Harnessing Data Power: Econometrics with Big Data: Tools & Applications Training Course

Introduction

The exponential growth of data, often referred to as "Big Data," has fundamentally transformed the landscape of economic analysis. Datasets are no longer confined to traditional surveys or administrative records; they now encompass vast quantities of unstructured text, high-frequency financial transactions, satellite imagery, and social media interactions. This revolution presents both unprecedented opportunities for deeper insights into economic phenomena and significant challenges for traditional econometric methodologies, which were often designed for smaller, more structured datasets.

This intensive training course is meticulously designed to equip participants with a comprehensive and practical understanding of how to integrate econometric principles with the tools and techniques of Big Data. From mastering the nuances of high-dimensional data handling and scalable computing to applying advanced machine learning algorithms for causal inference and prediction, you will gain the expertise to rigorously analyze complex, large-scale economic data. This empowers you to conduct cutting-edge data-driven research, inform evidence-based policy in the era of Big Data, and unlock valuable insights from novel data sources.

Target Audience

  • Economists and researchers working with large or complex datasets.
  • Data scientists and quantitative analysts interested in applying econometric rigor to Big Data.
  • Statisticians seeking to expand their skills into Big Data environments.
  • Policy analysts and advisors in government agencies, central banks, and international organizations.
  • Academics and graduate students (Master's and PhD) in economics, finance, business analytics, or data science.
  • Professionals in finance, marketing, and other industries dealing with large economic datasets.
  • Anyone looking to bridge the gap between traditional econometric theory and modern Big Data practices.
  • Software developers and engineers supporting analytical platforms for economists.

Duration: 10 days

Course Objectives

Upon completion of this training course, participants will be able to:

  • Understand the characteristics and challenges of Big Data in econometric applications.
  • Grasp the concepts of high-dimensional data, causality vs. prediction, and the role of machine learning in econometrics.
  • Analyze various tools and platforms for handling, processing, and storing Big Data.
  • Comprehend advanced econometric techniques adapted for large datasets, including regularization and dimension reduction.
  • Evaluate the applications of machine learning algorithms for forecasting, classification, and causal inference in economic contexts.
  • Develop practical skills in implementing Big Data econometric methods using statistical programming languages (e.g., Python, R).
  • Navigate the ethical considerations, privacy issues, and biases inherent in Big Data analysis.
  • Formulate robust, evidence-based insights and communicate complex results derived from Big Data.

Course Content

  1. Introduction to Big Data in Econometrics
  • What is Big Data? Volume, velocity, variety, veracity, value
  • How Big Data challenges traditional econometrics: "curse of dimensionality," endogeneity, spurious correlations
  • The synergy between econometrics and data science/machine learning
  • Prediction vs. causal inference in Big Data settings
  • Real-world examples of Big Data applications in economics and finance
  1. Big Data Infrastructure and Tools
  • Overview of distributed computing: Hadoop, Spark, cloud computing platforms (AWS, Azure, Google Cloud)
  • Data storage: HDFS, NoSQL databases (MongoDB, Cassandra), data warehouses, data lakes
  • Data processing: MapReduce, Spark operations (RDDs, DataFrames)
  • Introduction to relevant programming languages for Big Data: Python (PySpark), R (SparkR)
  • Working with large datasets: efficient data loading, cleaning, and manipulation
  1. Data Wrangling and Feature Engineering for Big Data
  • Data ingestion from various sources: APIs, web scraping, streaming data
  • Handling missing values and outliers in large datasets
  • Data transformation techniques: scaling, normalization, encoding categorical variables
  • Feature engineering: creating new variables from raw data for improved model performance
  • Data governance, quality, and data privacy issues in Big Data
  1. High-Dimensional Econometrics: Regularization Techniques
  • The problem of p≫n (many predictors, few observations)
  • Regularization methods: Lasso, Ridge, Elastic Net regression
  • Theoretical foundations and practical implementation
  • Cross-validation for hyperparameter tuning in high dimensions
  • Variable selection and interpretation of results in penalized regression
  1. Machine Learning for Prediction and Forecasting
  • Introduction to supervised learning: regression and classification
  • Tree-based methods: Decision Trees, Random Forests, Gradient Boosting (XGBoost, LightGBM)
  • Support Vector Machines (SVMs)
  • Neural Networks and Deep Learning fundamentals
  • Evaluating predictive models: RMSE, MAE, R-squared, AUC, precision, recall
  1. Causal Inference with Big Data and Machine Learning
  • Review of causal inference principles: potential outcomes, counterfactuals
  • The role of machine learning in improving causal inference:
    • High-dimensional controls in regression (e.g., using Lasso for confounder selection)
    • Doubly robust estimation and targeted maximum likelihood estimation (TMLE)
    • Causal forests and other tree-based methods for heterogeneous treatment effects
  • Synthetic control methods with large panel data
  • Applications: policy evaluation with large administrative datasets
  1. Time Series Econometrics with High-Frequency and Big Data
  • Characteristics of high-frequency financial data (tick data)
  • Volatility modeling with Big Data: GARCH models revisited, realized volatility
  • Machine learning for time series forecasting: deep learning approaches (LSTMs, CNNs)
  • Nowcasting and nowcasting with Big Data indicators
  • Event studies with large-scale high-frequency data
  1. Text Data Analytics for Economic Research
  • Introduction to Natural Language Processing (NLP)
  • Text as data: tokenization, stop words, stemming, lemmatization
  • Feature extraction from text: Bag-of-Words, TF-IDF, Word Embeddings (Word2Vec, GloVe)
  • Topic modeling (LDA) and sentiment analysis for economic indicators
  • Applications: analyzing central bank communications, news sentiment, social media for economic trends
  1. Network Data Analysis in Econometrics
  • Introduction to network theory: nodes, edges, network characteristics
  • Economic networks: trade networks, financial networks, social networks
  • Measuring centrality, clustering, and community detection in economic networks
  • Econometric models for network data: spatial autoregressive models on networks, network formation models
  • Applications: contagion in financial markets, diffusion of innovation, peer effects
  1. Ethical Considerations, Best Practices, and Future Directions
  • Data privacy and anonymization in Big Data econometrics
  • Algorithmic bias and fairness in economic models
  • Reproducibility and transparency in Big Data research
  • Responsible data use and governance frameworks
  • Emerging trends: federated learning, differential privacy, synthetic data generation
  • The evolving role of the econometrician in the age of Big Data.

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

For More Details call: +254-114-087-180

 

 harnessing Data Power: Econometrics With Big Data: Tools & Applications Training Course in Kenya
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