Important Dates:

Registration starts: December 12, 2023

Last date for registration: December 25, 2023

Last date for registration: January 02, 2024

Event date: January 03-16, 2024

Note: Registration requires the submission of your Name, Email Address, and Contact details. Upon fee payment, you will receive a confirmation email. Joining link will be sent to registered participants on their email address only 5-10 minutes before the commencement of each session.

Registration

Registration Fee 235.50 INR including GST+ Internet handling fee: 5.00 INR, Total registration fee: 240:50 INR

Overview:

The Data Science and Scientific Computing workshop is an intensive two-week program designed to provide participants with a comprehensive understanding of data science concepts, scientific computing techniques, and their practical applications. This hands-on workshop focuses on empowering participants with the skills and knowledge needed to analyze complex datasets, apply machine learning algorithms, and leverage scientific computing tools for problem-solving. Through a combination of lectures, interactive discussions, coding exercises, and real-world projects, participants will gain valuable experience in data manipulation, visualization, statistical analysis, and machine learning. The workshop will also delve into advanced topics such as deep learning, natural language processing, and cloud computing, ensuring a well-rounded learning experience.

Key Highlights:

• Expert Instructors: Learn from experienced data scientists and experts in the field who will guide participants through the workshop modules and provide personalized assistance.
• Hands-on Learning: Engage in practical, hands-on coding exercises and projects using industry-standard tools and libraries, allowing participants to reinforce their skills in a real-world context using R and Python.
• Comprehensive Curriculum: Cover a wide range of topics including data preprocessing, exploratory data analysis, statistical analysis, machine learning, scientific computing, deep learning, natural language processing, and cloud technologies.
• Project-Based Approach: Work on individual and group projects that challenge participants to apply their skills to solve real-world problems, encouraging creativity and innovation.
• Interactive Sessions: Participate in interactive discussions, Q&A sessions, and peer-to-peer learning, fostering collaboration and knowledge sharing among participants.
• Networking Opportunities: Connect with fellow participants, instructors, and guest speakers, creating valuable professional networks within the data science community.
• Career Guidance: Gain insights into various career paths within the data science field, receive advice on building a strong portfolio, and learn about job market trends and industry best practices.
• All registered participants will have access to all presented content, recordings of each live session, supportive materials, daily assignments, and quizzes.
• A certificate of participation will be granted upon successful attendance of all sessions.

Possible outcomes

Upon completing the two-week workshop on Data Science and Scientific Computing, participants will:
1. Develop Strong Analytical Skills: Gain proficiency in analyzing complex datasets, extracting meaningful insights, and making data-driven decisions.
2. Master Essential Tools and Technologies: Acquire expertise in Python programming, NumPy, SciPy, Pandas, and popular machine learning frameworks like scikit-learn and TensorFlow/PyTorch.
3. Enhance Data Visualization Abilities: Learn to create compelling visualizations using libraries like Matplotlib, Seaborn, and Plotly, improving communication of data insights.
4. Build Machine Learning Competency: Understand fundamental machine learning algorithms, enabling participants to develop predictive models and perform classification, regression, and clustering tasks.
5. Apply Deep Learning Techniques: Gain hands-on experience in building neural networks for tasks like image recognition, natural language processing, and sentiment analysis.
6. Excel in Scientific Computing: Master scientific computing techniques using NumPy and SciPy, enabling participants to solve complex mathematical problems and optimize algorithms.
7. Develop Real-World Projects: Work on practical projects, applying acquired skills to real-world scenarios, strengthening problem-solving abilities.
8. Gain Cloud Computing Knowledge: Understand cloud platforms such as AWS, GCP, or Azure, and learn to deploy data science models, enhancing scalability and accessibility.
9. Improve Communication and Presentation Skills: Learn to effectively communicate findings, visualize data, and present insights, crucial for collaboration in data science projects.
10. Receive Career Guidance: Acquire insights into diverse career paths within data science, along with valuable networking opportunities, empowering participants to pursue rewarding roles in the field.

Who Should Attend:

– Professionals aspiring to enter the field of data science or enhance their existing skills
– Researchers, scientists, and engineers interested in applying computational techniques to their research
– Students and recent graduates seeking a practical understanding of data science and scientific computing

Prerequisites:

Basic knowledge of programming concepts and familiarity with Python will be beneficial, but motivated beginners are welcome.

Contents:

Day 1: Introduction to Data Science: What is data science and its importance?,  Key tools and technologies in data science,  Setting up the environment (Python, Jupyter Notebook).

