(testing signal)

Tag: datavisualization

What Is Business Intelligence?

Business Intelligence (BI) includes the technologies and tools used to analyze and report on different business operations. Business Intelligence uses raw data stored in varying data warehouses, data marts, data lakes, and other storage platforms, and transforms it into actionable knowledge/information assets. Such elements include dashboards, spreadsheets, data visualizations, reports, and many others. According to DATAVERSITY’s Business Intelligence vs. Data Science…

https://www.dataversity.net/what-is-business-intelligence/…

Deep learning model to predict mRNA degradation

We will be using TensorFlow as our main library to build and train our model and JSON/Pandas to ingest the data. For visualization, we are going to use Plotly and for data manipulation Numpy.# Dataframeimport jsonimport pandas as pdimport numpy as np# Visualizationimport plotly.express as px# Deeplearningimport tensorflow.keras.layers as Limport tensorflow as tf# Sklearnfrom sklearn.model_selection import train_test_split#Setting seedstf.random.set_seed(2021)np.random.seed(2021)Target…

Continue reading: https://pub.towardsai.net/deep-learning-model-to-predict-mrna-degradation-1533a7f32ad4?source=rss—-98111c9905da—4

Source: pub.towardsai.net

Are we alone in the universe? — Data Analysis and Data Visualization of UFO sightings with R

How to analyze and visualize data of UFO sightings of the last century in the USA and the rest of the worldDashboard to see the generated plots about UFO Sightings. The link at the end of the article

One of the questions that human beings can ask themselves is whether we are alone in the universe. I think it would be ridiculous and extremely vain to assume that we are the only civilization with enough intelligence to visit other worlds in the universe. What if other civilizations more intelligent than us have already visited us? What if they are watching us right now?… Read more...

Use These Unique Data Sets to Sharpen Your Data Science Skills

Want to get your hands on some real-world data sets right now? Kick off your bootcamp prep with this list of hot-button data sets curated to help you hone different data science skills.

Sponsored Post.

Want to warm up your data science skills before jumping into a bootcamp program? Aspiring data scientists can practice key techniques like data cleaning, data analysis, data visualization and even machine learning with free, publicly available data sets. Hands-on data science exploration is one of the most effective ways to prepare for a data science bootcamp. In addition to learning more about your strengths, interests, and the skills you’ll need to grow, you’ll also gain experience working with the intricacies and idiosyncrasies of real-world data.


Better Data Visualization with Dual Axis Graphs in Python

First, we need to create an empty subplots figure using make_subplots (which we imported earlier). We’ll also define two variables to name our target categories.

# making dual axis and defining categories
fig = make_subplots(specs=[[{"secondary_y": True}]])
category_1 = "Groceries"
category_2 = "Restaurant"

We’re not outputting anything yet, but it’s good to note that in the make_subplots method we’re passing "secondary_y": True inside specs to make sure we can properly implement the dual-axis later on.

Next, we’ll manually create the first line in our line chart.

# creating first plot
y=df_grouped.loc[df_grouped["Category"]==category_1, "Amount"],
x=df_grouped.loc[df_grouped["Category"]==category_1, "Date"],

Before, using Plotly Express made it really easy for us to just pass one line of code to generate everything.


Enchanced Tabular Data Visualization (Pandas)

Simple but efficient techniques to improve pandas dataframe representation

From Pixabay

In this article, we’ll discuss some useful options and functions to efficiently visualize dataframes as a set of tabular data in pandas. Let’s start with creating a dataframe for our further experiments:

import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(20, 40))# Renaming columns
df.columns = [x for x in 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMN']
# Adding some missing values
df.iloc[3,4] = np.nan
df.iloc[2,0] = np.nan
df.iloc[4,5] = np.nan
df.iloc[0,6] = np.nan
Image by Author

Attention: the code from this article was run in pandas version 1.3.2. Some of the functions are quite new and will throw an error in the older versions.


Tips For Data Mapping And Replacing With Pandas And Numpy

In order to summarize main characteristics, spot anomalies, and visualize information, you should know how to rearrange and transform datasets. In other words, transforming data helps you play with your dataset, make sense of it, and gather as many insights as you can. In this article, I will show you some of my commonly used methods to play with data, and hope this would be helpful.

