-world experience and a strong resume builder.
Tools & Resources
DSEU Placement Cell, LinkedIn, Internshala, Company career pages, Startup incubators
Career Connection
Internships offer practical exposure, networking opportunities, and often lead to pre-placement offers, significantly enhancing employability and career launch in India.
Advanced Stage
Build a Comprehensive Capstone Project Portfolio- (Semester 6)
The Capstone Project (BADSC603) should be a well-documented, end-to-end solution to a real-world problem. Focus on a niche area or an industry domain of interest to showcase deep specialization.
Tools & Resources
GitHub for code, Medium/LinkedIn for project documentation/blogging, Data visualization tools (Tableau, Power BI)
Career Connection
A strong capstone project acts as a portfolio, demonstrating problem-solving capabilities and technical prowess to potential employers, especially critical for startups and consulting roles.
Specialize through Electives and Advanced Learning- (Semester 6 and beyond)
Thoughtfully choose Discipline Specific Electives (BADSC604) that align with your career aspirations. Supplement this with advanced online courses or workshops in areas like AI Ethics, MLOps, or specific industry analytics.
Tools & Resources
NPTEL, Coursera, Udemy, edX, Industry workshops, Professional body memberships
Career Connection
Specialization makes you a valuable asset in niche roles and demonstrates a commitment to continuous learning, crucial for career progression in a competitive Indian market.
Network and Prepare for Placements- (Semester 6)
Attend industry seminars, conferences, and DSEU''''s career fairs. Polish your resume, practice technical and HR interviews, and actively engage with alumni. Understand the Indian job market trends and compensation expectations.
Tools & Resources
LinkedIn, DSEU Alumni network, Placement workshops, Mock interviews, Glassdoor, AmbitionBox for salary insights
Career Connection
Effective networking and thorough preparation are paramount for securing desirable placements and building a professional trajectory in India''''s dynamic job landscape.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 examination from a recognized Board with minimum 50% marks (45% for SC/ST/PwD candidates) in aggregate with Mathematics as one of the subjects.
Duration: 6 semesters / 3 years
Credits: 132 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BADSC101 | Introduction to Data Science | Core Theory | 4 | Introduction to Data Science, Data Management, Data Visualization, Exploratory Data Analysis, Statistical Inference, Supervised and Unsupervised Learning |
| BADSC102 | Programming for Data Science | Core Theory | 4 | Introduction to Python, Data Types and Variables, Control Flow, Functions and Modules, Object-Oriented Programming, File Handling |
| BADSC103 | Discrete Mathematics | Core Theory | 4 | Set Theory, Relations and Functions, Logic and Proof, Combinatorics, Graph Theory, Recurrence Relations |
| BADSC104 | Data Science Lab | Core Lab | 2 | Python Programming, Data Manipulation, Data Visualization, Basic Statistical Analysis |
| BADSC105 | Programming for Data Science Lab | Core Lab | 2 | Python Programming, Data Structures implementation, Control flow exercises, Function usage, File I/O |
| BACC101 | Effective Communication | Ability Enhancement Compulsory Course | 2 | Communication Process, Listening Skills, Speaking Skills, Reading Skills, Writing Skills, Presentation Skills |
| BACC102 | Environmental Studies | Ability Enhancement Compulsory Course | 2 | Natural Resources, Ecosystems, Biodiversity, Environmental Pollution, Social Issues and the Environment, Human Population and Environment |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BADSC201 | Data Structures & Algorithms | Core Theory | 4 | Introduction to Data Structures, Arrays, Linked Lists, Stacks, Queues, Trees, Graphs, Sorting Algorithms, Searching Algorithms |
| BADSC202 | Probability and Statistics | Core Theory | 4 | Probability Theory, Random Variables, Probability Distributions, Sampling Distributions, Hypothesis Testing, Correlation and Regression |
| BADSC203 | Object Oriented Programming | Core Theory | 4 | OOP Concepts, Classes and Objects, Inheritance, Polymorphism, Encapsulation, Abstraction, Exception Handling |
| BADSC204 | Data Structures & Algorithms Lab | Core Lab | 2 | Implementation of Data Structures, Sorting and Searching algorithms in Python/C++ |
| BADSC205 | Database Management System | Core Theory | 4 | DBMS Concepts, Relational Model, SQL, Normalization, Transaction Management, Concurrency Control, Database Security |
| BADSC206 | Object Oriented Programming Lab | Core Lab | 2 | OOP implementation in Python/Java, Class design, Inheritance, Polymorphism exercises |
