Data Scientist — Programming Fundamentals (Python/R)

Mastering Speed - Description: Learn and apply Python's tools for concurrency and parallelism to accelerate data processing tasks. Focus on threads, processes, the asyncio library (coroutines, async/await), and multiprocessing to understand how to overcome the Global Interpreter Lock (GIL). Compare and contrast the different approaches, identifying their strengths and weaknesses for various data science scenarios (e.g., data loading, feature engineering, model training, and hyperparameter tuning). - Resources/Activities: - Study the threading, multiprocessing, and asyncio modules in the Python standard library. - Read and practice the "Python Concurrency with the multiprocessing module" documentation from the python.org website. - Implement a multithreaded and a multiprocessing program to scrape data from a large website. Compare performance. - Rewrite the scraping script from the previous step using asyncio and aiohttp for asynchronous web requests. Compare performance again. - Implement an example of parallel hyperparameter tuning (e.g., using multiprocessing or concurrent.futures) on a small-scale machine learning model. - Explore and utilize the concurrent.futures module for high-level concurrent task management. - Expected Outcomes: A solid understanding of concurrency and parallelism in Python. Ability to select the appropriate technique (threading, multiprocessing, asyncio) for a given data processing task. Proficiency in optimizing code for speed by leveraging parallel execution. Familiarity with the GIL and its impact on performance.

• Description: Dive deep into efficient data manipulation techniques in R, focusing on the data.table package for fast data aggregation, filtering, and transformation. Learn about memory management and vectorization to improve performance. Explore code profiling and optimization tools to identify bottlenecks in R code and learn how to write highly optimized R code. - Resources/Activities: - Study the official data.table documentation and tutorials. - Work through the "Introduction to data.table" vignette. - Re-implement common data manipulation tasks (e.g., joins, aggregations, filtering) using data.table and compare performance with base R and dplyr. Use large datasets. - Explore tools like profvis and Rprof to profile your R code and identify performance bottlenecks. - Learn about and apply techniques for vectorization and avoiding loops. - Experiment with various memory management techniques in R, such as using gc() to clean up memory and profiling memory usage with tools like pryr::mem_used(). - Expected Outcomes: Expertise in using data.table for high-performance data manipulation. Ability to profile and optimize R code for speed and efficiency. A strong understanding of R's memory management and vectorization capabilities. Proficiency in writing production-ready R code for data science tasks.

• Description: Explore functional programming principles in R and how they can improve code clarity, maintainability, and reusability. Learn how to design and build R packages, including documentation, testing, and version control. Implement coding best practices, adhering to established style guides, and master advanced debugging techniques in R. - Resources/Activities: - Study functional programming concepts in R, focusing on functions as first-class objects, closures, and the use of the purrr package for functional programming. - Create a basic R package using devtools or usethis, including documentation (using roxygen2), testing (using testthat), and version control (using Git). - Follow the style guide of the tidyverse (e.g., the tidyverse style guide) to write clean and readable R code. Use lintr for automated code linting. - Practice advanced debugging techniques using debug, browser, and interactive debugging tools. - Study and understand how to use different documentation tools like roxygen2 and pkgdown - Expected Outcomes: Understanding of functional programming in R. Ability to create and maintain R packages with proper documentation, testing, and version control. Proficiency in writing clean, readable, and maintainable R code. Expertise in advanced debugging techniques.

Beyond the Basics - Description: Deepen your knowledge of NumPy and SciPy, focusing on advanced features and optimization strategies. Explore advanced array manipulation in NumPy, including broadcasting, indexing, and advanced slicing. Understand and apply specialized SciPy functions for signal processing, image processing, sparse matrix operations, and optimization. This day will highlight the critical role these libraries play in optimized scientific computing. - Resources/Activities: - Review the NumPy and SciPy documentation. - Practice advanced array manipulation techniques using NumPy, including broadcasting and advanced indexing (e.g., fancy indexing and Boolean masking). - Experiment with SciPy's signal processing functions to denoise a noisy audio signal. - Apply SciPy's image processing functionalities (e.g. convolution, filtering) to enhance a low-quality image. - Implement a sparse matrix and use scipy’s sparse modules to solve a large system of linear equations. - Explore and apply SciPy optimization algorithms (e.g., scipy.optimize.minimize) to optimize a complex objective function related to a real-world data science problem (e.g., fitting a model to data). - Examine NumPy and SciPy's underlying performance optimizations, including vectorization and BLAS/LAPACK integration. - Expected Outcomes: Mastery of advanced NumPy and SciPy features. Ability to effectively leverage these libraries for complex data analysis, scientific computing, and model development. Understanding of performance optimization techniques within these libraries.

Production Level - Description: Focus on building and deploying machine learning models in R for real-world applications. Go beyond basic modeling to explore advanced techniques, including model evaluation, feature engineering, and ensemble methods. Learn how to deploy models using tools like plumber and shiny. Dive deep into the model life cycle and how to build production-level data science pipelines. - Resources/Activities: - Select a real-world dataset and implement advanced feature engineering techniques (e.g., using domain knowledge, interactions, and transformations). - Build and evaluate several machine learning models (e.g., using caret, glmnet, xgboost, or ranger) to tackle a complex data science problem (e.g., classification, regression). - Implement cross-validation and hyperparameter tuning to optimize model performance. - Apply ensemble methods (e.g., stacking, blending) to improve predictive accuracy. - Learn to deploy your machine learning model using plumber (for creating APIs) or shiny (for building interactive web applications). - Learn how to integrate model monitoring and retraining pipelines into the deployment process. - Expected Outcomes: Ability to build, evaluate, and deploy production-ready machine learning models in R. Proficiency in advanced feature engineering and ensemble methods. Experience with model deployment tools and techniques for creating maintainable data science pipelines.

• Description: Consolidate all the learned knowledge from the past six days by working on an end-to-end data science project. The project should incorporate both Python and R, demonstrating how to seamlessly integrate the two languages for complex data analysis tasks. The project will involve data loading, cleaning, feature engineering, modeling, model evaluation, and deployment, using the skills learned throughout the week. - Resources/Activities: - Choose a project with a clearly defined problem and dataset (e.g., a time-series forecasting problem, a fraud detection challenge, or a sentiment analysis project). - Use Python for data loading, cleaning, and preprocessing (e.g., using Pandas, NumPy, or other Python libraries). - Use R for modeling and statistical analysis, leveraging libraries like caret, glmnet, or xgboost, data.table. - Explore integration techniques. (e.g., calling R from Python using rpy2 or reticulate or by calling Python from R.) - Build an end-to-end data pipeline to automate data processing and model deployment. - Document your project thoroughly (code, explanations, results, and insights). - Expected Outcomes: Successful completion of a complex, end-to-end data science project demonstrating proficiency in both Python and R. Ability to integrate the two languages effectively. Production-ready code, a clear and concise presentation, and demonstration of a strong understanding of data science principles and techniques.