Data Science and Predictive Analytics Training Course

Introduction

In this data-driven era, organizations are increasingly relying on data science and predictive analytics to inform strategic decisions, improve operations, and drive innovation. This course equips participants with foundational to advanced competencies in data wrangling, modeling, and forecasting using powerful data science tools and methodologies.

Participants will learn to identify patterns in complex datasets, build predictive models, and apply machine learning techniques to real-world business problems. The course emphasizes the practical application of data science using tools such as Python, R, and cloud-based platforms.

Emerging trends like generative AI, ethical data practices, automated machine learning (AutoML), and the integration of AI in business intelligence platforms will also be explored to prepare participants for future-forward analytics environments.

Target Participants:

• Data Analysts and Scientists
• Business Intelligence Professionals
• Statisticians and Researchers
• IT and Software Developers
• Managers and Decision Makers
• Professionals interested in machine learning, AI, and forecasting

Duration

10 Days

Course Objectives:

By the end of this course, participants will be able to:

• Understand the data science life cycle and project workflow
• Clean, manipulate, and visualize large datasets
• Build and validate predictive models using machine learning techniques
• Apply statistical and algorithmic methods for forecasting and classification
• Integrate predictive models into business decision-making processes
• Explore emerging trends in data ethics, AI governance, and AutoML

Course Outline:

Day 1: Introduction to Data Science

• Overview of data science and predictive analytics
• Data science vs. traditional analytics
• Key components: data, models, decisions
• Tools overview: Python, R, Jupyter, Tableau

Day 2: Data Preparation and Cleaning

• Data wrangling techniques (Pandas, NumPy)
• Handling missing data, outliers, and duplicates
• Data types and transformations
• Data normalization and standardization

Day 3: Exploratory Data Analysis (EDA) and Visualization

• Univariate, bivariate, and multivariate analysis
• Data visualization tools and libraries (Matplotlib, Seaborn)
• Feature engineering and selection
• Identifying data patterns and anomalies

Day 4: Statistical Foundations

• Descriptive and inferential statistics
• Probability distributions
• Hypothesis testing and p-values
• Correlation and regression analysis

Day 5: Predictive Modeling Basics

• Introduction to supervised and unsupervised learning
• Linear and logistic regression
• Decision trees and random forests
• Model evaluation: accuracy, precision, recall, F1-score

Day 6: Machine Learning Techniques

• k-NN, SVM, and Naive Bayes
• Clustering techniques (K-means, DBSCAN)
• Dimensionality reduction (PCA, t-SNE)
• Cross-validation and hyperparameter tuning

Day 7: Time Series and Forecasting

• Time series components and decomposition
• ARIMA, SARIMA models
• Exponential smoothing methods
• Real-time forecasting applications

Day 8: Advanced Predictive Analytics and AI Integration

• Introduction to deep learning and neural networks
• AutoML platforms (Google AutoML, H2O.ai, Azure ML)
• Natural Language Processing (NLP) basics
• Use of AI in real-time analytics dashboards

Day 9: Ethics, Privacy, and Governance in Data Science

• Data governance and compliance (GDPR, HIPAA)
• Bias in data and algorithmic fairness
• Data privacy, consent, and secure storage
• Responsible AI and model explainability (XAI)

Day 10: Capstone Project and Business Case Studies

• End-to-end data science project development
• Presentation of predictive analytics solutions
• Real-world use cases across industries
• Feedback and improvement strategies

Emerging Topics and Issues Covered:

• Generative AI and predictive analytics
• Automated Machine Learning (AutoML)
• Responsible AI and bias mitigation
• Edge analytics and real-time processing
• Data democratization and citizen data science
• Cloud-based data analytics platforms

Training Methodology:

• Interactive lectures and expert facilitation
• Hands-on coding labs and exercises (Python/R)
• Case studies and industry applications
• Peer collaboration and group projects
• Real-world datasets for practice
• Capstone project with mentorship and evaluation

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue

The training will be held at our Upskill 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 Upskill 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: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.