I will create a simple score dataset, which includes information about different classes’ grades.

info = {'Class':['A1', 'A2', 'A3', 'A4','A5'],
'AverageScore':[3.2, 3.3, 2.1, 2.9, 'three']}
data = pd.DataFrame(info)


Fig 1: DataFrame

As the Average Score of Class A5 in our data is a string object, I want to replace it with a corresponding number for easier data manipulation.


Interactive Visualization with Plotly and Datapane

Visualization is a critical aspect of any analysis. But static plots limit our ability to understand data. Plotly and Datapane address this issue.

This article will show how to create interactive candlestick time-series plots to visualization the…

Continue reading: https://towardsdatascience.com/interactive-visualization-with-plotly-and-datapane-97472017cb54?source=rss—-7f60cf5620c9—4

Source: towardsdatascience.com

Real-Time Histogram Plots on Unbounded Data – KDnuggets

By Romain Picard, a data science engineer, working on the Digital TV.


Everyone’s data science toolbox contains some base tools. We use them systematically up to the point that we consider their usage for granted. Histograms are one of them. We use them for visualization in the exploration phase, during validation of the data distribution type before choosing a model, and many other things (sometimes without even being aware of it). Unfortunately, using histograms on real-time data is not possible with most libraries.

One typically uses histograms on bounded data like a CSV dataset. But the traditional way to compute a histogram does not apply to unbounded/stream data.


5 Development Rules to Improve Your Data Science Projects

1. Abstract scripts into functions and classes

Say you are working on a Jupyter notebook figuring out how to best visualize some data. As soon as that code works and you don’t think it will need much more debugging, it’s time to abstract it! Let’s look at an example,

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd

synthetic_data = np.random.normal(0,1,1000)

plt.plot(synthetic_data, color="green")
plt.title("Plotting Synthetic Data")
plt.xlabel("x axis")
plt.ylabel("y axis")

Here, we plotted some synthetic data. Assuming we are happy with our plot, what we want to do now is abstract this into a function and add it to the code base of our project:

def plotSyntheticDataTimeSeries(data):

What Data Science Tradecraft Looks Like and Why We Need It

Transparent Computational Analysis

The following methods should be used for both computer algorithms and data visualizations:

• Reproducible Computation

• Description of Code Availability

• Archiving of Data

• Explanation of Rationale

• Alternative Computations

Reproducible Computation:

Published products using computer computations must be replicable by other users. Code transparency is key to reproducibility. Even the same code can fail to produce the same output if it is run on different hardware, makes false assumptions about a user’s file directories, or compiles information using different settings. Therefore, computation must include documentation of all relevant data — including file and storage directories, as well as code associated with the algorithms or visualizations — in a reference section to ensure that the products can be accurately reproduced.


Fourier Transforms: An Intuitive Visualisation

Time-series Data Processing

An intuitive visualization of discrete Fourier transforms applied to simple time-series data.

Image by Author

This article visualizes the decomposition of a time series signal into its harmonics using the Fourier transform. The formula is explained in a visual manner to help understand its meaning.

The Fourier Transform is an extremely powerful tool used extensively in a wide variety of fields. Its power can be attributed to its ability to decompose time series signals into sinusoidal waveforms. This can be useful for example when denoising a signal and attempting to find the harmonics of a waveform.

Say for example one wants to extract information from the vibration signal of a jet engine.


Bad Data Visualizations and How To Fix Them

Using data visualization principles to fix misleading and uninformative charts

Building data visualizations: the stage in the data science cycle where you get to present your findings after you have worked on understanding and cleaning a dataset. I am sure you have wondered what the best way to go about showing the data graphically can be, and how different choices you make, whether they are colors, titles, labels, or units can affect how the audience perceives your results. So, what makes a visualization good or bad?

Intuitively, a good visualization should convey information about its contents clearly and accurately.


Guide To Data Visualization With ggplot2 In A Hour


Visualizing Spotify Data with Python and Tableau

Create a dynamic dashboard using your streaming data & Spotify’s API


Analysis of the emotion data — a dataset for emotion recognition tasks.

We’ll start by importing the necessary libraries and visualizing the data. As we already know, the data has been preprocessed, so that is a bonus. We’ll typically look for imbalance in the dataset and length of the tweets to start with. Beyond that, feel free to dive in further.

Creating a column with label names.

The label column currently has integers. To make it more understandable, we’ll create a new column called description containing the description of each integer in the label column.

Image by Author

Analysis of the Description Column

Now, let’s analyze and see how the description column looks like.