Day 2: Data Collection and Cleaning: Data sources and collection methods, Data preprocessing and cleaning techniques, Hands-on data cleaning exercises.

Day 3: Exploratory Data Analysis (EDA): Descriptive statistics, Data visualization with Matplotlib and Seaborn, EDA best practices.

Day 4: Statistical Analysis and Hypothesis Testing: Probability and distributions, Hypothesis testing and p-values,  Practical applications of statistical analysis.

Day 5: Introduction to Machine Learning: What is machine learning?, Supervised vs. unsupervised learning, Scikit-learn introduction

Day 6: Scientific Python Libraries: Introduction to NumPy for numerical computing, Scientific computing with SciPy, Linear algebra and optimization using NumPy and SciPy

Day 7: Data Manipulation with Pandas: Introduction to Pandas for data manipulation, Data cleaning and transformation with Pandas, Grouping and aggregating data

Day 8: Machine Learning in Practice: Feature engineering, Model selection and evaluation, Hands-on machine learning project

Day 9: Data Visualization and Storytelling: Advanced data visualization with libraries like Plotly, Creating interactive visualizations, Communicating results effectively

Day 10: Deep Learning and Neural Networks: Introduction to neural networks, Building deep learning models with TensorFlow or PyTorch, Hands-on deep learning project

Day 11: Natural Language Processing (NLP): Introduction to NLP concepts, Text preprocessing and tokenization, Sentiment analysis or text classification project

Day 12: Big Data and Cloud Computing: Introduction to big data technologies (Hadoop, Spark), Cloud computing platforms (AWS, GCP, Azure),  Deploying models on the cloud

Day 13: Real-world Applications and Career Guidance: Guest lectures from industry professionals, Real-world case studies in data science, Career opportunities and job market insights

Day 14: Project Work and Presentations: Participants work on their own data science projects, Mentoring and guidance from instructors, Project presentations and peer feedback

Throughout the workshop, participants should work on hands-on exercises and projects to apply what they’ve learned. The focus should be on practical, real-world applications, and participants should be encouraged to explore their own areas of interest within data science and scientific computing. Networking opportunities and resources for further learning should also be provided to help participants continue their data science journey after the workshop.

Speaker(s)

Prof. D. K. Lobiyal: Former Dean, School of Computer & Systems Sciences Jawaharlal Nehru University New Delhi, INDIA

Prof. (Dr.) Valentina E. Balas: Faculty of Engineering, Department of Automation and Applied Informatic , “Aurel Vlaicu” University of Arad, ROMANIA

Prof. (Dr.) Pooja: Dean: Faculty of Engineering & Technology, Sharda University Uzbekistan

Prof (Dr.) Amandeep Kaur: Department of Computer Science and Technology, Central University of Punjab, Bathinda, INDIA

Prof. (Dr.) Ihtiram Raza Khan: Academician at Jamia Hamdard University, Delhi, INDIA

Dr. Ajay Khunteta: Prof. & Dean, Faculty of Computer Science & Engg, Poornima University, Jaipur, INDIA

Dr. Ashok Kumar Pathak: Department of Mathematics and Statistics, Central University of Punjab, Bathinda

Dr. Harmanpreet Singh Kapoor: Department of Mathematics and Statistics, Central University of Punjab, Bathinda, INDIA

Dr. Prayas Sharma: Department of Statistics, Babasaheb Bhimrao Ambedkar University (A Central University), Lucknow, INDIA

Mr. Harish Kumar Pamnani: Faculty of Computer Engineering, Center of excellence Machine learning application for society, Poornima University, Jaipur, INDIA

Dr. Upinder Kaur: Department of Computer Science and Engineering, Akal University, Bathinda, INDIA

Dr. Shalu Gupta: Department of Computer Science, Baba Farid College, Bathinda, INDIA

Patron(s)

Prof. R. P. Tiwari: Vice Chancellor, Central University of Punjab, Bathinda, INDIA

Dr. Gurmeet Singh Dhaliwal: Chairman, Baba Farid Group of Institutions, Bathinda, INDIA

Ms. Kgomotso Morotolo: President, Nexus University, SOUTH AFRICA

Dr. Suresh Padhy, President, Poornima University, Rajasthan, INDIA

Co-Patron(s)

Prof. R. Wusurika: Dean Incharge Academics, Central University of Punjab, Bathinda, INDIA

Prof. Sanjeev K. Thakur: Dean, School of Basic Sciences, Central University of Punjab, Bathinda, INDIA

Dr. Manoj Gupta: Pro-President, Poornima University, Rajasthan, India

Dr. Manish Bansal: Principal, Baba Farid College, Bathinda, INDIA

Dr. Chandni Kirpalani: Registrar, Poornima University, Rajasthan, India

Convener(s)

