The Advanced Data Science Techniques for Academicians Program provides a deep dive into data science methodologies tailored for academic use cases. Participants will learn to analyze complex datasets, build predictive models, and create impactful visualizations to enhance research output and pedagogy. The workshop combines theoretical knowledge with practical applications, enabling academicians to integrate data science into their academic and research endeavors.

Aim

This workshop is designed for academicians and researchers to gain advanced skills in data science techniques, emphasizing their applications in academic research, teaching, and innovation. The workshop focuses on equipping participants with expertise in data analysis, machine learning, and visualization using modern tools and frameworks.

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Workshop Objectives

Equip participants with advanced data science skills for academic research.
Teach effective data wrangling, preprocessing, and analysis techniques.
Provide expertise in machine learning, NLP, and visualization tools.
Foster ethical data practices in academic and research environments.
Enable participants to complete a capstone project showcasing data science applications in their domain.

Workshop Structure

Introduction to Data Science and Research
- Overview of Data Science in academic research
- Key challenges in handling research data
Data Wrangling and Preprocessing Techniques
- Handling missing data, outliers, and data normalization
Exploratory Data Analysis (EDA)
- Data visualization techniques for research
- Tools for effective EDA (Python, R, and Excel)
Advanced Statistical Methods for Research
- Hypothesis testing, regression models, and ANOVA
- Applying advanced statistical models for research insights
Big Data Handling and Management
- Introduction to big data concepts (Hadoop, Spark)
- Working with large datasets in academic research

Day wise Schedule:

Day 1: Introduction to Data Science and Data Preprocessing
- Overview of data science for academic research
- Practical session: Cleaning and preprocessing academic datasets
Day 2: Exploratory Data Analysis and Visualization
- Hands-on EDA using Python (matplotlib, seaborn) and R (ggplot2)
Day 3: Advanced Statistical Techniques for Research
- Applying linear regression, ANOVA, and hypothesis testing on academic datasets
Day 4: Big Data and Research Management Tools
- Introduction to big data handling tools for large-scale academic research

Participant’s Eligibility

Academicians, researchers, PhD scholars, and faculty members across disciplines interested in applying data science techniques to academic research.

Important Dates

Registration Ends

2024-12-08
Indian Standard Timing 1:00 pm

Workshop Dates

2024-12-08 to 2024-12-11
Indian Standard Timing 5 PM

Workshop Outcomes

Mastery of advanced data science tools and techniques tailored for academic research.
Ability to analyze and visualize complex datasets for impactful research outcomes.
Expertise in ethical data practices and the use of cloud-based data platforms.
Completion of a research-ready project integrating data science methodologies.

Mentor Profile

Name: Gurpreet Kaur
Designation: Assistant Professor
Affiliation: UIC Department, Chandigarh University

Mrs. Gurpreet Kaur holds an MCA degree from Punjab Technical University (2010) and has over 7 years of IT industry experience as a Senior Software Developer in various companies. Her expertise lies in front-end technologies, data structures, and algorithms (DSA).

Fee Structure

Student

INR. 1999
USD. 55

Ph.D. Scholar / Researcher

INR. 2599
USD. 60

Academician / Faculty

INR. 3999
USD. 70

Industry Professional

INR. 6499
USD. 100

We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!
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