How to create fast and accurate scatter plots with lots of data in python


SAP BW Data Mining Analytics: Model Reporting (Part 1)

SAP BW Data Mining allows creating data mining models that implement respective analysis methods (either supplied by SAP as built-in with SAP BW Data Mining or supplied by certified vendors). Although analysis methods available via SAP BW Data Mining provide extensive reporting and visualizations, there could be a need for additional model- and method-related analytics that would facilitate management and deployment of the content created with SAP BW Data Mining. In this paper we will present the following analytics:

  • Dashboard – SAP BW Data Mining Model Reporting

Business Requirements

The main use of SAP BW Data Mining is creation of models based on analysis methods and of analysis processes based on the models.


5 Must Try Awesome Python Data Visualization Libraries


By Roja Achary, Machine Learning Enthusiast

“The purpose of visualization is insight, not pictures.”

―Ben Shneiderman

Source – Venn gage
Data visualization is the visual presentation of data or information. The goal of data visualization is to communicate data or information clearly and effectively to readers. Typically, data is visualized in the form of a chart, infographic, diagram, map and more.

How does it help?

  • Identify trends and outliers
  • Tell a story within the data
  • Reinforce an argument or opinion
  • Highlight an important point in a set of data

Let’s dive into each of them.

Libraries required

Use the package manager pip to install below:

pip install matplotlib
pip install seaborn
pip install plotnine
pip install plotly
pip install bokeh


Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.


7 Examples to Master Line Plots With Python Seaborn

Practical data visualization guide.

Photo by Markus Winkler on Unsplash

Data visualization is an integral part of data science. It helps us explore a dataset and the relationships between variables to create proper data visualizations. They are also highly efficient tools to deliver results and findings.

There are several different kinds of data visualizations. One of the most commonly used ones is line plot which is used for analyzing the relationships between two continuous variables.

In this article, we will go over 7 examples to explain in detail how to create line plots with the Seaborn library of Python.

The main use case for line plots is time series analysis.


Marketing Intelligence & Analytics Platform with Data Visualization Features

Today Marketers play the role of advanced and technical matchmakers as their job is to match their target consumers with the products and solutions that best meet their needs or wants. They are also responsible for matching their consumer segments with the content, messaging, creatives, and CTAs that best suits – across all the platforms and channels their audiences are on.  Marketers generally face massive barriers to understand how customers engage with marketing campaigns and where & how to optimize them. Data visualization, preparation, charts, dashboards and stats are the top areas where talented and expensive marketing resources are getting exhausted and that too are misaligned.


Deep Neural Networks Addressing 8 Challenges in Computer Vision

But first, let’s address the question, “What is computer vision?” In simple terms, computer vision trains the computer to visualize the world just like we humans do. Computer vision techniques are developed to enable computers to “see” and draw analysis from digital images or streaming videos. The main goal of computer vision problems is to use the analysis from the digital source data to convert it into something about the world. 

Computer vision uses specialized methods and general recognition algorithms, making it the subfield of artificial intelligence and machine learning. Here, when we talk about drawing analysis from the digital image, computer vision focuses on analyzing descriptions from the image, which can be text, object, or even a three-dimensional model.


Connecting Widgets To Visualizations

Using IPyWidgets for Creating Widgets to Control Visualizations

Source: By Author

Why Data Scientists Should Stay Open-Minded, Curious, and Non-Judgemental

How did you decide to enter the field of data science?

I started learning how to code for fun at the start of the pandemic; my primary interest was in data visualization and web app development. In June 2020, when my job in the nonprofit sector was eliminated, I had a lot of free time and decided to study data science because I’ve always loved statistics and storytelling. I followed my curiosity and quickly became fascinated with machine learning, then my obsession with language drove me to explore NLP in depth.

Since I am committed to social justice, I was naturally attracted to working with low-resource languages like Arabic.


A Collection of Data Visualizations in ggplot2

Great Learning Materials for Beginners as well

For R users ggplot2 is the most popular visualization library with a huge number of graphics available. It is simple to use and is able to generate complex plots with simple commands fast. For an R user, there is no reason to not work with ggplot2 for data visualization

As I mentioned earlier, a lot of options and graphics are available. Nobody can remember all of those. So, it is helpful to have a cheat sheet or guide in hand. This article is an attempt to make a nice guide or a cheat sheet for some common types of plots from basic to advanced levels.