In today's data-rich environment, the ability to predict future trends and outcomes is a critical skill for businesses and organizations across all sectors. Traditional statistical methods often fall short when faced with the volume, velocity, and variety of big data. This "Data-Driven Futures" training course is an immersive program designed to equip you with the advanced skills and tools necessary to build sophisticated predictive models that can handle massive datasets, enabling you to extract actionable insights and make informed decisions that drive growth and innovation.

This 10-day intensive program will take you on a journey through the entire predictive modeling lifecycle, from data exploration and preparation to model selection, training, and deployment. You will learn to use cutting-edge frameworks and techniques for distributed computing, parallel processing, and real-time analytics. By the end of this course, you will not only be able to build powerful predictive models but also to deploy them in a scalable, production-ready environment, turning raw data into a competitive advantage.

Duration: 10 days

Target Audience:

• Data Scientists
• Machine Learning Engineers
• Data Analysts
• Business Intelligence Professionals
• Statisticians
• Big Data Engineers
• IT and Analytics Managers
• Research Scientists
• Students in Data Science and Statistics
• Anyone interested in leveraging big data for prediction

Objectives:

• Understand the core concepts of predictive modeling and big data
• Learn to handle and process large-scale datasets efficiently
• Master various machine learning algorithms for prediction
• Develop skills in feature engineering for high-dimensional data
• Understand the principles of model validation and evaluation
• Gain hands-on experience with big data tools like Apache Spark
• Learn to build scalable and robust predictive pipelines
• Understand the challenges and opportunities of real-time prediction
• Gain insights into model deployment and monitoring
• Prepare for the future of AI-driven decision-making

Course Modules:

Module 1: Introduction to Predictive Modeling and Big Data

• Defining big data and its characteristics (3Vs)
• The role of predictive modeling in the modern business world
• An overview of the predictive modeling lifecycle
• Key differences between traditional and big data modeling
• The importance of a strategic approach to data

Module 2: Big Data Ecosystem and Tools

• An introduction to Apache Hadoop and its components
• The power of Apache Spark for in-memory processing
• An overview of distributed file systems (e.g., HDFS)
• The role of data warehousing and data lakes
• Introduction to cloud-based big data services

Module 3: Data Ingestion and Preparation

• Techniques for ingesting large datasets
• Data cleaning and handling missing values at scale
• Data normalization and standardization
• Handling data types in a big data context
• The challenges of data quality and consistency

Module 4: Exploratory Data Analysis (EDA) with Big Data

• The principles of big data visualization
• Using distributed computing for large-scale EDA
• Anomaly detection in massive datasets
• The importance of sampling techniques
• Communicating insights from large datasets

Module 5: Feature Engineering and Selection

• The art of creating meaningful features
• The use of feature hashing for high-dimensional data
• The role of dimensionality reduction techniques (e.g., PCA)
• Automated feature selection methods
• The importance of domain knowledge

Module 6: Supervised Learning for Prediction

• An overview of regression models
• The use of classification algorithms
• Decision trees and ensemble methods (e.g., Random Forest)
• An introduction to gradient boosting (e.g., XGBoost, LightGBM)
• Best practices for model training

Module 7: Unsupervised Learning and Clustering

• The principles of clustering algorithms (e.g., K-Means)
• Using unsupervised learning for customer segmentation
• Anomaly detection for fraud or security
• The role of association rule mining
• The importance of business interpretation

Module 8: Deep Learning for Big Data

• An introduction to neural networks
• The use of deep learning frameworks (e.g., TensorFlow, PyTorch)
• Convolutional Neural Networks (CNNs) for image data
• Recurrent Neural Networks (RNNs) for sequential data
• The challenges of training deep models on big data

Module 9: Model Validation and Evaluation

• The importance of cross-validation
• The use of different performance metrics (e.g., AUC-ROC)
• The role of business metrics in evaluation
• The challenges of model bias and fairness
• Techniques for model debugging

Module 10: Scalable Predictive Pipelines

• The principles of a machine learning pipeline
• Building a reproducible and automated workflow
• The role of MLOps
• Containerization (e.g., Docker) and orchestration (e.g., Kubernetes)
• Best practices for pipeline design

Module 11: Real-Time Prediction and Streaming Data

• The principles of streaming analytics
• Building a real-time predictive service
• The role of low-latency model inference
• The challenges of system throughput
• Strategies for continuous prediction

Module 12: Time Series Analysis and Forecasting

• The principles of time series data
• Common time series models (e.g., ARIMA, Prophet)
• The role of big data in time series forecasting
• Handling seasonality and trends
• The challenges of long-term prediction

Module 13: Model Deployment and Serving

• The principles of a model serving API
• The role of Flask or FastAPI for deployment
• Monitoring model performance in production
• Automated retraining and model decay
• Best practices for production-ready models

Module 14: Case Studies in Predictive Modeling

• Analyzing real-world examples of successful predictive models
• The challenges and successes of different approaches
• The importance of a multidisciplinary team
• The role of leadership in data-driven decision-making
• Lessons learned from the field

Module 15: Final Project and Discussion

• A hands-on project to build and deploy a predictive model on a big dataset
• Presenting your findings and recommendations
• A discussion of remaining challenges and open problems
• The importance of a continuous learning mindset
• The future of predictive modeling

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 10 working days before commencement of the training.