All the interesting stuff about Data Science that i've found.
Most are from Toward Data Science (TDS) blog.
(I have a few hundred blog posts in the queue to post... 😟)
- Hands-on Tutorial: How To Improve Your Problem-Solving Skills As A Programmer
- How to Find Out the Bottleneck of My Python Code
- Hasan Akçay - E-Commerce Forecasting Fbprophet + Optuna
- Albert Suryadi - How to create a simple churn model
- Introduction to time series data - An introduction to using time series data for forecasting problems
- 8 Visualizations with Python to Handle Multiple Time-Series Data
- An End-to-End Project on Time Series Analysis and Forecasting with Python
- Time Series Analysis in Python: An Introduction
- The Complete Guide to Time Series Analysis and Forecasting
- Basic Time Series Manipulation with Pandas
- How (not) to use Machine Learning for time series forecasting: Avoiding the pitfalls
- 3 facts about time series forecasting that surprise experienced machine learning practitioners
- Top 5 Time Series Analytics
- Benchmarking time series datasets with style - How to cross-check/verify large time-series datasets?
- Time-Series Analysis: Hands-On with SciKit-Learn Feature-Engineering
- Machine Learning for Retail Sales Forecasting — Features Engineering
- Olympic Medal Numbers Predictions with Time Series, Part 1: Data Cleaning
- Olympic Medal Numbers Predictions with Time Series, Part 2: Data Analysis
- Olympic Medal Numbers Predictions with Time Series, Part 3: Time Series Forecasting
- Open Machine Learning Course. Topic 9. Part 1. Time series analysis in Python
- Open Machine Learning Course. Topic 9. Part 2. Predicting the future with Facebook Prophet
- Aileen Nielsen - Practical Time Series Analysis: Prediction with Statistics and Machine Learning (2019)
- Ben Auffarth - Machine Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine learning methods (2021)
- Brockwell & Davis - Introduction to Time Series and Forecasting (2016)
- Predicting Netflix stock prices using Machine Learning, using Python
- Factor Investing with Python
- Factor Investing with Python #1 Data
- Factor Investing with Python #2 API
- Examining Financial Data to Make Smart Investing Decisions - Portfolio Optimization of High-Value Tech Stocks
- Access Companies SEC Filings Using Python
- “All Weather” Portfolios
- Analysing Institutional Investors Stocks Holdings with Python
- Backtesting Piotroski Score in the Current Time Period Using Python
- How to fetch stock data using Python (and make your code brilliant)
- How to fetch stock data using Python (and make your code brilliant). Part 2.
- Creating a Simple Stock Application with Python and APIs
- Python Assisted Trading Decisions
- Building an Economic Calendar using Python and APIs
- Python Invest
- Datacamp - Applied Finance in Python
- Datacamp - Finance Fundamentals in Python
- Forecasting Stock Prices using XGBoost (Part 1/5)
- Forecasting Stock Prices using XGBoost (Part 2/5)
- Forecasting Stock Prices using XGBoost (Part 3/5)
- Forecasting Stock Prices using XGBoost (Part 4/5)
- Forecasting Stock Prices using XGBoost (Part 5/5)
- Machine Learning for Financial Risk Management with Python: Algorithms for Modeling Risk
- Python for Finance - Second Edition: Apply powerful finance models and quantitative analysis with Python
- Python for Algorithmic Trading: From Idea to Cloud Deployment
- Mastering Python for Finance - Second Edition: Implement advanced state-of-the-art financial statistical applications using Python
- Python for Finance Cookbook: Over 50 recipes for applying modern Python libraries to financial data analysis
- Alicia Horsch - Hypothesis testing for data scientists
- Mahesh - Everything You Need To Know about Hypothesis Testing — Part I
- Mahesh - Everything You Need To Know about Hypothesis Testing — Part II
- Karan Oberoi - Hypothesis Testing: Design of Experiments
- Alison Yuhan Yao - 5 ways to Increase Statistical Power
- erma0x - Hypothesis testing in Data Science
- Bruno Luiz Mendes - Making a Data Science easier: What Is Hypothesis Testing — Introduction
- Harika Bonthu - Hypothesis Testing Made Easy For The Data Science Beginners!
