Advanced Data Science Techniques for Academicians

1. Introduction to Data Science and Research
   • Overview of Data Science in academic research
   • Key challenges in handling research data
2. Data Wrangling and Preprocessing Techniques
   • Handling missing data, outliers, and data normalization
3. Exploratory Data Analysis (EDA)
   • Data visualization techniques for research
   • Tools for effective EDA (Python, R, and Excel)
4. Advanced Statistical Methods for Research
   • Hypothesis testing, regression models, and ANOVA
   • Applying advanced statistical models for research insights
5. 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

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