.webp)
.svg)
Program overview
Online
On-site
Hybrid
Predictive Modeling in Data Science
Build a strong foundation in predictive modeling by understanding core concepts, algorithms, and real world applications. Learn how to preprocess data, engineer features, evaluate models, and build accurate predictive solutions using Python or R for data driven decision making.
Duration:
5 Days
Level:
Advanced
.svg)
.avif)
1500+ users onboarded
Who will Benefit from this Training?
Training Objectives
Build a high-performing, job-ready tech team.
Personalise your team’s upskilling roadmap and design a befitting, hands-on training program with Uptut
Key training modules
Comprehensive, hands-on modules designed to take you from basics to advanced concepts
Download Curriculum
- Predictive Modeling Foundations and Workflow
- Map business outcomes to predictive modeling problems
- Walk the end-to-end modeling workflow from data to decision
- Define success metrics before building models
- Identify common failure modes in production predictions
- Data Preparation and EDA
- Clean and transform data for modeling readiness
- Explore distributions, correlations, and leakage risks
- Visualize patterns that inform feature and model choice
- Hands-on: Produce an EDA brief for a modeling dataset
- Feature Selection and Engineering
- Select relevant features and create predictive signals
- Encode categorical and temporal features safely
- Avoid target leakage in engineered features
- Track feature definitions for reuse in scoring
- Linear and Logistic Regression
- Build linear models for continuous outcomes
- Apply logistic regression for binary classification
- Interpret coefficients and odds in business language
- Diagnose underfitting and overfitting early
- Tree-Based Models (Decision Trees, Random Forests, Gradient Boosting)
- Train decision trees for transparent baselines
- Improve performance with random forests
- Apply gradient boosting for strong tabular results
- Compare tree-family trade-offs for accuracy vs explainability
- SVM and Neural Networks
- Use SVMs for classification and regression tasks
- Introduce neural networks for non-linear patterns
- Match model complexity to data volume and signal
- Set realistic expectations for training cost and tuning
- Time Series Forecasting
- Structure time-dependent data for forecasting
- Apply core forecasting approaches for business horizons
- Validate forecasts with time-aware splits
- Communicate uncertainty and forecast limits
- Evaluation, Cross-Validation, and Hyperparameter Tuning
- Choose metrics for ranking, classification, and regression
- Use cross-validation for trustworthy estimates
- Tune hyperparameters without leaking test data
- Build a model comparison scorecard
- Interpretability and Imbalanced Data
- Explain predictions with practical interpretability methods
- Handle class imbalance with sampling and thresholding
- Align decision thresholds to business cost
- Review fairness and bias signals in model outputs
- Ensemble Methods and Deep Learning Intro
- Combine models to improve robustness
- Understand when ensembles help vs hurt latency
- Introduce deep learning use cases for predictive work
- Decide when classical models remain the better default
- Deployment, Monitoring, and Ethics
- Deploy models for batch or real-time scoring
- Monitor drift, performance decay, and data quality
- Plan retraining and model maintenance loops
- Apply ethics, fairness, and transparency checkpoints
- Case Studies and Advanced Topics
- Review industry predictive modeling case studies
- Practice scoping a prediction use case end to end
- Survey advanced directions (Bayesian methods, RL overview)
- Present recommendations with risks and next steps
Hands-on Experience with Tools
Training Delivery Format
Flexible, comprehensive training designed to fit your schedule and learning preferences
Opt-in Certifications
AWS, Scrum.org, DASA & more
.avif)
100% Live
on-site/online training
.avif)
Hands-on
Labs and capstone projects
.avif)
Lifetime Access
to training material and sessions
Skill-Gap Assessment
Analysing skill gap and assessing business requirements to craft a unique program
1
Personalisation
Customising curriculum and projects to prepare your team for challenges within your industry
2
Implementation
Supplementing training with consulting support to ensure implementation in real projects
3
Why this course
- Improved Decision-Making: Predictive models enable data-driven decision-making by analysing historical data, identifying patterns, and forecasting future outcomes. You can leverage them to anticipate risks, identify opportunities, and understand customer behavior.
- Enhanced Operational Efficiency: Predictive modeling can optimise your operations by identifying bottlenecks, streamlining processes, and reducing costs.
- Risk Management and Fraud Detection: Predictive models can help you identify potential risks and detect fraudulent activities. Fraud detection models analyse transaction patterns and anomalies to identify fraudulent activities, reducing financial losses.
Training objectives
- Gain a clear understanding of predictive modeling, its applications, and its role in data science.
- Gain proficiency in data preprocessing techniques
- Develop knowledge of various predictive modeling algorithms
- Learn how to select relevant features and engineer new ones to enhance their models' predictive power.
- Understand model evaluation and selection.
- Gain hands-on experience in building predictive models using popular programming languages (e.g., Python or R) and relevant libraries and frameworks.
Who will benefit
- Data Scientists
- Analysts
- Data Engineers
- Machine Learning Engineers
.avif)
Lead the Digital Landscape with Cutting-Edge Tech and In-House " Techsperts "
Discover the power of digital transformation with train-to-deliver programs from Uptut's experts. Backed by 70,000+ professionals across the world's leading tech innovators.
.svg)
.svg)
.svg)
.svg)
.svg)
Recommended Training
Continue your learning journey with these hand-picked courses and bundles
Frequently Asked Questions
2. Will my team get any practical experience with this training?
.svg)
With our focus on experiential learning, we have made the training as hands-on as possible with assignments, quizzes and capstone projects, and a lab where trainees will learn by doing tasks live.
3. What is your mode of delivery - online or on-site?
We conduct both online and on-site training sessions. You can choose any according to the convenience of your team.
4. Will trainees get certified?
Yes, all trainees will get certificates issued by Uptut under the guidance of industry experts.
5. What do we do if we need further support after the training?
We have an incredible team of mentors that are available for consultations in case your team needs further assistance. Our experienced team of mentors is ready to guide your team and resolve their queries to utilize the training in the best possible way. Just book a consultation to get support.