Our Programs
At SPARK Institute of Data Science and Applied Statistics, we offer specialized programs designed to equip students with the skills and knowledge needed to excel in the fields of statistics and data science. Our curriculum combines theoretical foundations with practical applications to prepare you for real-world challenges.
Introduction to Data Science
Course Overview
This foundational course introduces students to the exciting world of data science. Learn the fundamental concepts, tools, and techniques that form the backbone of modern data analysis and machine learning.
Course Outline
- Introduction to Data Science and its Applications
- Data Collection and Cleaning Techniques
- Exploratory Data Analysis (EDA)
- Statistical Foundations for Data Science
- Introduction to Programming with Python
- Data Visualization Principles
- Basic Machine Learning Concepts
- Ethics in Data Science
Learning Outcomes
- Understand the data science lifecycle and methodologies
- Collect, clean, and preprocess data for analysis
- Perform exploratory data analysis using statistical methods
- Create effective data visualizations to communicate insights
- Apply basic machine learning algorithms to solve problems
- Recognize ethical considerations in data science projects
Infectious Disease Modeling
Course Overview
A cutting-edge course focusing on the statistical and computational modeling of infectious diseases, including epidemic forecasting and intervention strategies. Perfect for those interested in public health data science.
Course Outline
- Epidemiological Principles and Disease Dynamics
- Mathematical Modeling of Infectious Diseases
- Statistical Methods for Epidemiological Data
- Compartmental Models (SIR, SEIR, etc.)
- Spatial and Temporal Disease Spread
- Intervention Strategies and Impact Assessment
- Forecasting Epidemic Trends
- Real-world Case Studies and Applications
Learning Outcomes
- Build mathematical models for infectious disease dynamics
- Analyze epidemiological data using statistical methods
- Simulate disease spread under various scenarios
- Evaluate the effectiveness of public health interventions
- Forecast disease trends using computational models
- Communicate findings to public health stakeholders
Machine Learning Fundamentals
Course Overview
Dive deep into the world of machine learning with this comprehensive course. From basic algorithms to advanced techniques, learn how to build, train, and deploy machine learning models for various applications.
Course Outline
- Introduction to Machine Learning and AI
- Supervised Learning: Regression and Classification
- Unsupervised Learning: Clustering and Dimensionality Reduction
- Neural Networks and Deep Learning Basics
- Model Evaluation and Validation Techniques
- Feature Engineering and Selection
- Ensemble Methods and Model Stacking
- Practical Applications and Project Work
Learning Outcomes
- Implement various machine learning algorithms from scratch
- Preprocess and engineer features for model training
- Evaluate model performance using appropriate metrics
- Tune hyperparameters to optimize model performance
- Apply machine learning to real-world problems
- Understand the ethical implications of ML models
Statistical Analysis with R
Course Overview
Master the art of statistical analysis using R, the language of statisticians. This course covers everything from basic statistical concepts to advanced analytical techniques using R programming.
Course Outline
- Introduction to R and RStudio
- Data Structures and Manipulation in R
- Descriptive Statistics and Data Visualization
- Probability Distributions and Hypothesis Testing
- Regression Analysis (Linear and Multiple)
- Analysis of Variance (ANOVA)
- Time Series Analysis and Forecasting
- Creating Reports with R Markdown
Learning Outcomes
- Write efficient R code for data analysis
- Perform comprehensive statistical analyses
- Create publication-quality visualizations
- Conduct hypothesis tests and interpret results
- Build and validate statistical models
- Communicate findings through reproducible reports
Big Data Analytics
Course Overview
Learn to handle and analyze massive datasets using modern big data technologies. This course covers the tools and techniques needed to extract insights from large-scale data in distributed computing environments.
Course Outline
- Introduction to Big Data Concepts and Challenges
- Hadoop Ecosystem and MapReduce
- Spark for Big Data Processing
- NoSQL Databases (MongoDB, Cassandra)
- Stream Processing with Kafka and Spark Streaming
- Cloud Computing for Big Data (AWS, Azure, GCP)
- Big Data Visualization and Reporting
- Real-world Big Data Case Studies
Learning Outcomes
- Set up and configure big data processing environments
- Process large datasets using distributed computing
- Design and implement NoSQL database solutions
- Build real-time data processing pipelines
- Analyze big data using Spark and other tools
- Solve real-world big data challenges
Data Visualization & Storytelling
Course Overview
Transform data into compelling visual stories that drive decision-making. This course teaches the principles and practices of effective data visualization and storytelling using modern tools and techniques.
Course Outline
- Principles of Effective Data Visualization
- Visual Perception and Cognitive Psychology
- Tools for Data Visualization (Tableau, Power BI, Python)
- Creating Interactive Dashboards
- Advanced Visualization Techniques
- Data Storytelling and Narrative Structure
- Visualizing Geospatial and Temporal Data
- Presenting Data Insights Effectively
Learning Outcomes
- Design effective visualizations for different data types
- Use industry-standard visualization tools proficiently
- Create interactive dashboards for data exploration
- Craft compelling data narratives and stories
- Choose appropriate visualization techniques for data
- Presentation of data insights to diverse audiences