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Erdogan Taskesen
Erdogan Taskesen

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Published in

Towards Data Science

·Pinned

How to Find the Best Theoretical Distribution for Your Data

Knowing the underlying data distribution is an essential step for data modeling and has many applications, such as anomaly detection, synthetic data creation, and data compression. — Knowing the underlying (probability) distribution of your data has many modeling advantages. The easiest manner to determine the underlying distribution is by visually inspecting the random variable(s) using a histogram. With the candidate distribution, various plots can be created such as the Probability Distribution Function plot (PDF/CDF), and the QQ…

Probability Distributions

19 min read

How to Find the Best Theoretical Distribution for Your Data
How to Find the Best Theoretical Distribution for Your Data
Probability Distributions

19 min read


Published in

Towards Data Science

·Pinned

Step-by-Step Guide to Generate Synthetic Data by Sampling From Univariate Distributions

Learn how to create synthetic data in case your project runs low on data or use it for simulations — Data is the fuel in Data Science projects. But what if the observations are scarce, expensive, or difficult to measure? Synthetic data can be the solution. Synthetic data is artificially generated data that mimics the statistical properties of real-world events. I will demonstrate how to create continuous synthetic data by…

Synthetic Data

10 min read

Step-by-Step Guide to Generate Synthetic Data by Sampling From Univariate Distributions.
Step-by-Step Guide to Generate Synthetic Data by Sampling From Univariate Distributions.
Synthetic Data

10 min read


Published in

Towards Data Science

·Pinned

D3Blocks: The Python Library to Create Interactive and Standalone D3js Charts.

Create interactive, stand-alone, and visually attractive charts that are built on the graphics of d3 javascript (d3js) but configurable with Python. — Python has become one of the most popular programming languages to analyze and visualize your data. Visualizing can be the key to success in projects because it can reveal hidden insights in the data, and improve understanding. The best way to understand and explain the data is by making it…

Python

11 min read

D3Blocks: The Python Library to Create Interactive and Standalone D3js Charts.
D3Blocks: The Python Library to Create Interactive and Standalone D3js Charts.
Python

11 min read


Published in

Towards Data Science

·May 22

The Power of Bayesian Causal Inference: A Comparative Analysis of Libraries to Reveal Hidden Causality in Your Dataset.

Reveal the hidden causal variables in your data set by using the best-suited Bayesian causal inference library: a comparison with hands-on examples of five popular libraries. — Understanding the causal effect of variables in systems or processes is very valuable. There are a number of Python libraries that can assist in determining causal relationships. I will compare five popular causal inference libraries in their functionality, ease of use, and flexibility. Each is accompanied by hands-on examples. The…

Causality

20 min read

The Power of Bayesian Causal Inference: A Comparative Analysis of Libraries to Reveal Hidden…
The Power of Bayesian Causal Inference: A Comparative Analysis of Libraries to Reveal Hidden…
Causality

20 min read


Published in

Towards Data Science

·May 10

From Clusters To Insights; The Next Step

Learn how to quantitatively detect which features drive the formation of the clusters — Cluster analysis is a great technique for identifying groups with similar patterns. However, once clusters are formed, it can remain challenging to determine the driving features behind the clusters. But this step is crucial to reveal valuable insights that may have been missed before and can be used for decision-making…

Clustering

9 min read

From Clusters To Insights; The Next Step
From Clusters To Insights; The Next Step
Clustering

9 min read


Published in

Towards Data Science

·Apr 26

From Data to Clusters: When is Your Clustering Good Enough?

Hidden gems can be found using clustering approaches but you need the right clustering method and evaluation approach to make sensible clusters. Learn how to find them in four steps. — With unsupervised cluster analysis, we can group observations with similar patterns, and reveal (hidden) trends in the data. The use of cluster evaluation methods helps to determine the clustering tendency, quality, and optimal number of clusters. In this blog, we will dive into cluster evaluation methods, learn how to interpret…

Clustering

17 min read

From Data to Clusters; When is Your Clustering Good Enough?
From Data to Clusters; When is Your Clustering Good Enough?
Clustering

17 min read


Published in

Towards Data Science

·Mar 11

Outlier Detection Using Principal Component Analysis and Hotelling’s T2 and SPE/DmodX Methods

Thanks to PCA’s sensitivity, it can be used to detect outliers in multivariate datasets. — Principal Component Analysis (PCA) is a widely used technique for dimensionality reduction while preserving relevant information. Due to its sensitivity, it can also be used to detect outliers in multivariate datasets. Outlier detection can provide early warning signals for abnormal conditions, allowing experts to identify and address issues before they…

Multivariate Analysis

11 min read

Outlier Detection Using Principal Component Analysis and Hotelling’s T2 and SPE/DmodX Methods
Outlier Detection Using Principal Component Analysis and Hotelling’s T2 and SPE/DmodX Methods
Multivariate Analysis

11 min read


Published in

Towards Data Science

·Feb 18

Outlier Detection Using Distribution Fitting in Univariate Datasets

Learn how to detect outliers using Probability Density Functions for fast and lightweight models and explainable results. — Anomaly or novelty detection is applicable in a wide range of situations where a clear, early warning of an abnormal condition is required, such as for sensor data, security operations, and fraud detection among others. Due to the nature of the problem, outliers do not present themselves frequently, and due…

Anomaly Detection

16 min read

Outlier Detection Using Distribution Fitting in Univariate Datasets
Outlier Detection Using Distribution Fitting in Univariate Datasets
Anomaly Detection

16 min read


Published in

Towards Data Science

·Dec 14, 2022

The Starters Guide to Release your Python Package in PyPi

A step-by-step guide to effectively release your Python package in the Python Package Index (PyPI) to pip install it — Releasing a package in PyPi and installing it with pip has great advantages as it will automatically resolve dependencies, and it becomes accessible on any machine. However, publishing an open-source Python Package requires some preparation steps, such as the unambiguous structuring of your directory, versioning, archive building, and publishing the…

Python

11 min read

The Starters Guide to Release your Python Package in PyPi.
The Starters Guide to Release your Python Package in PyPi.
Python

11 min read


Published in

Towards Data Science

·Nov 17, 2022

Get the Most Out of Your Scatterplot by Making It Interactive Using D3js and Python.

Scatterplots are extremely useful for visualizing relationships between two sets of numerical variables. It is even more insightful when it is interactive with zooming and brushing capabilities. — The scatter plot is perhaps the most well-known chart to visualize numerical variables. Such basic charts are very useful from time to time, especially when they are interactive with brushing and zooming capabilities. In this blog post, I will demonstrate how to create interactive scatter plots with varying colors, sizes…

Scatter Plots

7 min read

Get the most out of your Scatterplot by making it interactive using D3js and Python.
Get the most out of your Scatterplot by making it interactive using D3js and Python.
Scatter Plots

7 min read

Erdogan Taskesen

Erdogan Taskesen

1.5K Followers

Machine Learning | Statistics | D3js visualizations | Lead Data Scientist | Ph.D | erdogant.github.io

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