| BADSC207 | Database Management System Lab | Core Lab | 2 | SQL queries, Database design, Data definition and manipulation language commands |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BADSC301 | Statistical Methods for Data Science | Core Theory | 4 | Probability Distributions, Hypothesis Testing, ANOVA, Non-parametric Tests, Correlation and Regression, Time Series Analysis |
| BADSC302 | Linear Algebra & Optimization | Core Theory | 4 | Vector Spaces, Matrices, Eigenvalues and Eigenvectors, Linear Transformations, Optimization Techniques, Linear Programming |
| BADSC303 | Data Mining & Warehousing | Core Theory | 4 | Data Warehouse Architecture, OLAP, Data Preprocessing, Association Rule Mining, Classification, Clustering, Outlier Analysis |
| BADSC304 | Statistical Methods for Data Science Lab | Core Lab | 2 | Statistical computing with R/Python, Numpy, Pandas, Hypothesis testing, Regression analysis |
| BADSC305 | Data Mining & Warehousing Lab | Core Lab | 2 | Data Mining tools (e.g., Weka), Implementation of Classification algorithms, Clustering algorithms |
| BADSC306 | Discipline Specific Elective - 1 (Theory) | Elective Theory | 4 | Choices: BADSC306A Big Data Technologies, BADSC306B Data Visualization, BADSC306C Cloud Computing. |
| BADSC307 | Discipline Specific Elective - 1 (Lab) | Elective Lab | 2 | Lab practice relevant to chosen elective from BADSC306 |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BADSC401 | Introduction to Machine Learning | Core Theory | 4 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Model Evaluation, Feature Engineering, Ensemble Methods |
| BADSC402 | Research Methodology | Core Theory | 4 | Research Design, Data Collection Methods, Sampling Techniques, Data Analysis, Report Writing, Research Ethics |
| BADSC403 | Big Data Analytics | Core Theory | 4 | Big Data Concepts, Hadoop Ecosystem, Spark Framework, NoSQL Databases (MongoDB, Cassandra), Stream Processing |
| BADSC404 | Introduction to Machine Learning Lab | Core Lab | 2 | Implementation of ML algorithms using Python (Scikit-learn), Data Preprocessing, Model Training, Evaluation metrics |
| BADSC405 | Big Data Analytics Lab | Core Lab | 2 | Hands-on with Hadoop MapReduce, Spark RDDs/DataFrames, HiveQL queries, Pig scripts |
| BADSC406 | Discipline Specific Elective - 2 (Theory) | Elective Theory | 4 | Choices: BADSC406A Natural Language Processing, BADSC406B Computer Vision, BADSC406C Business Intelligence. |
| BADSC407 | Discipline Specific Elective - 2 (Lab) | Elective Lab | 2 | Lab practice relevant to chosen elective from BADSC406 |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BADSC501 | Deep Learning | Core Theory | 4 | Neural Network Architectures, Activation Functions, Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs, Transfer Learning |
| BADSC502 | Big Data Tools & Technologies | Core Theory | 4 | Advanced Spark, Apache Kafka, Apache Flink, Data Lake Architecture, Cloud-based Big Data services, Data Orchestration |
| BADSC503 | Deep Learning Lab | Core Lab | 2 | Implementation of Deep Learning models using TensorFlow/Keras/PyTorch, Fine-tuning, Hyperparameter optimization |
| BADSC504 | Big Data Tools & Technologies Lab | Core Lab | 2 | Hands-on experience with advanced Spark features, Kafka message queues, Flink stream processing |
| BADSC505 | Discipline Specific Elective - 3 (Theory) | Elective Theory | 4 | Choices: BADSC505A Ethical Hacking, BADSC505B Blockchain Technologies, BADSC505C IoT Analytics. |
| BADSC506 | Discipline Specific Elective - 3 (Lab) | Elective Lab | 2 | Lab practice relevant to chosen elective from BADSC505 |
| BADSC507 | Industrial Training / Project | Project/Internship | 4 | Real-world problem solving, Industry environment exposure, Project report documentation, Presentation of findings |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BADSC601 | Advanced Data Analytics | Core Theory | 4 | Advanced ML techniques, Time Series Forecasting, Recommender Systems, Reinforcement Learning, AI Ethics and Governance |
| BADSC602 | Data Governance & Ethics | Core Theory | 4 | Data Privacy Laws (e.g., DPDP Bill), Data Security Best Practices, Regulatory Compliance, Ethical AI Principles, Data Lifecycle Management |
| BADSC603 | Capstone Project | Project | 6 | Comprehensive data analytics project, Problem identification, Solution design, Implementation, Evaluation, Final report and presentation |
| BADSC604 | Discipline Specific Elective - 4 (Theory) | Elective Theory | 4 | Choices: BADSC604A Quantum Computing, BADSC604B Data Storytelling, BADSC604C Financial Analytics. |
| BADSC605 | Discipline Specific Elective - 4 (Lab) | Elective Lab | 2 | Lab practice relevant to chosen elective from BADSC604 |
