Further reading Blog

3 ways Hex makes leading a support team less stressful

Our Product Expert Lead, Bre, shares how to use conversational analytics to explain ticket spike and win headcount.

What does it mean to be right?

The foreword for "Effective Data Analysis" by Mona Khalil

Detecting Seasonality Through Autocorrelation

Autoencoders and methods like ACF and PACF effectively identify seasonality in time series data, enhancing business forecasting.

Unveiling Patterns Using Time Series Visualization

The article uses a mock AirPassengers dataset to visually demonstrate trends and seasonal patterns in the airline industry.

Understanding the Importance of Stationarity in Time Series

Stationarity, crucial for reliable time series analysis, is confirmed through tests like ADF and KPSS, facilitating easier modeling and interpretation.

The Essential Steps in Data Preprocessing for Different Data Formats

Data preprocessing refines raw data for accurate analysis by handling missing values, normalizing and processing data.

The Importance of Data Cleaning in EDA

Why and how to clean data

When To Choose Density-Based Methods

Compare, k-means, DBSCAN and Hierarchical Clustering

Scaling Hierarchical Clustering

Learn how Fastcluster, Apache Spark, and GPU-accelerated solutions can help.

Unveiling patterns in unlabeled data with k-means clustering

Sift through the noise and categorize datasets into actionable segments

How Overfitting Ruins Your Feature Selection

Learn how to prevent overfitting from impacting your model.

Using Autoencoders for Feature Selection

Learn how to use autoencoders which are a class of artificial neural networks for data compression and reconstruction.

Detecting and Remedying Multicollinearity in Your Data Analysis

Learn to ensure the validity, reliability, and accuracy of your model.

How To Use Univariate Analysis in Your Data Exploration

Learn how to describe, summarize, and find patterns in the data from a single variable.

Accessing data in Jupyter with Python

Learn how to access the most popular data sources with Python in Jupyter Notebooks