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Here's the full list of our articles, tutorials, and code examples on data science, machine learning, and data engineering.
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13 minutes to read
As of Tuesday 12 October, 2021, there are 211 articles, tutorials, and code examples on data science, machine learning, and data engineering.
Data Science
- How to create a Google rank checker tool using Python
- How to use the Feefo API for ecommerce competitor analysis
- How to compare time periods using the Google Search Console API
- A quick guide to the RFM model for data scientists
- How to run time-based SEO tests using Python
- How to create content recommendations using TF IDF
- A quick guide to customer segmentation for B2B e-commerce
- How to detect Google Search Console anomalies
- How to identify SEO keyword opportunities with Python
- How to add days and subtract days from dates in Pandas
- How to analyse Google Analytics demographics and interests with GAPandas
- How to identify striking distance keywords with Python
- A quick guide to lead scoring for B2B e-commerce sites
- How to trigger marketing automations using the Mailchimp API in Python
- How to create monthly Google Search Console API reports with EcommerceTools
- How to use the Mailchimp Marketing Python API with Pandas
- How to use the eBay Finding API with Python
- How to export Zendesk tickets into Pandas using Zenpy
- How to query the Google Search Console API with EcommerceTools
- How to read Google Sheets data in Pandas with GSpread
- How to calculate the Lin Rodnitzky Ratio using GAPandas
- How to analyse product replenishment
- Data science courses for budding data scientists and data engineers
- A quick guide to customer segmentation for data scientists
- How to read an RSS feed in Python
- 19 Python SEO projects that will improve your site
- How to identify internal and external links using Python
- How to scrape Google results in three lines of Python code
- How to create a simple product recommender system in Pandas
- 15 ways you can use data science to boost ecommerce performance
- How to create PDF reports in Python using Pandas and Gilfoyle
- How to create monthly Google Analytics reports in Pandas
- How to segment your customers using EcommerceTools
- How to use EcommerceTools for technical SEO
- How to segment customers using RFM and ABC
- How to perform a customer cohort analysis in Pandas
- How to machine translate product descriptions
- How to identify near duplicate content using LMS
- How to create a product and price metadata scraper
- How to calculate CLV using BG/NBD and Gamma-Gamma
- How to assign RFM scores with quantile-based discretization
- How to analyse product consumption and repurchase rates
- How to use Spintax to create content and ad copy in Python
- How to scrape schema.org metadata using Python
- How to scrape People Also Ask data using Python
- How to scrape Google search results using Python
- How to join Google Analytics and Google Search Console data
- How to identify SEO keywords using Google Autocomplete
- How to engineer customer purchase latency features
- How to create targeted B2B company sector datasets
- How to create a UK data science jobs dataset
- How to create a dataset containing all UK companies
- How to count indexed pages using Python
- How to calculate safety stock and reorder point
- How to calculate operations management metrics in Python
- How to calculate marketing metrics in Python
- How to calculate customer experience metrics in Python
- How to calculate category management metrics in Python
- How to access the Google Knowledge Graph Search API
- A quick guide to catalogue marketing data science
- How to use Extruct to identify Schema.org metadata usage
- How to unzip files with Python
- How to unserialize serialized PHP arrays using Python
- How to send data to Google Analytics in Python with PyGAMP
- How to scrape Open Graph protocol data using Python
- How to scrape and parse a robots.txt file using Python
- How to scrape a site's page titles and meta descriptions
- How to scan a site for 404 errors and 301 redirect chains
- How to resize and compress images with TinyPNG
- How to parse XML sitemaps using Python
- How to parse URL structures using Python
- How to identify keyword cannibalisation using Python
- How to download files with Python
- How to calculate Economic Order Quantity in Python
- How to build a web scraper using Requests-HTML
- How to audit a site's Core Web Vitals using Python
- How to analyse Pandas dataframes using SQL with PandaSQL
- How to analyse non-ranking pages and search index bloat
- How to access the Google Search Console API using Python
- How to use Screaming Frog from the command line
- How to send a Slack message in Python using webhooks
- How to geocode and map addresses using GeoPy
- How to create paid search keywords using Pandas
- How to create a Python web scraper using Beautiful Soup
- How to write better code using DRY and Do One Thing
- How to visualise data with quirky hand-drawn plots
- How to visualise conversion funnels with Plotly
- How to use style guidelines to improve your Python code
- How to use SQLite in Python
- How to use operators in Python
- How to use lists in Python
- How to use Git for your data science projects
- How to use docstrings to improve your Python code
- How to use the Pandas value_counts() function
- How to query MySQL and other databases using Pandas
- How to open, read, and write to files in Python
- The four Python data science libraries you need to learn
- How to visualise text data using word clouds in Python
- How to visualise statistical distributions with Seaborn
- How to visualise data using Venn diagrams in Matplotlib
- How to visualise data using line charts in Seaborn
- How to visualise data using barplots in Seaborn
- How to visualise correlations using Pandas and Seaborn
- How to visualise categorical data in Seaborn
