Programme Parameters
Advanced Postgrad
4 Weeks (40 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Predictive Modeling and Data Analysis builds the core skill of turning data into dependable prediction. You learn the full workflow: exploring and understanding data, preparing and engineering features, building regression and classification models, and evaluating them honestly. The course keeps the focus on the fundamentals done well — the difference between a model that predicts and one that only appears to. You finish able to build and evaluate a predictive model on real data. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers predictive modelling and data analysis — the essential workflow of exploring data and building models that predict outcomes reliably.
📋 Course Objectives
1. Explore and understand data.
2. Prepare and engineer features.
3. Build regression and classification models.
4. Evaluate models honestly.
5. Avoid common modelling pitfalls.
👥 Who Should Enroll?
• Analysts and aspiring data scientists
• Professionals applying prediction
• Researchers modelling outcomes
• Students of data analysis
🚀 Key Learning Outcomes
• The ability to build predictive models.
• A sound analysis workflow.
• A predictive-modelling project.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Foundations of Predictive Modeling and Data Analysis
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.
Data Collection, Cleaning, and Preprocessing
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.
Exploratory Data Analysis and Visualization
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.
Statistical Analysis and Data Interpretation
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.
Machine Learning for Predictive Modeling
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.
Model Training, Testing, and Evaluation
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.
Forecasting and Time-Series Analysis
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.
Business, Research, and Industry Applications
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.
Capstone: End-to-End Predictive Modeling Project
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.
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Predictive Modeling |
| Covered Tool / Platform | Data Analysis |
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Excel |
| Covered Tool / Platform | SQL |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | NumPy |
| Covered Tool / Platform | Scikit-Learn |
| Covered Tool / Platform | Machine Learning |
| Covered Tool / Platform | Data Visualization |
| Covered Tool / Platform | Forecasting |
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