Introduction
With the current data deluge, companies, governments, and non-profit organizations alike are striving to convert information into actionable information and insight. The sheer “volume”, “velocity” and “variety” of today’s data pose unique challenges and also creates unique opportunities. Present data sets require more programming, mathematics/statistics, modelling skills, and domain knowledge than a traditional undergraduate curriculum offers.
In every facet of modern life, from online shopping and social networks to scientific research and finance, we collect immensely detailed information. Data scientists are concerned with turning this data into intelligence through the application of cutting-edge techniques in Statistics, Mathematics and Computer Science.
Global demand for combined statistical and computing expertise outstrips supply, with evidence-based predictions of a major shortage in this area for at least the next 15 years. For graduates of Data Science, this shortage presents opportunities to forge careers in a large number of areas involving quantitative data analysis and computational skills. These include commerce (e-commerce), finance, government, genomics, and other areas of “big science”, entertainment and sport, education, and academic research. Career opportunities include business intelligence analyst, data mining engineer, data architect and data scientist. Graduates will also be highly adaptable to new data-related challenges as they arise, perhaps in hitherto unforeseen fields.
In line with the guidelines provided by HEC Pakistan, the BS (Data Science) program has been designed in such a way that it focuses on computation, simulation, visualization, prediction of complex phenomena (e.g., customer behavior, economic trends, and medical data) and complex mathematical models to facilitate interpretation of data. The Center of Excellence in IT (CEIT) at IMSciences has highly research-active faculty, who encourage students to be involved in their applied/research work. BS-Data Science degree is excellent preparation for the job market of the future and Data Science majors take up careers in every imaginable field. Our graduates have enjoyed excellent job placements, both within Pakistan and internationally. Many have chosen to make their own successful companies.
Program Structure
BS (Data Science) has a dual emphasis on basic principles of statistics and computer science, with foundational training in statistical and mathematical aspects of data analysis. This program develops foundation on broad computer science principles, including algorithms, data structures, data management and machine learning. This program will prepare graduates for a career in data analysis, combining foundational statistical concepts with computational principles from computer science.
Eligibility Criteria
- FA/F. Sc or Equivalent qualifications with at least second division, securing 50% marks in aggregate.
- The students who have not studied Mathematics at intermediate level must pass deficiency courses of Mathematics of 6 credit hours within one year of their regular studies.
- Qualifying the admission test and interview is compulsory. A candidate scoring less than 40% marks in the test and interview will stand disqualified for admission.
- Candidates who have secured at least 40% in the NTS-NAT are also eligible to apply.
- The merit of a candidate shall be measured by a 50 % weight age to the marks obtained in HSC or equivalent, 40 % to the marks obtained in the written test, and 10% to the marks obtained in the interview.
- A candidate shall be given a special credit of thirty marks for admission in each program mentioned above if he/she has studied Computer Science and/or statistics at intermediate level (for BS-Data Science program only) at intermediate level or has done A level.
- The Hafiz Quran shall be given a special credit of 20 marks.
- The credit marks shall be added to the marks obtained at HSC or equivalent, subject to fulfilment of basic eligibility criteria of 50% marks.
Degree Requirements
For a BS-Data Science 4-year degree, a student is required to complete a minimum of 130-140 credit hours including a 6-credit hour research thesis/project. The normal duration for completion of BS-Data Science degree is 8 semesters over a period of 4 years. The maximum duration for obtaining BS-Data Science degree shall be 7 years.
Program Education Objectives (PEOs)
Following are the Program Education Objectives (PEOs) of BS-Data Science.
- Knowledge of how to apply analytic techniques and algorithms (including statistical and data mining approaches) to large data sets to extract meaningful insights.
- Acquisition of hands-on experience with relevant software tools, languages, data models, and environments for data processing and visualization.
- Ability to communicate results of analysis effectively (visually and verbally) to a broad audience.
- Ability to extract useful knowledge from data in various forms that help drive evidence-based decisions.
- To prepare students to stand out in one of the world’s fastest growing careers.
