Instructor Profile:
Dr. Jainesh Sarvaiya
PhD in Engineering | Researcher | Educator | Faculty Development Leader
Dr. Jainesh Sarvaiya, a distinguished academic and researcher, holds a PhD from SVNIT Surat and brings over a decade of expertise to his field. With a robust research portfolio, he has authored more than 10 papers in reputed peer-reviewed journals, making significant contributions that have advanced knowledge in his area of specialization. His work has garnered recognition in academic circles, highlighting his impact on both the research community and industry-related innovations.
As an educator with a passion for fostering critical thinking and innovation, Dr.Sarvaiya has become a vital figure in shaping the minds of future engineers and professionals. He delivers engaging and insightful lectures that inspire students to think critically and develop innovative solutions to complex problems. His commitment to education extends beyond the classroom, where he actively guides students in both their academic and professional development, ensuring their success in competitive environments.
In addition to his research and teaching, Dr.Sarvaiya is deeply involved in faculty development. He has organized and led numerous Faculty Development Programs (FDPs), workshops, and seminars designed to enhance the skills and knowledge of educators and students. Through these initiatives, Dr.Sarvaiya plays a pivotal role in fostering a culture of continuous learning and development, ensuring that the academic community remains at the forefront of educational and technological progress.
With his blend of research excellence, educational leadership, and faculty development, Dr. Jainesh Sarvaiya continues to make lasting contributions to academia and beyond. His involvement in Massive Open Online Courses (MOOCs) will provide learners worldwide with the opportunity to benefit from his vast experience, innovative teaching approach, and dedication to educational excellence.
Course Outline: Data Analytics
Module 1: Introduction to Data Analytics
- Overview of Data Analytics: Definition and importance of data analytics, Types of data analytics: Descriptive, Diagnostic, Predictive, and Prescriptive
- Data Collection and Management: Sources of data: Structured vs. Unstructured data, Data collection methods and tools, Introduction to databases and data warehousing
- Data Preparation and Cleaning: Data preprocessing techniques, Handling missing values and outliers, Data transformation and normalization
- Tools and Technologies: Introduction to popular data analytics tools (Excel, R, Python, SQL)
Module 2: Exploratory Data Analysis (EDA)
- Understanding Data Visualization: Importance of data visualization in analytics, Principles of effective visualization
- Visualization Tools and Techniques: Using libraries (e.g., Matplotlib, Seaborn, ggplot2) for visualization, Creating basic charts: Histograms, Scatter plots, Box plots, etc.
- Statistical Analysis: Descriptive statistics: Measures of central tendency and variability, Correlation and causation, Hypothesis testing basics
- Interpreting EDA Results: Identifying patterns and trends, Drawing conclusions from visualized data
Module 3: Predictive Analytics
- Introduction to Predictive Modeling: Concepts of predictive analytics and its applications, Overview of common predictive models
- Machine Learning Basics: Types of machine learning: Supervised vs. Unsupervised, Introduction to algorithms: Linear regression, Decision trees, Clustering
- Model Evaluation and Validation: Techniques for model evaluation: Accuracy, Precision, Recall, F1 Score, Cross-validation methods, Overfitting and underfitting concepts
Module 4: Advanced Data Analytics Techniques
- Introduction to Big Data Analytics: Understanding big data concepts and technologies (Hadoop, Spark), Challenges in big data analytics
- Text Analytics and Natural Language Processing (NLP): Basics of text analytics, Introduction to NLP techniques: Tokenization, Sentiment analysis, Topic modeling
- Time Series Analysis: Understanding time series data, Forecasting techniques: ARIMA, Exponential Smoothing
- Capstone Project: Application of learned concepts to a real-world data analytics project, Presentation and peer review of project findings
This course is designed to equip participants with a comprehensive understanding of data analytics, from foundational concepts to advanced techniques, enabling them to analyze and derive insights from data effectively.
