Fundamentals of Predictive Analytics Training Course
Gain the skills you need to advance your career. This course offers hands-on learning, expert guidance, and real-world applications designed to help you grow.
Course Overview
| Course Title | Fundamentals of Predictive Analytics Training Course |
| Organization | Tech For Development (T4D) |
| Venue | Tech For Development (T4D) Training Center along Tala Road, Runda, Nairobi |
| Duration | 5 days |
| Target Industries |
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| Target Job Roles |
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| Course Fees (Face-to-Face) | USD 1,100/KES 75,000 (Exclusive of VAT) |
| Course Fees (Virtual) | USD 1,000/KES 70,000 (Exclusive of VAT) |
| Training Modes | Virtual and face-to-face training |
| Payment | Payment should be made to the Tech For Development (T4D) bank account on or before the start of the course |
| Accreditation | Tech For Development Certificate of Course Completion |
Course Overview
If you want to predict how customers will respond in the future, you need to turn to predictive analytics. By learning from your abundant historical data, predictive analytics provides something beyond standard business reports and sales forecasts: actionable predictions for each customer. These predictions encompass all channels, both online and off, foreseeing which customers will buy, click, respond, convert or cancel. The customer predictions generated by predictive analytics deliver more relevant content to each customer, improving response rates, click rates, buying behavior, retention and overall profit. For online applications such as e-marketing and customer care recommendations, predictive analytics acts in real-time, dynamically selecting the ad, web content or cross-sell product each visitor is most likely to click on or respond to, according to that visitor's profile.
Duration
5 days
Target Audience
- Aspiring Data Analysts
- Business Analysts
- Marketing Professionals
- Finance Professionals
- Product Managers
- Small Business Owners
- Researchers
Organizational Impact
- Enhanced decision-making capabilities
- Improved forecasting accuracy
- Increased efficiency in data management
- Better strategic planning through data insights
Personal Impact
- Advanced skillset in predictive analytics
- Proficiency in using Dataverse for data analysis
- Enhanced career opportunities in data science and analytics
- Ability to make informed decisions based on predictive models
Course Objectives
- Applications: Business, marketing and web problems solved with predictive analytics
- The techniques, tips and pointers you need in order to run a successful predictive analytics and data mining initiative
- How to strategically position and tactically deploy predictive analytics and data mining at your company
- How to bridge the prevalent gap between technical understanding and practical use
- How a predictive model works, how it's created and what it looks like
- Evaluation: How well a predictive model works and how much revenue it generates
- Detailed case studies that demonstrate predictive analytics in action and make the concepts concrete
- Two tool demonstrations showing how predictive analytics really works
Course Modules
Course Outline
Module 1: Introduction to Predictive Analytics
- Definition and objectives of predictive analytics
- Key concepts and terminology
- Types of predictive models
- Tools and software used in predictive analytics
- Case Study: Predicting customer churn in a subscription-based service
Module 2: Data Preparation and Exploration
- Data collection methods
- Data cleaning and preprocessing techniques
- Exploratory data analysis (EDA)
- Feature selection and engineering
- Case Study: Preparing a dataset for predicting housing prices
Module 3: Statistical Foundations
- Basic statistical concepts (mean, median, variance, etc.)
- Probability distributions and their applications
- Hypothesis testing and confidence intervals
- Correlation and regression analysis
- Case Study: Analyzing the relationship between advertising spend and sales revenue
Module 4: Predictive Modeling Techniques
- Overview of regression models (linear, logistic, etc.)
- Decision trees and random forests
- Support vector machines and neural networks
- Model evaluation metrics (accuracy, precision, recall, etc.)
- Case Study: Building a predictive model for loan default risk
Module 5: Implementing and Interpreting Predictive Models
- Model deployment strategies
- Model interpretation and communication
- Handling model updates and maintenance
- Ethical considerations in predictive modeling
- Case Study: Implementing a fraud detection system in financial transactions
Course Administration
Methodology
Instructor-led sessions use a blended learning approach combining presentations, guided practical exercises, web-based tutorials, and group work, delivered by seasoned industry experts. All facilitation and course materials are in English, so participants should be reasonably proficient in the language.
Accreditation
Upon successful completion of this training, participants will be issued a Tech For Development Certificate of Course Completion certified by the National Industrial Training Authority (NITA).
Training Venue
The training will be held at the Tech For Development Training Centre. The course fee covers the course tuition, training materials, two break refreshments, and lunch. All participants will additionally cater to their travel expenses, visa application, insurance, and other personal expenses.
Accommodation & Airport Transfer
Arranged upon request.For reservations contact the Training Officer:
Email: [email protected]
Phone: (+254) 790 824 179
Tailor-Made
This training can also be customized to suit the needs of your institution upon request. You can have it delivered in our T4D Training Centre or at a convenient location.
Payment
Payment should be transferred to the T4D account through a bank on or before the start of the course. Send proof of payment to [email protected].
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