- How to install the NVIDIA Data Science Stack on Ubuntu 20.04
- How to create desktop data science apps using Nativefier
- How to create an Ubuntu desktop entry to run Jupyter
- How to build a data science workstation
- How to visualise analytics data using heatmaps in Seaborn
- How to visualise RFM data using treemaps
- How to visualise data using scatterplots in Seaborn
- How to visualise data using histograms in Pandas
- How to visualise data using boxplots in Seaborn
- How to select, filter, and subset data in Pandas dataframes
- How to resample time series data in Pandas
- How to reformat dates in Pandas
- How to import data into Pandas dataframes
- How to group and aggregate transactional data using Pandas
- How to analyse search traffic using the Google Trends API
- How to use identify visually similar images using hashing
- How to create an ABC XYZ inventory classification model
- How to create an ABC inventory classification model
- How to connect to MySQL via an SSH tunnel in Python
- How to calculate relative dates for Google Analytics queries
- How to create an ecommerce trading calendar using Pandas
- Dell Precision 7750 mobile data science workstation review
- How to use the Pandas melt function to reshape wide format data
- How to use the Apriori algorithm for Market Basket Analysis
- How to scrape JSON-LD competitor reviews using Extruct
- How to scrape competitor technology data in Python
- How to create a Pandas dataframe
- How to create a collaborative filtering recommender system
- How to use GAPandas to view your Google Analytics data
- How to use scikit-learn datasets in data science projects
- How to use Python regular expressions to extract information
- How to engineer date features using Pandas
- How to create a Python virtual environment for Jupyter
Machine Learning
- How to create a contractual churn model in scikit-learn
- How to avoid model overfitting with early stopping rounds
- How to classify customer support tickets using Naive Bayes
- How to use pipelines in your machine learning models
- How to infer the effects of marketing using the Causal Impact model
- How to engineer new features using Decision Tree models
- How to create an ecommerce purchase intention model in Python
- How to create a classification model using XGBoost in Python
- How to predict employee churn using CatBoost
- How to auto-generate meta descriptions with EcommerceTools
- How to create a basic Marketing Mix Model in scikit-learn
- How to make time series forecasts with Neural Prophet
- How to use the Isolation Forest model for outlier detection
- How to use k means clustering for customer segmentation
- How to identify the causes of customer churn
- How to create ecommerce anomaly detection models
- How to create a non-contractual churn model for ecommerce
- How to classify customer service emails with Bart MNLI
- How to auto-generate product summaries using deep learning
- How to assess product copy using EQA models
- How to use bagging, boosting, and stacking in ensembles
- How to perform time series decomposition
- How to find spelling and grammar issues on product pages
- How to create a product matching model using XGBoost
- How to create a Naive Bayes product classification model
- How to use knee point detection in k means clustering
- How to preprocess text for NLP in four easy steps
- How to detect sarcasm using machine learning
- How to detect fake news with machine learning
- A quick guide to search intent classification for SEO
- How to create a dataset for product matching models
- How to use SMOTE for imbalanced classification
- How to use Recursive Feature Elimination in your models using RFECV
- How to use model selection and hyperparameter tuning
- How to use transform categorical variables using encoders
- How to save and load machine learning models using Pickle
- How to create ecommerce sales forecasts using Prophet
- How to create a response model to improve outbound sales
- How to create a linear regression model using Scikit-Learn
- How to speed up the NLP text annotation process
- How to create synthetic data sets for machine learning
- How to create image datasets for machine learning models
- How to bin or bucket customer data using Pandas
- How to annotate training data for NLP models using Doccano
- Ecommerce and marketing data sets for machine learning
- How to use the BG/NBD model to predict customer purchases
- How to use NLP to identify what drives customer satisfaction
- How to use Category Encoders to encode categorical variables
- A quick guide to Product Attribute Extraction models
- A quick guide to Next-Product-To-Buy models
- A quick guide to machine learning
- A quick guide to machine learning uplift models
- A quick guide to Learning to Rank models
- How to use Natural Language Understanding models
- How to tune model hyper-parameters with grid search
- How to test your Keras, CUDA, CuDNN, and TensorFlow install
- How to perform facial recognition in Python
- How to separate audio source data using Spleeter
- How to build the 'Hotdog , not Hotdog' image classifier
- How to create a neural network for sentiment analysis
- How to use your GPU to accelerate XGBoost models
- How to use mean encoding in your machine learning models
- How to interpret the confusion matrix
- How to impute missing numeric values in your dataset
Data Engineering
- How to create a Google Service Account client secrets JSON key
- The difference between data scientists and data engineers
- How to use Google Secret Manager to improve data security
- How to import data into Google Data Studio using Python
- How to import data into BigQuery using Pandas and MySQL
- How to create a BI platform using Apache Superset
- How to use Apache Druid for real-time analytics data storage
- How to set up a Docker container for your MySQL server
- How to create ecommerce data pipelines in Apache Airflow
- How to use Docker for your data science projects
