ALL ABOUT THE DEPARTMENT

ABOUT DEPARTMENT

The Department of CSE (Data Science) was established in the year 2020 with a sanctioned intake of 60 to offer B. Tech course. Data Science is transforming everything around us. Data Science is an interdisciplinary field with the ability to extract knowledge/insights from data – be it structured, unstructured, or semi-structured data. In today’s technical world where data is growing exponentially, data science ensures that the huge incoming data is properly handled, efficiently analyzed and extracts useful knowledge for business development.The Data Science Department delivers leading-edge, innovative methods for solving data-intensive science problems.

The basic objectives of this course are to train the students to acquire the skills to perform intelligent data analysis which is a key component in numerous real-world applications. With the tremendous amount of data generated every day and the computing power available, Data Science plays a seminal role in helping every business organization in identifying business trends and changes through advanced Big Data Analytics.

Highly qualified and experienced faculty:  The department has five teaching faculty with multidisciplinary expertise. Two of the faculty have completed their doctorate, and one pursuing Ph.D. Three faculty members are pursuing Diploma on Artificial Intelligence and MachineLearning at University of Hyderabad, Hyderabad(HCU).

Duration:4 years (Regular) / 3 years (Lateral Entry)

No. of Semesters:8 (Regular) / 6 (Lateral Entry)

No. of Seats:Total – 60 ( NRI Approval Status – Yes )

Eligibility:10+2 system of Education. Must have secured a pass in Physics, Chemistry and Mathematics in the qualifying examination.

Scope for Higher Studies:M.E. / M.Tech / M.B.A./ M.S.

VISION of The Department 

To produce excellent standard, quality education of professionals by imparting cognitive learning environment, research and industrial orientation to become innovative Data Science Professional.

MISSION of The Department 

  • To develop professionals in the areas of math’s (probability and statistics, liner algebra and Calculus), natural language processing, text mining, and problem solving.
  • To educate the students with latest technologies to update their knowledge in the field of AI and Data science.
  • To impart quality and value based education and contribute towards the innovation of computing system, data science to raise satisfaction level of all stakeholders
  • Enabling students to get expertise in critical skills with data science education and facilitate socially responsive research and innovation.
  • Our effort is to apply new advancements in high performance computing hardware and software

OBJECTIVES

PROGRAM EDUCATIONAL OBJECTIVES

  • To introduce the fundamentals of science and engineering concepts essential for a data architect / data scientist.
  • To inculcate the knowledge of mathematical foundations and algorithmic principles for effective problem solving.
  • To provide knowledge in data science for modern computational data analysis and modeling methodologies.
  • To provide the knowledge in artificial intelligence techniquesand apply them to develop relevant models and real time products.
  • To impart knowledge to analyze, design, test and implement the model required for various applications.
  • To hone personality skills, trigger social commitment and inculcate societal responsibilities.

PROGRAM OUTCOMES (POs):

  • PO1: Analyze and build models applying the knowledge of mathematics, statistics,electronic, electrical and computer science discipline and solve the problem.
  • PO2: Identify the sources of information for data collection, design and conduct the experiments and interpret the result.
  • PO3: Think out-of-the box and solve the real time problems using their creativity in designing human friendly software systems.
  • PO4: Comprehend computer engineering concepts of the new research developments and apply them to develop relevant software and hardware products.
  • PO5: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of the limitations.
  • PO6: Apply the computing knowledge to solve the socially relevant problems.
  • PO7: Understand the impact of engineering solutions in global, economic, environmental, societal context and apply it in exploring the new developments, research trends and involve them in research.
  • PO8: Develop professional integrity by understanding and appreciating professional, legal, ethical, cyber security and related issues and act with responsibility.
  • PO9: Communicate, collaborate and work as a team by involving in the group projects of multi-disciplinary nature.
  • PO10: To prepare documents as per the standards and present effectively to improve software documentation skills.
  • PO11: Apply the hardware and software project management techniques to estimate the time and human resources required to complete computer engineering projects.
  • PO12: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change

PROGRAM SPECIFIC OUTCOMES (PSOs):

  •  PSO1: Understand, analyze and develop essential proficiency in the areas related to data science in terms of underlying statistical and computational principles and apply the knowledge to solve practical problems.
  • PSO2: Implement  data science techniques such as search  algorithms, neural networks, machine learning and data analytics for solving a  problem and designing novel algorithms for successful career and entrepreneurship.

