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Predictive Analytics in Business Intelligence (BI)

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Certificate :

After Completion

Start Date :

10-Jan-2025

Duration :

30 Days

Course fee :

$150

COURSE DESCRIPTION:

– This program explores the significant impact of predictive analytics on Business Intelligence (BI), emphasizing its role in transforming data into actionable insights.
– Participants will acquire skills to analyze historical data effectively, enabling them to forecast future trends, behaviors, and results with precision.
– The curriculum includes practical training and analysis of real-world case studies, ensuring that learners develop proficiency in various predictive modeling techniques and tools.
– This course is particularly suited for professionals aiming to incorporate predictive analytics into their BI processes, enhancing their ability to make informed decisions in a competitive environment.
– By the end of the program, attendees will be equipped with the knowledge and skills necessary to leverage predictive analytics for improved strategic planning and operational efficiency.

CERTIFICATION:

  1. Participants will earn a Certificate in Predictive Analytics for Business Intelligence, showcasing their ability to implement predictive models and drive strategic business decisions.

LEARNING OUTCOMES:

By the conclusion of the course, participants will possess the skills to:

– Grasp the essential principles of predictive analytics and its significance within the realm of business intelligence (BI).
– Choose suitable predictive modeling techniques tailored to different business contexts and requirements.
– Develop, assess, and implement predictive models utilizing software tools such as Python, R, Power BI, and Tableau.
– Incorporate predictive analytics seamlessly into BI dashboards and operational processes.
– Utilize predictive methodologies for practical applications, including demand forecasting, customer segmentation, and risk evaluation, while also tackling issues such as overfitting, data bias, and model interpretability, and effectively communicating predictive findings to stakeholders.

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Course Curriculum

Introduction to Predictive Analytics in BI
  1. Overview of predictive analytics and its applications in business
  2. The predictive analytics lifecycle: From data preparation to deployment
  3. Understanding the role of predictive analytics in BI tools
  4. Real-world use cases: Forecasting sales, improving customer experience, and reducing churn
Data Preparation for Predictive Analytics
  1. Collecting and preprocessing data for predictive modeling
  2. Feature engineering: Creating meaningful predictors
  3. Handling missing data, outliers, and multicollinearity
  4. Tools for data preparation: Python (Pandas), R, Power Query
Predictive Modeling Techniques
  1. Overview of predictive models: Regression, classification, and time-series forecasting
  2. Supervised vs. unsupervised learning in predictive analytics
  3. Introduction to machine learning algorithms: Decision trees, random forests, gradient boosting, and neural networks
  4. Hands-on activity: Building a regression model to predict sales
Predictive Analytics with BI Tools
  1. Integrating predictive models into Power BI, Tableau, and Looker
  2. Using DAX and R/Python scripts in BI tools for predictive analysis
  3. Building interactive dashboards with predictive insights
  4. Real-world example: Creating a churn prediction dashboard in Power BI
Evaluating Predictive Models
  1. Performance metrics for predictive models: RMSE, MAE, accuracy, precision, recall, and F1 score
  2. Cross-validation and hyperparameter tuning
  3. Addressing overfitting and underfitting issues
  4. Practical exercise: Evaluating and improving a predictive model
Advanced Predictive Techniques
  1. Time-series forecasting techniques: ARIMA, Prophet, and exponential smoothing
  2. Customer segmentation using clustering algorithms (K-Means, DBSCAN)
  3. Text analytics and sentiment analysis for unstructured data
  4. Predictive modeling with cloud-based platforms (Azure ML, Google AI, AWS SageMaker)
Implementing Predictive Insights in BI Workflows
  1. Automating predictive analytics workflows
  2. Using APIs to integrate predictive models into existing systems
  3. Real-time predictive analytics for dynamic decision-making
  4. Case study: End-to-end implementation of a predictive analytics BI solution
Ethics, Bias, and Governance in Predictive Analytics
  1. Understanding and addressing bias in predictive models
  2. Ethical considerations in predictive analytics
  3. Ensuring data privacy and compliance with regulations (GDPR, CCPA)
  4. Building explainable and interpretable models for transparency

Training Features

Video Tutorials

Step-by-step instructions for predictive modeling and integration.

Interactive Exercises

Hands-on tasks for building and deploying predictive models.

Live Q&A Sessions

Weekly expert-led sessions to address challenges and questions.

Career Support

Resume building, GitHub portfolio creation, and interview preparation.

Capstone Project

Solve a real-world problem with predictive analytics.

Certification

A globally recognized certificate upon completing the course.

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