Program Learning Outcomes (PLOs) of BS-Data Science
- Completion of an accredited program of study designed to prepare graduates as Data Science professionals (Academic Education).
- Apply knowledge of mathematics, statistics, natural sciences, computing fundamentals, and a data specialization to the solution of complex data science problems. (Computing and Data Science Knowledge).
- Identify, formulate, research literature, and analyze/solve complex computing problems reaching substantiated conclusions using fundamental principles of mathematics, statistics, computing sciences, and relevant domain disciplines (Problem Analysis).
- Design and evaluate solutions for complex computing problems, and design and evaluate systems, components, or processes that meet specified needs with appropriate consideration for public health and safety, cultural, societal, and environmental considerations (Design/ Development of Solutions)
- Create, select, adapt, and apply appropriate techniques, resources, and modern computing/data science tools including prediction and modelling for complex data science problems. (Modern Tool Usage)
- Function effectively as an individual and as a member or leader in diverse teams and in multi-disciplinary settings (Individual and Teamwork)
- Communicate effectively with the computing community and with society about complex computing/data science activities by being able to comprehend and write effective reports, design documentation, make effective presentations, and give and understand clear instructions (Communication)
- Understand and assess societal, health, safety, legal, and cultural issues within local and global contexts, and the consequential responsibilities relevant to professional computing practice (Computing Professionalism and Society)
- Understand and commit to professional ethics, responsibilities, and norms of professional computing practice (Ethics)
- Recognize the need, and have the ability, to engage in independent learning for continual development as a computing professional (Lifelong Learning)
Upon completion of BS-Data Science degree, all the students should have attained the aforementioned ten SOs.
Curriculum for BS-Data Science
Following are the proposed areas, which are required to cover to complete the degree. Covered areas consist of core courses (compulsory), foundation courses, general courses, and electives.
AREAS COVERED IN BS-DATA SCIENCE
COMMON COURSES |
|||
| Course Group |
Min. No. of Credit Hours |
Min. No. of Courses |
Percentage |
| General Education |
19 |
7 |
14.2 % |
| Mathematics & Science Foundation |
15 |
5 |
11.2 % |
| Computing Core |
39 |
11 |
29.3 % |
| Institute Electives |
12 |
4 |
09.0 % |
| Common Courses |
85 |
27 |
64.0 % |
DOMAIN COURSES |
|||
| Computer Science Core |
18 |
5 |
13.5 % |
| DS Core (Domain Core) |
18 |
6 |
13.5 % |
| DS Electives (Domain Electives) |
12 |
4 |
09.0 % |
| Domain Courses |
48 |
15 |
36.0 % |
|
TOTAL |
133 |
42 |
100% |
General Education Courses
|
Course Code |
Course Title |