- Nitin Chauhan - Understanding Hypothesis Testing for Data Science
- An Interactive Guide to Hypothesis Testing in Python
- Three Common Hypothesis Tests All Data Scientists Should Know
- P-Values: Innocent Until Proven Guilty - A journey through statistical hypothesis, linear regressions and multiverses
- Why is statistics important in Data Science, Machine learning, and Analytics
- Christian Koguchi - Using Data Science to Estimate Your Chances of Surviving in Squid Game
- Implementing an Enterprise Recommendation System - An end-to-end look at implementing a “real-world” content-based recommendation system
- Susan Li - Exploring, Clustering and Mapping Toronto’s Crimes
- Chris Brownlie - Patterns in Crime - An analysis of police force data from the last 3 years
- WY Fok - A demonstration of carrying data analysis - Crimes in Denver EDA
- Anthony Agnone - Fight San Francisco Crime with fast.ai and Deepnote
- Using Folium on Police Data
- Free 10 Hour Machine Learning Course - FreeCodeCamp
- 10 Popular Machine Learning Algorithms on a Nutshell
- The Hundred-Page Machine Learning Book
- Learning to rank: A primer
- ML for Beginners - a project based approach
- Google - Introduction to Machine Learning Problem Framing
- Google - Machine Learning Crash Course
- 60 Days of Machine Learning - Amazing stuff with MANY projects!! From beginner to NLP.
- Learning to Rank: A Complete Guide to Ranking using Machine Learning
- Types of Machine Learning Algorithms You Should Know
- Which Machine Learning Algorithm Should You Use By Problem Type?
- A Deep Dive into Curve Fitting for ML
- Building an Open Source ML Pipeline: Part 1
- Building an Open Source ML Pipeline: Part 2 - Event-Driven Data Processing with Argo Events and Argo Workflows
- How to Secure Your AWS API Gateways with CloudFormations
- MLops — Data And Model Versioning With DVC And Azure Blob Storage - An end-to-end approach on how to keep track of your data — less than 5 minutes!
- NLP MLops Project With DagsHub — Multi-Language Sentiment Classification Using Transformers — Part 1
- NLP MLops Project With DagsHub — Deploy Your Streamlit App On AWS EC2 Instance — Part 2
- Step-by-step Approach to Build Your Machine Learning API Using Fast API
- Modularise your Notebook into Scripts
- Why does data need a massage? - Data Transformation
Databricks, PySpark and Cloud providers.
- Beginner’s Guide to Machine Learning with Big Data
- The Top Clouds Evaluated Such That You Don’t Need to Repeat Our Mistakes
- A (not so) Comprehensive Guide on Databricks for Beginners
- Get started with Databricks as a data scientist
- Learn to Use Databricks for Data Science
- How Starbucks Forecasts Demand at scale with Facebook Prophet and Azure Databricks
- Databricks Certified Data Scientist Professional
- Databricks Certified Machine Learning Professional
- Spark & Databricks: Important Lessons from My First Six Months
- Getting Started with Databricks — Analyzing COVID-19
- Introducing Azure Databricks for Data Science
- Get started Spark with Databricks and PySpark
- What does Databricks do?
- Elegant CICD with Databricks notebooks - How to release Databricks Notebook artifacts with Azure DevOps
- A love-hate relationship with Databricks Notebooks
- Free Cloud CPUs for Data Science and Machine Learning
- PySpark for Beginners – Take your First Steps into Big Data Analytics (with Code)
- An Introduction to Data Analysis using Spark SQL
- How to use a Machine Learning Model to Make Predictions on Streaming Data using PySpark
- How to use a Machine Learning Model to Make Predictions on Streaming Data using PySpark - Predicting users who canceled their service
- Exploratory Data Analysis using Spark
- Big Data Fundamentals with PySpark
- Introduction to PySpark
- Feature Engineering with PySpark
- Building Recommendation Engines with PySpark
- Machine Learning with PySpark
- Hasan Akçay - 10 Notebooks That Made Me a Kaggle Master
- Kaggle - From Beginner to Intermediate Level - Including SQL, Dataviz, Python basics and Time Series models.
Not yet categorized.
- Hasan Akçay - What Are Baseline Models and Benchmarking For Machine Learning, Why We Need Them? Part 1 Classification
- Reza Bagheri - Introduction to SHAP Values and their Application in Machine Learning
- 100+ Most Valuable Github Repositories for Machine Learning
- 6 Sklearn Mistakes That Silently Tell You Are Rookie
- Documenting Python code with Sphinx
- Create your own GPU accelerated Jupyter Notebook Server for Google Colab using Docker
- I’m a Self-Taught Data Scientist. Here Are My 3 Suggestions for Newcomers
- Typical 8-Step A/B Test Workflow for Data Scientists in 2022
- An Agile Framework for AI Projects — Development, QA, Deployment and Maintenance
- Feature Selection for the Lazy Data Scientist
- Productivity Tips for Data Scientists
- How Data Scientists Can Develop Business Acumen
- Top Qualities Hiring Managers Look For In Data Scientist Candidates
- Claus O. Wilke - Fundamentals of Data Visualization (book)
- 4 tricks you should know to parse date columns with Pandas read_csv()