Unlocking Insights: Machine Learning in Econometrics

Unlocking Insights: Machine Learning in Econometrics, available at $44.99, has an average rating of 2, with 54 lectures, based on 2 reviews, and has 13 subscribers.

You will learn about Understand Econometric Foundations: Grasp core concepts, models, and techniques in econometrics for data-driven economic analysis. Apply Statistical Methods: Apply regression analysis, time series modeling, and hypothesis testing to real-world economic datasets. Integrate Machine Learning: Explore the fusion of ML techniques with econometrics for enhanced predictive modeling and policy insights. Handle Economic Data: Learn data preprocessing, normalization, and handling outliers in economic datasets. Predict Economic Trends: Build predictive models to forecast economic trends, aiding informed decision-making. Ethical Data Usage: Understand ethical considerations and responsible use of data in economic analyses. Future Trends Awareness: Stay updated on emerging trends, like AI-driven economics, shaping the future of the field. This course is ideal for individuals who are Economics Students: Ideal for undergraduate and graduate economics students aiming to enhance analytical skills and apply machine learning in economic research. or Data Analysts: Suited for data analysts seeking to specialize in economic analysis, combining econometrics and machine learning techniques. or Economists: Valuable for practicing economists aiming to modernize their skills, leverage data-driven approaches, and make informed policy recommendations. or Research Professionals: Beneficial for researchers and professionals in economics, finance, and related fields who want to integrate advanced data analysis techniques. or Policy Makers: Useful for policymakers interested in data-driven insights to design effective economic policies and understand their potential impact. or Academic Researchers: Appropriate for researchers and academics exploring interdisciplinary studies at the intersection of economics and machine learning. or Data Science Enthusiasts: Suitable for individuals with a strong interest in data science and its applications in economic analysis. or Prerequisite-Knowledge Seekers: Designed for learners looking to bridge their econometrics and machine learning expertise. It is particularly useful for Economics Students: Ideal for undergraduate and graduate economics students aiming to enhance analytical skills and apply machine learning in economic research. or Data Analysts: Suited for data analysts seeking to specialize in economic analysis, combining econometrics and machine learning techniques. or Economists: Valuable for practicing economists aiming to modernize their skills, leverage data-driven approaches, and make informed policy recommendations. or Research Professionals: Beneficial for researchers and professionals in economics, finance, and related fields who want to integrate advanced data analysis techniques. or Policy Makers: Useful for policymakers interested in data-driven insights to design effective economic policies and understand their potential impact. or Academic Researchers: Appropriate for researchers and academics exploring interdisciplinary studies at the intersection of economics and machine learning. or Data Science Enthusiasts: Suitable for individuals with a strong interest in data science and its applications in economic analysis. or Prerequisite-Knowledge Seekers: Designed for learners looking to bridge their econometrics and machine learning expertise.

Enroll now: Unlocking Insights: Machine Learning in Econometrics

Summary

Title: Unlocking Insights: Machine Learning in Econometrics

Price: $44.99

Average Rating: 2

Number of Lectures: 54

Number of Published Lectures: 54

Number of Curriculum Items: 54

Number of Published Curriculum Objects: 54

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Understand Econometric Foundations: Grasp core concepts, models, and techniques in econometrics for data-driven economic analysis.
Apply Statistical Methods: Apply regression analysis, time series modeling, and hypothesis testing to real-world economic datasets.
Integrate Machine Learning: Explore the fusion of ML techniques with econometrics for enhanced predictive modeling and policy insights.
Handle Economic Data: Learn data preprocessing, normalization, and handling outliers in economic datasets.
Predict Economic Trends: Build predictive models to forecast economic trends, aiding informed decision-making.
Ethical Data Usage: Understand ethical considerations and responsible use of data in economic analyses.
Future Trends Awareness: Stay updated on emerging trends, like AI-driven economics, shaping the future of the field.

Who Should Attend

Economics Students: Ideal for undergraduate and graduate economics students aiming to enhance analytical skills and apply machine learning in economic research.
Data Analysts: Suited for data analysts seeking to specialize in economic analysis, combining econometrics and machine learning techniques.
Economists: Valuable for practicing economists aiming to modernize their skills, leverage data-driven approaches, and make informed policy recommendations.
Research Professionals: Beneficial for researchers and professionals in economics, finance, and related fields who want to integrate advanced data analysis techniques.
Policy Makers: Useful for policymakers interested in data-driven insights to design effective economic policies and understand their potential impact.
Academic Researchers: Appropriate for researchers and academics exploring interdisciplinary studies at the intersection of economics and machine learning.
Data Science Enthusiasts: Suitable for individuals with a strong interest in data science and its applications in economic analysis.
Prerequisite-Knowledge Seekers: Designed for learners looking to bridge their econometrics and machine learning expertise.

Target Audiences

Economics Students: Ideal for undergraduate and graduate economics students aiming to enhance analytical skills and apply machine learning in economic research.
Data Analysts: Suited for data analysts seeking to specialize in economic analysis, combining econometrics and machine learning techniques.
Economists: Valuable for practicing economists aiming to modernize their skills, leverage data-driven approaches, and make informed policy recommendations.
Research Professionals: Beneficial for researchers and professionals in economics, finance, and related fields who want to integrate advanced data analysis techniques.
Policy Makers: Useful for policymakers interested in data-driven insights to design effective economic policies and understand their potential impact.
Academic Researchers: Appropriate for researchers and academics exploring interdisciplinary studies at the intersection of economics and machine learning.
Data Science Enthusiasts: Suitable for individuals with a strong interest in data science and its applications in economic analysis.
Prerequisite-Knowledge Seekers: Designed for learners looking to bridge their econometrics and machine learning expertise.