Credit Hours |
Contact Hours |
| CSC 301 | Introduction to Information & Communication Technologies |
3 (2-1) |
2-3 |
| ENG 301 | English (General) |
3 (3-0) |
3-0 |
| ENG 302 | English (Functional) |
3 (3-0) |
3-0 |
| ENG 401 | English (Academic) |
3 (3-0) |
3-0 |
| HSS 305 | Fundamentals of Islamic Studies/ Ethics |
2 (2-0) |
2-0 |
| HSS 301 | Fundamentals of Pakistan Studies |
2 (2-0) |
2-0 |
| CSC 595 | Professional Practices |
3 (3-0) |
3-0 |
| TOTAL |
19 (18-1) |
18-3 |
|
Mathematics and Science Foundation Courses
|
Course Code |
Course Title |
Credit Hours |
Contact Hours |
| MTH 311 | Calculus & Analytical Geometry |
3 (3-0) |
3-0 |
| MTH 315 | Linear Algebra |
3 (3-0) |
3-0 |
| STA 415 | Probability & Statistics |
3 (2-1) |
2-3 |
| MTH 505 | Differential Equations |
3 (3-0) |
3-0 |
| PHY | Applied Physics |
3 (3-0) |
3-0 |
|
TOTAL |
15 (14-1) |
14-3 |
|
Computing Core Courses
|
Course Code |
Course Title |
Credit Hours |
Contact Hours |
| CSC 305 | Programming Fundamentals |
4 (3-1) |
3-3 |
| CSC 321 | Discrete Structures |
3 (3-0) |
3-0 |
| CSC 315 | Object Oriented Programming |
4 (3-1) |
3-3 |
| CSC 451 | Database Systems |
4 (3-1) |
3-3 |
| CSC 401 | Data Structures & Algorithms |
4 (3-1) |
3-3 |
| CSC 556 | Information Security |
3 (3-0) |
3-0 |
| CSC 575 | Computer Networks |
4 (3-1) |
3-3 |
| CSC 465 | Operating Systems |
4 (3-1) |
3-3 |
| SWE 401 | Software Engineering |
3 (3-0) |
3-0 |
| FYP 611 | Final Year Project – I |
3 (0-3) |
0-9 |
| FYP 612 | Final Year Project – II |
3 (0-3) |
0-9 |
| TOTAL |
39 (27-12) |
27-36 |
|
Institute Elective Courses
(Must be any FOUR courses or 12 credit hours, not limited to the areas listed below, Institutions may add/replace courses)
| Course Code |
Course Title |
Credit Hours |
Contact Hours |
| ACC 301 | Fundamentals of Accounting |
3 (3-0) |
3-0 |
| BUS 301 | Introduction to Business |
3 (3-0) |
3-0 |
| ENI 301 | Entrepreneurship |
3 (3-0) |
3-0 |
| FIN 301 | Fundamentals of Business Finance |
3 (3-0) |
3-0 |
| HRM 301 | Fundamentals of Human Resource Management |
3 (3-0) |
3-0 |
| HSS 311 | Fundamentals of Sociology |
3 (3-0) |
3-0 |
| HSS 415 | Fundamentals of Psychology |
3 (3-0) |
3-0 |
| HSS 505 | Logic and Critical Thinking |
3 (3-0) |
3-0 |
| MGT 301 | Principles of Management |
3 (3-0) |
3-0 |
| POL 301 | Introduction to Political Science |
3 (3-0) |
3-0 |
| POL 501 | International Relations |
3 (3-0) |
3-0 |
| LAN 512 | Regional Language (Pashto) |
3 (3-0) |
3-0 |
| LAN 513 | Regional Language (Sindhi) |
3 (3-0) |
3-0 |
| LAN 514 | Regional Language (Punjabi) |
3 (3-0) |
3-0 |
| LAN 521 | Foreign Language (French) |
3 (3-0) |
3-0 |
| LAN 522 | Foreign Language (Chinese) |
3 (3-0) |
3-0 |
| LAN 523 | Foreign Language (German) |
3 (3-0) |
3-0 |
| LAN 524 | Foreign Language (Persian) |
3 (3-0) |
3-0 |
| CSC 550 | Computing and Society |
3 (3-0) |
3-0 |
| TOTAL |
12 (12-0) |
12-0 |
|
Domain Courses for BS-Data Science
Computer Science CORE Courses
| Course Code | Course Title | Credit Hours | Contact Hours |
