Data Scientist — Exploratory Data Analysis (EDA)

[Description] This day explores advanced visualization techniques to uncover patterns in high-dimensional datasets. The focus is on moving beyond basic plots to leverage techniques such as: parallel coordinates, t-SNE, UMAP, and custom interactive visualizations using tools like Bokeh and Plotly. Emphasis will be placed on understanding the limitations and biases inherent in these techniques. - Learn: High-dimensional data visualization, interactive plotting with Bokeh/Plotly, dimensionality reduction techniques (t-SNE, UMAP). - Do: 1. Apply dimensionality reduction (e.g., PCA, t-SNE, UMAP) to a high-dimensional dataset and visualize the results. 2. Create interactive dashboards for data exploration using Bokeh or Plotly (with filtering, zooming, and tooltips). 3. Compare and contrast the effectiveness of different visualization techniques for specific analytical questions. 4. Create custom visualizations for specific data types (e.g., time series, geospatial data). - Resources: - Expected Outcomes: Ability to visualize and explore high-dimensional data effectively, create interactive dashboards for data analysis, and understand the trade-offs of different visualization approaches.

Advanced Techniques and Automation - [Description] Focus on advanced feature engineering techniques and strategies for automated feature generation. The day will cover: domain-specific feature engineering (e.g., for time series data, text data, or geospatial data), feature interaction creation, automated feature selection, and the use of feature engineering pipelines using libraries like scikit-learn and featuretools. - Learn: Advanced feature engineering, domain-specific feature engineering (time series, text, geospatial), feature interaction, and automated feature selection. - Do: 1. Apply advanced feature engineering techniques to a dataset (e.g., time series features like rolling statistics and lags, text features like TF-IDF and word embeddings, geospatial features like distance calculations). 2. Implement feature interaction creation. 3. Explore and implement automated feature selection techniques (e.g., filter methods, wrapper methods, embedded methods). 4. Build a feature engineering pipeline using scikit-learn or featuretools for automated feature generation. - Resources: - Expected Outcomes: Mastery of advanced feature engineering techniques, the ability to automate feature generation processes, and understanding of the importance of feature selection.

[Description] This day is dedicated to in-depth time series analysis beyond the basics. Focus will be on techniques like: advanced decomposition methods (e.g., seasonal-trend decomposition based on Loess - STL), advanced forecasting models (e.g., Prophet, ARIMA with exogenous variables, state-space models), and time series anomaly detection. - Learn: Advanced time series decomposition (STL), forecasting models (Prophet, ARIMA with exogenous variables, state-space models), and anomaly detection. - Do: 1. Perform STL decomposition to identify seasonal and trend components. 2. Build and evaluate advanced forecasting models (e.g., Prophet, ARIMA with exogenous variables, state-space models). 3. Implement time series anomaly detection techniques (e.g., using moving average, z-score, or statistical process control methods). 4. Compare and contrast the performance of different time series analysis techniques. - Resources: - Expected Outcomes: Advanced understanding of time series analysis, the ability to apply advanced forecasting and anomaly detection techniques, and the ability to compare and evaluate different time series models.

[Description] This day focuses on working with geospatial data. It covers: handling geospatial data formats (e.g., shapefiles, GeoJSON), understanding coordinate reference systems (CRS), performing geospatial analysis (e.g., spatial joins, distance calculations, and buffer analysis), and integrating geospatial data with other data sources. - Learn: Geospatial data formats (shapefiles, GeoJSON), coordinate reference systems, geospatial analysis (spatial joins, distance calculations, buffer analysis). - Do: 1. Load, manipulate, and visualize geospatial data using libraries like GeoPandas. 2. Perform spatial joins and distance calculations. 3. Conduct buffer analysis and create spatial aggregations. 4. Integrate geospatial data with other data sources to extract insights. - Resources: - Expected Outcomes: Proficiency in working with geospatial data, the ability to perform geospatial analysis tasks, and the ability to integrate geospatial data with other datasets.

[Description] This day centers on advanced statistical inference and hypothesis testing. The focus is on: non-parametric tests, Bayesian methods, power analysis, and multiple hypothesis correction. The goal is to move beyond basic t-tests and understand how to correctly apply and interpret these advanced statistical methods. - Learn: Non-parametric tests, Bayesian methods, power analysis, multiple hypothesis correction. - Do: 1. Apply non-parametric tests when the assumptions of parametric tests are not met. 2. Perform Bayesian inference using libraries like PyMC3. 3. Conduct power analysis to determine the required sample size for detecting an effect. 4. Apply multiple hypothesis correction methods to control for false positives. - Resources: - Expected Outcomes: Advanced understanding of statistical inference, the ability to apply non-parametric tests and Bayesian methods, and the knowledge to correctly perform hypothesis testing in various scenarios.

[Description] This day is dedicated to automating the EDA process and generating comprehensive reports. This involves: creating reproducible EDA pipelines using scripting, incorporating automated analysis and visualizations, and generating interactive reports. The emphasis is on efficiency, clarity, and the ability to communicate findings effectively. - Learn: Automation of the EDA process, report generation, incorporating automated analysis and visualizations, and creating interactive reports. - Do: 1. Create a reproducible EDA pipeline using scripting (e.g., Python scripts or R scripts). 2. Automate data loading, cleaning, and transformation steps. 3. Generate automated analysis and visualizations. 4. Create an interactive report that includes a summary of key findings, data visualizations, and recommendations. - Resources: - Expected Outcomes: Ability to automate the EDA process, generate comprehensive and interactive reports, and communicate findings effectively to stakeholders.