OBJECTIVES:

  • To equip the students with strong fundamental concepts, analytical capability, programming and problem solving skills.
  • To create an academic environment conducive for higher learning through student training, self learning, sound academic practices and research
    endeavors.
  • To make the students industry ready and to enhance their employability through training and internships. To make  students job-ready by applying what you learn and building real-life projects
  • To improve department industry collaboration through interaction including participation in professional society activities, guest lecturers and industrial visit.
  • To provide opportunities in order to promote organizational and leadership skills in students through various co-curricular and extra – curricular activities

ABOUT THE COURSE

About the Course

This course also has many advantages on learning the concepts such as analysis of big data, handling large amounts of data, operations of large amounts of data efficiently, etc. will be a benefit in learning the concepts of this All in One Data Science course including a lot of data operations tools, IDEs, frameworks, and techniques which will be of more useful in learning the many other concepts related to Data Science. The course curriculum starts by introducing you with Building Blocks of Data Science covering data science foundations, concepts, and basic programming elements. The next stage covers Data Visualization and Analytics (Excel, SQL & Tableau) elaborating on data extraction, manipulation, analysis, reporting, and building intuitive business dashboards.

Top Programming languages for Data Science:

  • Python
  • R Programming
  • Java script
  • Java
  • SQL
  • Scala
  • Key Areas of Data Science

IDE:

  • Pycharm
  • Jupyter

Data Analysis:

  • FE
  • Data Wrangling
  • EDA (explore data analysis)

Data Visualization:

  • Tableau
  • Power BI
  • MATplotlib
  • Ggplot
  • Seaborn

WEB Scraping:

  • Beautiful Soup
  • Scrapy
  • URL LIB

Key areas of data science

1. Data Engineering and Data Warehousing

Data Engineering refers to transforming data into a useful format for analysis. This often involves managing the source, structure, quality, storage, and accessibility of the data so that it can be queried and analyzed by other analysts.

related jobs: Data Engineer, Database Developer, Data Analyst

2. Data Mining and Statistical Analysis

Data Mining refers to the application of statistics in the form of exploratory data analysis and predictive models to reveal patterns and trends in data from existing data sources. This person will be able to look at a business problem and translate it to a data question, create predictive models to answer the question and story tell about the findings.

related jobs: Data Scientist, Business Analyst, Statistician

3. Cloud and Distributed Computing

Cloud and System Architecture refers to designing and implementing enterprise infrastructure and platforms required for cloud and distributed computing. The role also analyzes system requirements and ensures that systems will be securely integrated with current applications and business uses.

related jobs: Cloud Architect, Cloud Engineer , Platform Engineer

4. Database Management and Architecture

This role is responsible for designing, deploying, and maintaining databases in support of high volume, complex data transactions for specific services or groups of services.

related jobs: Database Analyst, Database Administrator, Data Specialist

5. Business Intelligence and Strategy

Some of the key responsibilities in BI include improving back-end data sources for increased accuracy and simplicity, building tailored analytics solutions, managing dashboards, reporting to stakeholders, identifying opportunities and recognizing best practices in reporting and analysis: data integrity, test design, analysis, validation, and documentation.

related jobs: BI Engineer, BI Developer, BI Analyst, Data Strategist

6. ML / Cognitive Computing Development

This is what most people associate with data science: “making robots”. This is a larger, more complex version of data mining and statistical analysis. These people focus more on getting all the input you need to feed the model; building data pipelines, convenient data sources, A/B testing and bench marking infrastructure. When/if this is done you might focus on building the actual algorithms/models, but this part more often than not involves well known, industry standard tools and statistical techniques. These focus area has become a buzzword in many organizations though, so I encourage looking into sub-fields within it in order to truly identify what you like.

related jobs: ML Engineer, AI Specialist, Cognitive Developer, Researcher

7. Data Visualization and Presentation

Being able to present data in a visually appealing way has become part of almost every business analyst and data scientist role. When these focus area becomes an actual role in a company, their main responsibility includes creating BI solutions for teams and customers based on specific business requirements and use cases. In other instances, it can be more graphic design oriented.

related jobs: Data Viz Engineer, Data Viz Developer, Software Developer

8. Operations-Related Data Analytics

If you don’t consider yourself to be very technical yet have a passion for problem solving and processes, these might be the right path for you. These type of roles focus on leveraging the tools and data provided by the other members of the data science team in order to find opportunities of improvement within the operations of the business. These can either be focused on logistics, technology, financials, human resources, etc.

