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Data Science: Inference and Modeling Training

Live Online & Classroom Enterprise Training

Data Science: Inference and Modeling focuses on statistical analysis and building predictive models from data. It covers concepts like probability, hypothesis testing, and regression to draw insights and make decisions.

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What is Data Science: Inference and Modeling Training about?

This course introduces the statistical concepts that form the basis of modern data science. Learners will explore probability, statistical inference, and predictive modeling, gaining the skills to analyze uncertainty and make sound conclusions from data. The course emphasizes practical applications in hypothesis testing, confidence intervals, regression, and modeling approaches that are central to data-driven problem solving. By combining theoretical understanding with real-world examples, participants will develop a strong foundation for advanced analytics and machine learning.

What are the objectives of Data Science: Inference and Modeling Training ?

  • Understand the principles of probability and statistical inference. 
  • Apply hypothesis testing and confidence intervals in decision-making. 
  • Build and evaluate regression and predictive models. 
  • Interpret statistical outputs to derive actionable insights. 
  • Use inference and modeling techniques for real-world case studies.

Who is Data Science: Inference and Modeling Training for?

  • Aspiring Data Scientists and Analysts. 
  • Students and researchers in statistics, economics, or related fields. 
  • Professionals working with large datasets who want to strengthen statistical skills. 
  • Machine learning enthusiasts seeking a strong statistical foundation. 
  • Decision-makers who rely on data-driven strategies.

What are the prerequisites for Data Science: Inference and Modeling Training?

Prerequisites:  

  • Basic knowledge of statistics and probability. 
  • Familiarity with Python or R for data analysis. 
  • Understanding of data visualization fundamentals. 
  • Comfort with basic algebra and mathematical reasoning. 
  • An interest in applying statistics to practical problems. 


Learning Path: 

  • Introduction to Probability and Inference 
  • Hypothesis Testing and Confidence Intervals 
  • Linear Regression and Predictive Modeling 
  • Interpreting Model Outputs and Assumptions 
  • Applications of Inference and Modeling in Data Science 


Related Courses: 

  • Data Science: Probability 
  • Data Science: Linear Regression 
  • Data Visualization with Python 
  • Statistics for Data Science and Business Analysis

Available Training Modes

Live Online Training

2 Days

Course Outline Expand All

Expand All

  • Sampling Model Parameters and Estimates
  • The Sample Average
  • Polling versus Forecasting
  • Properties of Our Estimate
  • The Central Limit Theorem in Practice
  • Margin of Error
  • A Monte Carlo Simulation for the CLT
  • The Spread
  • Bias: Why Not Run a Very Large Poll?
  • Confidence Intervals
  • A Monte Carlo Simulation for Confidence Intervals
  • The Correct Language
  • Power
  • p-Values
  • Poll Aggregators
  • Pollsters and Multilevel Models
  • Poll Data and Pollster Bias
  • Data-Driven Models
  • Bayesian Statistics
  • Bayes' Theorem
  • Bayes in Practice
  • The Hierarchical Model
  • Election Forecasting
  • Mathematical Representations of Models
  • Predicting the Electoral College
  • Forecasting
  • The t-Distribution
  • Association Tests
  • Chi-Squared Tests

Who is the instructor for this training?

The trainer for this Data Science: Inference and Modeling Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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