📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of Predictive Modeling and Data Analysis, from foundations to a certified capstone project.
Outline
Understand the role of data analysis in business, research, healthcare, finance, marketing, and technology. • Learn key concepts such as datasets, variables, features, targets, prediction, classification, regression, and model evaluation. • Explore how predictive modeling supports data-driven decision-making and future trend forecasting.
Outline
Learn how to collect structured and unstructured data from spreadsheets, databases, surveys, and online sources. • Handle missing values, duplicate records, incorrect entries, outliers, and inconsistent data formats. • Prepare clean datasets for analysis using practical data preprocessing techniques.
Outline
Analyze data patterns, distributions, trends, relationships, and correlations. • Create charts, graphs, dashboards, and summary reports for better interpretation. • Use visual insights to identify business opportunities, risk factors, and research patterns.
Outline
Understand descriptive statistics, probability, hypothesis testing, correlation, and regression basics. • Interpret statistical results for decision-making and reporting. • Apply statistical thinking to validate assumptions and improve model accuracy.
Outline
Learn supervised learning methods for prediction, classification, and decision-making. • Build models using regression, decision trees, random forests, and other common machine learning techniques. • Understand how algorithms learn from data and generate predictions.
Outline
Split datasets into training and testing sets for model validation. • Evaluate models using accuracy, precision, recall, F1-score, RMSE, MAE, and confusion matrix. • Improve model performance through feature selection, tuning, and error analysis.
Outline
Understand time-based data and forecasting concepts for sales, demand, finance, and operations. • Analyze trends, seasonality, moving averages, and future patterns. • Apply forecasting techniques to support planning and strategic decision-making.
Outline
Apply predictive modeling in healthcare analytics, customer behavior, finance, marketing, education, and operations. • Build analytical reports that communicate insights clearly to decision-makers. • Explore case studies showing how predictive models solve real-world problems.
Outline
Work on a complete data analysis and predictive modeling project from raw data to final insights. • Clean data, perform analysis, build a model, evaluate performance, and present results. • Create a project portfolio that demonstrates practical predictive analytics skills.
Earn government-registered certification in Predictive Modeling and Data Analysis
e-Certificate and e-Marksheet issued on successful completion.