related jobs: Planning Analyst, Decisions Analyst, Communications Analyst, etc

9. Market-Related Data Analytics

These role has different levels of technical expertise depending on the level of analysis and company. These people tend to focus on more external data related to customers, sales and marketing, yet their purpose is similar to those in operations: track performance and find opportunities.

related jobs: Web Analyst, Product Analyst, Market Analyst, Sales Analyst

10. Sector-Specific Data Analytics (Healthcare, Finance, Insurance, etc.)

Lastly, if you studied Healthcare, Finance or something that requires domain-knowledge expertise to analyze, you might opt to look into simple analyst positions within organizations in these industries. Again, the technical expertise of these roles will depend on the expectations of the company hiring and the tools they use.

related jobs: Data Analyst, Business Analyst, Data Scientist — specialized

Opportunities

A field in the spotlight, data science offers high salaries and big opportunities. The field that is considered as the hottest career option of the 21st century is Data Science Data Science is encountering a surge in jobs across the whole world. India is one such country that is also experiencing a data explosion. As more and more companies are adopting data science, companies are hiring data scientists by hordes.

  1. Data Scientist
  2. Data Analyst
  3. Data Engineer
  4. Business Intelligence Developer.
  5. Data architect
  6. Statistician
  7. Business Analyst
  8. Machine Learning engineer
  9. Database Administrator

UNIQUENESS OF DATA SCIENCE

The demand for data scientists is rising exponentially every day. This is because data scientists are believed to have profound knowledge and expertise in fields like machine learning, statistics, mathematics, computing science, data visualization, and communication. Moreover, as companies witness the proliferation of data, they need to tap this resource for extracting value that shall help them boost business and help in adapting to the changing technologies in the market. This is why companies need to hire the right people with reliable data science skills. These data scientists can help manipulate vast amounts of data with sophisticated statistical and visualization techniques and predict potential outcomes and possible threats. Also, as demand increases, it presents promising career prospects for students and existing professionals.

On a typical day, a data scientist’s job includes data mining by using APIs or building ETL pipelines, data cleaning using programming languages like R or Python. They explore disparate and disconnected data sources look for better ways to analyze information. Most of the data scientists have the ability to assist businesses to interpret and manage data and solve intricate problems using expertise in a variety of data niches with correct datasets and variables. They also build models and design algorithms to mine stores of big data, to recognize patterns and trends. Later they communicate these findings to stakeholders using tools like visualization. Currently, the ‘data scientist’ is deemed as one of the sexiest jobs of the 21st century. While it is common and fundamental to have experience in R, Python, Cloud computing, machine learning, knowledge of multivariable calculus, probability and statistics, SQL, Tensorflow, Big data, and soft skills like data storytelling, good communication, business acumen, with critical thinking, there are few skills that can set one apart in this highly competitive domain. Some of them are:

Data Wrangling: Data sets can be messy and chaotic, with database fields ill-defined, valueless, used for various purposes in the same field, be full of outliers that no-one can explain, and so on. Hence it is a must to transform, standardize, normalize, and clean them undertaking any real modeling work to extract insights. Data wrangling is the process of transforming data from one format to another. And for this, patience is a must, as no amount of time and knowledge can make up for a poorly represented dataset. E.g., Python Data Wrangling

Web Analytics: As the audience, i.e., the customer is increasingly moving towards social media platforms like Facebook, Twitter, Instagram, etc. these sites act as a storehouse of untapped data that can be used to improve customer services with personalized experiences and enhance products and services offered by a brand. Therefore, it is crucial to deploy web analytics algorithms to collect online data and use it to understand the target customers better. Some common web analytic tools include Kissmetrics, Mixpanel, and Google Analytics, which let companies track and analyze website traffic.

Visualization and Storytelling: While this forms an essential part of a data science job, recruiters may not pay much attention to this skill while hiring. However, through data visualization, one can showcase the results coming from a machine learning algorithm. As mentioned above, it lets data scientists describe and communicate their findings to technical and non-technical audiences. Some useful tools for data visualization are Matplotlib, d3.js, Tableau, ggplot. One can also use eye-catching, high-quality charts, and graphs to present the findings clearly and concisely. Along with that, a data scientist must have a creative mind to important to increase data storytelling skills. This helps in engaging with stakeholders and gaining their support when required.