Dr. Swati Gokhru – Dean, International Relations, Poornima University, Rajasthan, INDIA

Dr. Deep Singh: Department of Mathematics and Statistics, Central University of Punjab, Bathinda, INDIA

Prof. Gauree Shanker: Department of Mathematics and Statistics, Central University of Punjab, Bathinda, INDIA

Dr. Mehar Chand: Faculty of Mathematics, Baba Farid College, Bathinda & President, MTTF, Fazilka, INDIA

Mr. Adekunle Owolabi: Nexus University, SOUTH AFRICA

Co-Convener(s)

Dr. Anoop Kumar: Department of Mathematics and Statistics, Central University of Punjab, Bathinda, INDIA

Dr. Jaswinder Pal: Department of Physics and Mathematics, Baba Farid College, Bathinda, INDIA & Director of Accounts, MTTF, Fazilka, INDIA

Dr. Harmanpreet Singh Kapoor: Department of Mathematics and Statistics, Central University of Punjab, Bathinda, INDIA

Dr. Jasmeet Kaur: Department of Physics and Mathematics, Baba Farid College, Bathinda, INDIA

Dr. Gurmej Singh Sandhu: General Secretary, MathTech Thinking Foundation, Fazilka, INDIA

Coordinator(s)

Dr. Zaved AHMED KHAN: Dean, Basic Sciences, Baba Farid College, Bathinda, INDIA

Dr. Manoj Kumar: Department of Computer Science, BBAU (A Central University), Lucknow, INDIA

Dr. Sachin Kumar: Department of Mathematics and Statistics, Central University of Punjab, Bathinda, INDIA

Dr. Ashok Kumar Pathak: Department of Mathematics and Statistics, Central University of Punjab, Bathinda, INDIA

Ms. Monika Sharma: Assistant Professor, Department of Computer Science & Engineering, Poornima University, Jaipur, INDIA

Anuradha Raheja: Faculty of Computer Engineering, Poornima University, Jaipur, INDIA

Dr. Yogita Shama: Executive Member, MathTech Thinking Foundation, Fazilka, INDIA

Organizing Committee Member(s)

Mr. Anuj Kumar, Department of Mathematics and Statistics, Central University of Punjab, Bathinda, INDIA

Dr. Harbhajan Singh: Department of Physics and Mathematics, Baba Farid College, Bathinda, INDIA

Mr. Navneet Garg: Department of Physics and Mathematics, Baba Farid College, Bathinda, INDIA

Ms. Akshita Rani: Department of Physics and Mathematics, Baba Farid College, Bathinda, INDIA

Ms. Jaskiran Kaur: Department of Physics and Mathematics, Baba Farid College, Bathinda, INDIA

Ms. Alisha Rani: Department of Physics and Mathematics, Baba Farid College, Bathinda, INDIA

Ms. Rupinder Kaur: Department of Physics and Mathematics, Baba Farid College, Bathinda, INDIA

Ms. Rajveer Kaur: Department of Physics and Mathematics, Baba Farid College, Bathinda, INDIA

Dr. Amit Paul: Department of Mathematics, Guru Nanak Dev University, Amritsar, Punjab, INDIA

Dr. Madhuchanda Rakshit: Department of Mathematics, Gurukashi University, Bathinda, INDIA

Dr. Bharti Kapoor: Executive Member, MathTech Thinking Foundation, Fazilka, INDIA

Dr. Daljeet Kaur: Executive Member, MathTech Thinking Foundation, Fazilka, INDIA

Dr. Upinder Kaur: Executive Member, MathTech Thinking Foundation, Fazilka, INDIA

Dr. Biswaranjan Senapati: Executive Member, MathTech Thinking Foundation, INDIA

Mr. Jatin Bansal: Executive Member, MathTech Thinking Foundation, INDIA

Dr. Bharti V. Nathwani: Department of Mathematics, Amity School of Applied Sciences, Amity University, Mumbai, INDIA

Dr. Krunal Kachhia: Department of Mathematics, Chaotar University of Science and Technology, Changa, Anand, Gujarat, INDIA

Dr. Naveen Kumar: Department of Mathematics, Chandigarh University, Mohali, Chandigarh, INDIA

Jointly organized by

Central University of Punjab, Bathinda, INDIA

Poornima University, Jaipur, INDIA

Nexus University, South Africa

Baba Farid College, Bathinda, INDIA

MathTech Thinking Foundation, Fazilka, Punjab, INDIA