Resume for Computer Science Student with Projects and Achievements

This resume showcases the education, projects, achievements, and technical proficiencies of a computer science student. It includes a career objective, key achievements, and a bibliography. The projects include exploratory analysis on a dataset for a novel recommender system, creating a user-friendly java graphical user interface, and analyzing MOOC discussion forums for relevancy. The technical proficiencies include big data, programming, database, analytical tools, and data science libraries.

Running head: RESUME
RESUME
Name of the Student:
Name of the University:
Author note:

1RESUME
Name Address Email Address Contact Number
Career Objective: To pursue a challenging career and be an important part of a progressive
organization which offers significant opportunity to improve my expertise, knowledge, skills and
to attain the pinnacle in the domain of computer and technology with sheer determination,
enthusiasm, dedication and hard work.
Education
 MSc in Computer Science (Pursuing) to completed in 2020| California State University-
Fresno, CA
 Bachelor of Technology (GPA: 8.22)| VNR Vignana Jyothi Institute of Engineering and
Technology| Computer Science and Technology
Projects and Work Done
Project 1
Role:
 Engaged as a Team Leader to execute exploratory analysis on the dataset to examine
unexpected insights in NOVEL RECOMMENDER SYSTEM for E-commerce Grocery
Shopping.
 Engaged in providing an anonymized dataset of online grocery procurement by users
Responsibilities:
 Also constructed utility matrix in order to operate collaborative filtering algorithm to
align similar orders and users that is employed to foresee the upcoming procurements by
users.

2RESUME
 Additional analysis was conducted with consideration of similar orders by using natural
language processing in order to determine the dietary value of the product for improving
recommendations.
Project 2
Role:
 Worked as a Team Leader to successfully create user-friendly java graphical user
interface.
Responsibilities:
 Primarily engaged to ONLINE DATA DE-DUPLICATION for IN-MEMORY BIG
DATA ANALYTIC SYSTEMS
 Proficiently managed providing set of files which illustrated certain level of resemblance
where an issue of executing data redundancy exclusion across range of allocated worker
nodes is taken into account.
 Engaged throughout the session of structuring partitioning scheme which proactively
allocates files to the servers in the big-data analytics cluster in digital manner.
 Successfully designed redundancy elimination system which has been efficiently
positioned in space-effective and access-isolated place.
 Primarily involved in successful implementation of preliminary and incremental
algorithms in method which will help the optimal amount of space to be utilized in order
to store files in the system.
Project 3
Role:
 Engaged as an Analyst of MOOC DISCUSSION FORUMS FOR RELEVANCY

3RESUME
Responsibilities:
 Successfully created a dataset by employing various queries from diverse array of
discussion forums to draw comparison to the application of various responses to each
query
 Critically analysed complexions of foreseeing implication of those responses by taking
into consideration various answers to query in the MOOC discussion forum
 Strategically developed datasets based on several elements essential to foresee
implication
 Tactically predicted relevance of response received based on the classifier model
established
Key Achievements
 Will present Research paper on “Novel Recommender System for healthy Grocery
Shopping”. The research topic has been approved by the Future of Information and
Communications Conference (FICC) 2020- will take place in 2020| San Francisco
 Conducted Experimental Study with Python on Foreseeing Emotional Attributes of
Humans with the utilization of brain EEG signals
 Conducted Predictive Analysis in order to calculate the types of medication for precise
type of disease by using Natural Language Processing, SVM and Naïve Bayes
Technical Proficiencies
1. Big data: MapReduce, Hive, Spark, Hadoop, Sqoop
2. Programming: Python, HTML, C, CSS, C++, Java, JavaScript, Perl
3. Database: Cassandra, SQL, MongoDB, MySQL

4RESUME
4. Analytical Tools: MS Excel, R, Tableau, SAS
5. Data Science Libraries: Tensorflow, Numpy, Scikit-learn, Keras

5RESUME
Bibliography
Deb, D., Cousins, S. and Fuad, M., 2018, May. Teaching Big Data and Cloud Computing: A
Modular Approach. In 2018 IEEE International Parallel and Distributed Processing Symposium
Workshops (IPDPSW) (pp. 377-383). IEEE.
Govender, D.W. and Basak, S.K., 2015. An investigation of factors related to self-efficacy for
java programming among computer science education students. Journal of Governance and
Regulation, 4(4), pp.612-619.
Shankaralingappa, D.M., De Fransicsi Morales, G. and Gionis, A., 2016, October. Extracting
skill endorsements from personal communication data. In Proceedings of the 25th ACM
International on Conference on Information and Knowledge Management (pp. 1961-1964).
ACM.
Yang, T.C., Hwang, G.J., Yang, S.J. and Hwang, G.H., 2015. A two-tier test-based approach to
improving students' computer-programming skills in a web-based learning environment. Journal
of Educational Technology & Society, 18(1), pp.198-210.