| CSC 601 | Artificial Intelligence |
4 (3-1) |
3-3 |
| CSC 405 | Digital Logic Design |
4 (3-1) |
3-3 |
| CSC 531 | Design and Analysis of Algorithms |
3 (3-0) |
3-0 |
| CSC 411 | Computer Organization & Assembly Language |
4 (3-1) |
3-3 |
| SWE 539 | Parallel & Distributed Computing |
3 (3-0) |
3-0 |
| TOTAL |
18 (15-3) |
15- 9 |
|
Data Science CORE Courses
| Course Code | Course Title | Credit Hours | Contact Hours |
| STA 421 | Advanced Statistics |
3 (2-1) |
2-3 |
| DSC 301 | Introduction to Data Science |
3 (2-1) |
2-3 |
| CSC 661 | Data Mining |
3 (2-1) |
2-3 |
| DSC 635 | Data Visualization |
3 (2-1) |
2-3 |
| DSC 625 | Data Warehousing & Business Intelligence |
3 (2-1) |
2-3 |
| DSC 642 | Big Data Analytics |
3 (2-1) |
2-3 |
| TOTAL |
18 (12-6) |
12-18 |
|
Data Science ELECTIVES Courses
(Must be any FOUR courses or 12 credit hours, not limited to the areas listed below, Institute may add/replace courses)
| Course Code | Course Title |
Credit Hours |
Contact Hours |
| DSC 525 | Social Network Analysis |
3 (3-0) |
3-0 |
| DSC 528 | Pattern Recognition |
3 (3-0) |
3-0 |
| DSC 531 | Econometrics for Big Data Analysis – I |
3 (2-1) |
2-3 |
| DSC 551 | Statistical Thinking for Data Science & Analytics |
3 (2-1) |
2-3 |
| DSC 601 | Predictive Analytics for Business |
3 (2-1) |
2-3 |
| CSC 611 | Advanced Database Systems |
3 (3-0) |
3-0 |
| CSC 685 | Machine Learning |
3 (2-1) |
2-3 |
| DSC 675 | Deep Learning and Applications |
3 (3-0) |
3-0 |
| CSC 501 | Theory of Automata |
3 (3-0) |
3-0 |
| CSC 605 | Artificial Neural Networks |
3 (3-0) |
2-3 |
| DSC 541 | Business Process Management |
3 (3-0) |
3-0 |
| CSC 618 | Speech Processing |
3 (3-0) |
3-0 |
| CSC 631 | Cloud Computing |
3 (3-0) |
3-0 |
| CSC 619 | Text Mining |
3 (3-0) |
3-0 |
| DSC 521 | Topics in Data Science |
3 (3-0) |
3-0 |
| CSC 453 | Fundamentals of Internet of Things (IoT) |
3 (3-0) |
3-0 |
| CSC 637 | Selected Topics in Internet of Things (IoT) |
3 (3-0) |
3-0 |
| CSC 571 | Mobile Application Development |
3 (3-0) |
3-0 |
| CSC 505 | Real-Time Systems |
3 (3-0) |
3-0 |
| CSC 551 | E-Commerce |
3 (3-0) |
3-0 |
|
TOTAL (Any four courses or 12 credit hours) |
12 (x-x) |
x-x |
|
BS-Data Science – Semester-wise Breakdown
4-Year Program (8 Regular Semester of 18 weeks each) (133 Credit Hours)
Semester 1 |
||||
|
Course Code |
Course Title |
Credit Hours |
Contact Hours |
Pre-requisite |
|
CSC 301 |
Introduction to Information and Communication Technologies |
3 (2-1) |
2-3 |
|
|
CSC 305 |
Programming Fundamentals |
4 (3-1) |
3-3 |
|
|
ENG 301 |
English (General) |
3 (3-0) |
3-0 |
|
|
HSS 301 |
Fundamental of Pakistan Studies |
2 (2-0) |
2-0 |
|
|
MTH 311 |
Calculus and Analytical Geometry |
3 (3-0) |
3-0 |
|
|
PHY 305 |
Applied Physics |
3 (3-0) |
3-0 |
|
| Total |
18(16-2) |
16-6 |
|
|
Semester 2 |
||||
|
Course Code |
Course Title |
Credit Hours |
Contact Hours |
Pre-requisite(s) |
|
CSC 315 |
Object Oriented Programming |
4 (3-1) |
3-3 |
Programming Fundamentals |
|
CSC 321 |
Discrete Structures |
3 (3-0) |
3-0 |
|
|