One of the most valuable skills for the future will be unlocking insights from data. Often, practitioners are experts in machine learning oreconometrics, but not both. However, having at least a basic understanding of the concepts in both econometrics and machine learning will allow practitioners to unlock data insights to the fullest extent. This course is designed to be a first step in bridging the gap between the two fields. Those fluent in machine learning will benefit from examples of econometric thinking, and econometricians will benefit from discussions of machine learning concepts.

In this course, I will discuss the key concepts at the intersection of machine learning and econometrics. I will start by comparing and contrasting the two fields, then I will move into basic data handling skills, then I will discuss keys to exploratory data analysis, and end with a segment on using regression in a machine learning context to make economic predictions. I will give Python code examples for some concepts, and work through a basic case study predicting the economic growth of different countries around the world. This is an introductory course that provides overviews and summaries of the most important ideas, in future courses I will dig deeper into individual concepts – feel free to message me with suggestions!

Course Curriculum

Chapter 1: Introduction to Machine Learning in Econometrics

Lecture 1: Section 1 Overview

Lecture 2: Evolution of Machine Learning and its Role in Econometrics

Lecture 3: Applications of Machine Learning in Economics and Finance

Lecture 4: The Synergy of Traditional and Modern Approaches

Lecture 5: Key Concepts: Supervised, Unsupervised, and Reinforcement Learning

Lecture 6: Challenges and Opportunities in Integrating ML with Econometrics

Lecture 7: Introduction to Relevant Machine Learning Algorithms

Lecture 8: Ethical Considerations in Data-Driven Economic Analysis

Lecture 9: Balancing Model Complexity and Interpretability

Lecture 10: Evaluating Model Performance in Econometric Context

Lecture 11: Future Trends in Machine Learning for Economic Insights

Chapter 2: Data Preprocessing and Cleaning for ML in Econometrics

Lecture 1: Section 2 Overview

Lecture 2: Types of Economic and Financial Data

Lecture 3: Data Collection and Preprocessing Techniques

Lecture 4: Handling Missing Data and Imputation Methods

Lecture 5: Normalization and Standardization of Economic Variables

Lecture 6: Feature Engineering for Enhanced Econometric Analysis

Lecture 7: Feature Engineering for Enhanced Econometric Analysis – part 2

Lecture 8: Time-Series Data Handling and Temporal Patterns

Lecture 9: Incorporating External Data for Improved Predictions

Lecture 10: Addressing Outliers and Data Anomalies

Lecture 11: Data Quality Assurance in Economic ML Models

Lecture 12: Building a Clean and Structured Economic Dataset

Chapter 3: Exploratory Data Analysis for Economic Insights

Lecture 1: Section 3 Overview

Lecture 2: Descriptive Statistics and Data Distributions in Economics

Lecture 3: Visualizing Economic Time-Series Data

Lecture 4: Identifying Correlations and Relationships in Economic Variables

Lecture 5: Cluster Analysis for Segmentation of Economic Data

Lecture 6: Detecting Seasonal Patterns and Cycles

Lecture 7: Interactive Data Visualization for Economic Interpretation

Lecture 8: Mapping Economic Trends Using Geographic Data

Lecture 9: Time-Series Decomposition and Components Analysis

Lecture 10: Outlier Detection in Economic Time-Series

Lecture 11: Extracting Insights from Exploratory Analysis in Economics

Lecture 12: Extracting Insights from Exploratory Analysis in Economics – Part 2

Chapter 4: Regression Analysis and Prediction in Economics

Lecture 1: Regression Models for Economic Analysis

Lecture 2: Regression Models for Economic Analysis – part 2

Lecture 3: Linear Regression and its Assumptions

Lecture 4: Linear Regression in Python

Lecture 5: Ridge and Lasso Regression in Economic Context

Lecture 6: Ridge and Lasso Implementation Strategies

Lecture 7: Ridge and Lasso in Python

Lecture 8: Evaluation Metrics for Economic Regression Models

Lecture 9: Model Interpretation and Feature Importance

Lecture 10: Model Interpretation and Feature Importance – Part 2

Lecture 11: Combining Econometric and Machine Learning Approaches

Lecture 12: Combining Econometric and Machine Learning Approaches – Part 2

Lecture 13: Combining Econometric and Machine Learning Approaches – Part 3

Lecture 14: Combining Econometric and Machine Learning Approaches in Python

Lecture 15: Prediction and Forecasting Using Regression Models

Lecture 16: Prediction and Forecasting Using Regression Models – Part 2

Lecture 17: Regularization Techniques in Economic ML Models

Lecture 18: Regularization Techniques in Economic ML Models – Part 2

Lecture 19: Case Study: Building a Regression Model for Economic Forecasting

Instructors

Grant Gannaway
Instructor at Udemy

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3 stars: 1 votes
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Frequently Asked Questions

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