FACULTY

Name   Dr. P. Chiranjeevi
Designation Associate Professor
Qualification B.Tech (CSE), M.Tech (CSE), Ph.D(CSE)
Professional Exp. 13 Years
Research Interests Opinion Mining
Registration Number 5569-150423-175518
Profile Click here
Name   Ketan Anand
Designation Assistant Professor
Qualification M.Tech
Professional Exp. 06 Years
Research Interests Computer Networks
Registration Number 9779-210503-154822
Profile Click here
Name   Mrs. Thatikonda Supraja
Designation Assistant Professor
Qualification M.Tech
Professional Exp. 03 Years
Research Interests Opinion Mining
Registration Number 0589-210702-161730
Profile Click here

ACHIEVEMENTS

S.NO. Name
1 Dr. P. CHIRANJEEVI Click here
2 Mrs. SOPPARI KAVITHA Click here
3 KETAN ANAND Click here
4 Mrs. THATIKONDA SUPRAJA Click here
5 Ms.NIDUMOLU PRAGNATHY Click here

INFRASTRUCTURE

GPU SERVER CONFIGURATION

Hardware configuration Dell OptiPlex 7080 Tower  with 500w upto 92% efficiency PSU Intel Core i7 10700 10th Generation 32GB DDR4 RAM, M.2 256SSD, 1TB HDD, NVIDIA GeForce RTX 2070 Super 8GB Dell wired keyboard, Windows 10 pro (64bit)
Operating System Windows10, Ubuntu 16.04.
Open-Source Tools Python 3.6.5, R-Language, OpenCV, C, C++, JAVA.
Licensed Tools Oracle 12.1.0.2.0 with analytics.
Other resources High Speed Internet, Projector, White Board, Intercom

Client system Configuration:

Hardware configuration Intel i5 processor with 2.5GHz clock frequency, 4GB RAM, 500GB Hard Disk.
Operating System Windows10, Ubuntu 16.04.
Open Source Tools Python 3.6.5, R-Language, OpenCV, C, C++, JAVA.
Licensed Tools Oracle 12.1.0.2.0 with analytics.
Other resources High Speed Internet, Projector, White Board, Intercom

COURSE STRUCTURE

I B.Tech I-Semester

S.No    Course Code Course Title
1 MA101BS Mathematics – I
2 CH102BS Engineering Chemistry
3 EE103ES

 Basic Electrical Engineering

4 ME105ES Engineering Workshop
5 EN105HS English
6 CH106BS Engineering Chemistry Lab
7 EN107HS English Language and Communication Skills Lab
8 EE108ES Basic Electrical Engineering Lab
9 MC109 Python Programming
10 MC110 Aptitude & Reasoning

I B.Tech II-Semester

S.No    Course Code Course Title
1 MA201BS Mathematics – II
2 PH202BS Applied Physics
3 CS203ES Programming for problem Solving
4 ME204ES Engineering Graphics
5 PH205BS Applied Physics Lab
6 CS206ES Programming for problem Solving Lab
7 MC207ES Environmental Science
8 MC208 Business English

II B.Tech I-Semester

S.No    Course Code Course Title
1  CS301PC Discrete Mathematics
2  CS302PC Data Structures
3  MA305BS Mathematical and Statistical Foundations
4  CS304PC Computer Organization and Architecture
5  CS310PC Advanced Python Programming
6  SM302MS Business Economics & Financial Analysis
7  CS307PC Data Structures Lab
8  CS311PC Advanced Python Programming Lab
9  MC309HS Gender Sensitization Lab

II B.Tech II-Semester

S.No    Course Code Course Title
1 CS409PC  Formal Language and Automata Theory
2 CS410PC  Software Engineering
3 CS403PC  Operating Systems
4 CS404PC  Database Management Systems
5 CS405PC  Java Programming
6 CS406PC  Operating Systems Lab
7 CS407PC  Database Management Systems Lab
8 CS408PC  Java Programming Lab
9 MC409HS  Constitution of India

SYLLABUS

I B.Tech CSE(DS)

II B.Tech CSE(DS)

PREVIOUS QUESTION PAPERS

 MATHEMATICS-I

MATHEMATICS-II

PROGRAMMING FOR PROBLEM SOLVING

PYTHON PROGRAMMING

COURSE MATERIAL

PROGRAMMING FOR PROBLEM SOLVING

Unit – 1

Unit – 2

Unit – 3

Unit – 4

Unit – 5

COURSE MATERIAL

Unit-2, 3, 5 Questions

QUESTION BANK