Sitemap
Here's the full list of our articles, tutorials, and code examples on data science, machine learning, and data engineering.
Map by Annie Spratt, Unsplash.
17 minutes to read
As of Wednesday 25 May, 2022, there are 245 articles, tutorials, and code examples on data science, machine learning, and data engineering.
Tags
- Best practice
- Customer experience
- Customer segmentation
- Data visualisation
- Datasets
- Docker
- Feature engineering
- Google Analytics
- Google Cloud Platform
- Image recognition
- Marketing science
- Natural Language Processing
- Numpy
- Operations management
- Pandas
- Python
- Python SEO
- Quick guides
- SQL
- Technical ecommerce
- TensorFlow
- Time series
- Ubuntu
- Web analytics
- Web scraping
- XGBoost
- scikit learn
Data Science
- How to dedupe lists in Python with set() and intersection()
- How to use DATEDIFF() to calculate date differences in MySQL
- How to use DATE_ADD() and DATE_SUB() to add and subtract from dates
- How to use CASE for flow control in SQL statements
- How to use DATE_FORMAT() to reformat dates in MySQL
- How to use string functions in SQL statements
- How to use ORDER BY to sort an SQL result set
- How to use GROUP BY and HAVING in SQL statements
- How to use BETWEEN in SQL statements to return data between two values
- How to use SELECT, FROM, WHERE, and AND in SQL statements
- How to import a MySQL database
- How to read QR codes in Python using OpenCV
- How to calculate month start and month end dates in Python
- How to calculate ISO week numbers and start and end dates in Python
- How to use dictionaries in Python
- How to analyse Average Order Value with Jenks natural breaks classification
- How to check if URLs are redirected using Requests
- How to use operators in Google Analytics API queries
- How to calculate abandonment and completion rates using the Google Analytics API
- How to use try except for Python exception handling
- How to use the Pip Python package manager
- How to calculate the time difference between two dates in Pandas
- How to use the Dropbox API with Python
- How to send emails using the Mailchimp Transactional email API
- How to read an XML feed into a Pandas dataframe
- How to use the Pandas apply function on dataframe rows and columns
- How to create descriptive statistics using the Pandas describe function
- 16 Python web scraping projects for ecommerce and SEO
- A quick guide to customer retention
- 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 forecast Google Trends search data with NeuralProphet
- How to use CountVectorizer for n-gram analysis
- How to create Google Search Console time series forecasts using Neural Prophet
- 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 Dockerize a data science application
- How to backup a MySQL database using mysqldump, SSH and SCP
- 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
