INTRODUCTION TO PREDICTIVE ANALYTICS
Course 2303
3 Hours
$299
Harness the power of data to forecast the future. This course introduces professionals to predictive analytics, teaching them how to build models, interpret results, and apply insights to real-world challenges. Start making data-driven decisions with confidence.
Target Audience
- Data analysts and professionals new to predictive analytics
- Managers and business leaders looking to leverage predictive insights
- IT professionals supporting data analytics teams
- Students and professionals exploring data science careers
Course Format:
- Live or Virtual Lectures: Delivered by predictive analytics experts.
- Interactive Activities: Hands-on exercises, scenario planning, and case study discussions.
- Resources Provided: Datasets, model templates, and additional learning guides.
Payment Information: We accept credit card payments.
Course Objectives
- Understand the fundamentals of predictive analytics and its role in data-driven decision-making.
- Learn common predictive modeling techniques, such as regression and classification.
- Gain hands-on experience building and interpreting predictive models.
- Explore real-world applications across industries like healthcare, finance, and marketing.
Course Structure:
Fundamentals of Predictive Analytics
- What is predictive analytics? Definition and key concepts.
- The predictive analytics lifecycle: data collection, modeling, and interpretation.
- Overview of common techniques: regression, classification, and time series analysis.
Data Preparation for Predictive Analytics
- Cleaning and preprocessing data for modeling.
- Handling missing values, outliers, and feature selection.
- Tools and platforms for data preparation.
Building Predictive Models
- Introduction to linear regression, logistic regression, and decision trees.
- Training, testing, and validating predictive models.
- Interpreting model outputs and understanding key metrics.
Applications and Use Cases
- Examples of predictive analytics in action: healthcare risk prediction, customer segmentation, and sales forecasting.
- Challenges in applying predictive analytics: data quality, overfitting, and scalability.
- Strategies for integrating predictive insights into decision-making processes.
Future Trends and Next Steps
- Emerging trends in predictive analytics: AI-driven forecasting, real-time analytics, and automation.
- Resources for continuing education in predictive analytics.
- Building a learning roadmap to advance your skills.
Group Rates Available!
Bring your team and save – perfect for healthcare organizations looking to train multiple leaders.
Discounts available for organizations enrolling 5+ participants.
Class Schedule
Rates below are for each participant.
January 21, 2025
9:00 AM – 12:00 PM EST
Intro to Predictive Analysis
Virtual, EST
$299
Class ID: 2303
February 18, 2025
12:00 PM – 3:00 PM EST
Intro to Predictive Analysis
Virtual, EST
$299
Class ID: 2303
March 25, 2025
9:00 AM – 12:00 PM EST
Intro to Predictive Analysis
Virtual, EST
$299
Class ID: 2303
Frequently Asked Questions
What will I learn in this course?
You’ll learn the basics of predictive analytics, including data preparation, common modeling techniques, and how to interpret and apply predictive insights.
Do I need prior experience with analytics to take this course?
No prior experience is required. The course is designed for beginners and provides a practical introduction to predictive analytics concepts and tools.
What tools will I use during the course?
The course uses beginner-friendly tools, such as Excel, Python, or R, along with datasets provided for hands-on practice.
Will I build a predictive model during the course?
Yes, participants will build and evaluate a simple predictive model using real-world datasets.
How does this course address real-world applications?
The course includes case studies and scenarios showcasing predictive analytics in industries like healthcare, finance, and marketing.
Is this course suitable for non-technical professionals?
Yes, the course emphasizes practical skills and insights, making it accessible to both technical and non-technical participants.
Does the course cover ethical considerations in predictive analytics?
Yes, ethical concerns like data privacy and fairness are briefly addressed, particularly in the context of real-world applications.
What resources will I receive after the course?
Participants will receive datasets, model templates, and a curated list of additional learning resources for continued exploration.