My guides on data analysis and programming

My data analysis guides, particularly related to social science and Python.

September 1, 2023

Welcome to the Guides section of my website. I daily work with complex datasets and hope to share some of my knowledge here.

Latest guides

Topics covered

The field of data science evolves fast - having clear, accessible, and practical guides to navigate this landscape is thus of great importance. I’ll be sharing a series of guides that aim to help data scientist navigate the ever-changing landscape of data analysis, machine learning, and artificial intelligence. Whether you’re looking to understand the nuances of neural network architectures or you’re simply trying to figure out the most efficient way to clean and preprocess your data, you’ll find resources here that are tailored to your needs.

The guides will cover a broad range of topics, including but not limited to:

  1. Data Wrangling: Learn the art of transforming raw data into a format that’s ready for analysis. We’ll cover everything from dealing with missing values to encoding categorical variables, and from SQL tricks to pandas wizardry.

  2. Exploratory Data Analysis (EDA): Before diving into complex models, it’s crucial to understand the data at hand. I’ll share techniques for visualizing distributions, identifying outliers, and uncovering patterns through statistical summaries.

  3. Machine Learning Pipelines: Discover how to build reproducible pipelines that take you from raw data to predictive models. We’ll explore feature engineering, model selection, hyperparameter tuning, and cross-validation strategies.

  4. Deep Learning: For those looking to delve into the depths of neural networks, I’ll provide clear explanations of concepts like backpropagation, regularization, and the various flavors of deep learning architectures, from CNNs to RNNs and GANs.

  5. Big Data Technologies: As datasets grow, so does the complexity of handling them. Learn about the ecosystems of big data technologies like Hadoop, Spark, and Dask, and how they enable scalable data processing.

  6. Data Ethics and Privacy: With great power comes great responsibility. We’ll discuss the ethical considerations of data science, including privacy concerns, bias in AI, and the importance of transparent and fair algorithms.

  7. Productionizing Models: It’s not just about building models; it’s about putting them into production. I’ll guide you through the process of deploying models into live environments, monitoring their performance, and ensuring they remain accurate and reliable over time.

  8. Continual Learning: Stay ahead of the curve with strategies for continual learning in data science. I’ll share resources for keeping your skills sharp, from online courses and workshops to conferences and research papers.

Each guide will be crafted with the precision and clarity that’s characteristic of the Hackernews community. Expect a no-nonsense, straight-to-the-point style that respects your time and intelligence. Moreover, I encourage active discussion and collaboration, much like the vibrant exchanges you see on HN threads. Your questions, insights, and experiences will enrich these guides and help create a living resource that benefits the entire community.

So whether you’re a data science newbie looking to get your feet wet, or a battle-hardened analyst seeking to refine your craft, you’ve come to the right place. Let’s embark on this journey together, and turn data into wisdom.