ENG 302 |
English (Functional) |
3 (3-0) |
3-0 |
English (General) |
|
HSS 305 |
Fundamentals of Islamic Studies |
2 (2-0) |
2-0 |
|
|
MTH 315 |
Linear Algebra |
3 (3-0) |
3-0 |
Calculus and Analytical Geo. |
|
DSC 301 |
Introduction to Data Science |
3 (2-1) |
2-3 |
|
|
Total |
18(16-2) |
16-6 |
|
|
Semester 3 |
||||
|
Course Code |
Course Title |
Credit Hours |
Contact Hours |
Pre-requisite |
|
CSC 401 |
Data Structures and Algorithms |
4 (3-1) |
3-3 |
Programming Fundamentals |
|
CSC 405 |
Digital Logic Design |
4 (3-1) |
3-3 |
Applied Physics |
|
STA 415 |
Probability and Statistics |
3 (2-1) |
2-3 |
|
|
MTH 505 |
Differential Equations |
3 (3-0) |
3-0 |
Calculus and Analytical Geo. |
|
SWE 401 |
Software Engineering |
3 (3-0) |
3-0 |
|
|
Total |
17(14-3) |
14-9 |
|
|
Semester 4 |
||||
|
Course Code |
Course Title |
Credit Hours |
Contact Hours |
Pre-requisite |
|
CSC 411 |
Computer Organization & Assembly Language |
4 (3-1) |
3-3 |
DLD, Prog. Fundamentals |
|
CSC 451 |
Database Systems |
4 (3-1) |
3-3 |
|
|
CSC 465 |
Operating Systems |
4 (3-1) |
3-3 |
Data Structures & Algo. |
|
CSC 601 |
Artificial Intelligence |
4 (3-1) |
3-3 |
OOP, Data Structure & Algo. |
|
STA 421 |
Advanced Statistics |
3 (2-1) |
2-3 |
Probability and Statistics |
|
Total |
19(14-5) |
14-15 |
|
|
Semester 5 |
||||
|
Course Code |
Course Title |
Credit Hours |
Contact Hours |
Pre-requisite |
|
CSC 661 |
Data Mining |
3 (2-1) |
2-3 |
Adv. Stat, Intro. to DS |
|
ENG 401 |
English (Academic) |
3 (3-0) |
3-0 |
English (Functional) |
|
CSC 531 |
Design and Analysis of Algorithms |
3 (3-0) |
3-0 |
Data Structures & Algo. |
|
CSC 575 |
Computer Networks |
4 (3-1) |
3-3 |
|
|
DSC 625 |
Data Warehousing & Business Intelligence |
3 (2-1) |
2-3 |
Intro. to Data Science |
|
– |
Institute Elective – I |
3 (3-0) |
3-0 |
|
|
Total |
19(16-3) |
16-9 |
|
|
Semester 6 |
||||
|
Course Code |
Course Title |
Credit Hours |
Contact Hours |
Pre-requisite |
|
CSC 556 |
Information Security |
3 (3-0) |
3-0 |
|
|
SWE 539 |
Parallel and Distributed Computing |
3 (3-0) |
3-0 |
OOP, Operating Systems |
|
DSC 635 |
Data Visualization |
3 (2-1) |
2-3 |
Data Warehouse & BI |
|
– |
Institute Elective – II |
3 (3-0) |
3-0 |
|
|
– |
DS Elective – I |
3 (x-x) |
x-x |
|
|
Total |
15(x-x) |
x-x |
|
|
Semester 7 |
||||
|
Course Code |
Course Title |
Credit Hours |
Contact Hours |
Pre-requisite |
|
DSC 642 |
Big Data Analytics |
3 (2-1) |
2-3 |
Intro. to DS., Prob. & Stat., Prog. Fundamentals |
| Institute Elective – III |
3 (3-0) |
3-0 |
||
| DS Elective – II |
3 (x-x) |
x-x |
||
| DS Elective – III |
3 (x-x) |
x-x |
||
|
FYP 611 |
Final Year Project – I |
3 (0-3) |
0-9 |
|
|
Total |
15(x-x) |
x-x |
|
|
Semester 8 |
||||
|
Course Code |
Course Title |
Credit Hours |
Contact Hours |
Pre-requisite |
|
CSC 595 |
Professional Practices |
3 (3-0) |
3-0 |
|
| Institute Elective – IV |
3 (3-0) |
3-0 |
||
| DS Elective – IV |
3 (x-x) |
x-x |
||
|
FYP 612 |
Final Year Project – II |
3 (0-3) |
0-9 |
Final Year Project – I |
|
Total |
12(x-x) |
x-x |
|
|
