CoderzColumn : Data Science Tutorials (Page: 3)

Data Science Tutorials


Data science is an interdisciplinary field that applies information from data across a wide range of application fields by using scientific methods, procedures, algorithms, and systems to infer knowledge and insights from noisy, structured, and unstructured data.

Data visualization libraries like matplotlib, Bokeh, bqplot, Plotnine, cufflinks, Altair, hvplot, Holoviews, seaborn and more.

Different Interactive charts Sunburst Charts, Sankey Diagrams (Alluvial), Candlestick Charts, Network Charts, Chord Diagram, Parallel Coordinates Plots, Radar Charts, Connection Map, Treemap, Choropleth Maps, Scatter & Bubble Maps.

Apart from this, you will find tutorials about time series data and its applications, creating dashboards, and other concepts.

For an in-depth understanding of the above concepts, check out the sections below.

Recent Data Science Tutorials


Tags cufflinks, maps, plotly, pandas
Choropleth Maps & Scatter Maps using cufflinks [Python]
Data Science

Choropleth Maps & Scatter Maps using cufflinks [Python]

Choropleth Maps & Scatter Maps using cufflinks [Python]

Sunny Solanki  Sunny Solanki
Tags animation, bqplot
Simple 2D Animation in Python using bqplot & ipywidgets
Data Science

Simple 2D Animation in Python using bqplot & ipywidgets

Simple 2D Animation in Python using bqplot & ipywidgets

Sunny Solanki  Sunny Solanki
Tags bqplot, tooltip
How to put the chart into the tooltip of another chart in bqplot [Python]?
Data Science

How to put the chart into the tooltip of another chart in bqplot [Python]?

How to put the chart into the tooltip of another chart in bqplot [Python]?

Sunny Solanki  Sunny Solanki
Tags bokeh, maps
Plotting Maps using Bokeh [Python]
Data Science

Plotting Maps using Bokeh [Python]

The tutorial provides an in-depth guide to plot an interactive maps using Bokeh [Python].

Sunny Solanki  Sunny Solanki
Tags ipyleaflet, maps
ipyleaflet [Python] - Interactive Maps in Python based on leafletjs
Data Science

ipyleaflet [Python] - Interactive Maps in Python based on leafletjs

ipyleaflet [Python] - Interactive Maps in Python based on leafletjs

Sunny Solanki  Sunny Solanki
Tags bqplot, choropleth-maps
Interactive Choropleth Maps using bqplot [Python]
Data Science

Interactive Choropleth Maps using bqplot [Python]

Interactive Choropleth Maps using bqplot [Python]

Sunny Solanki  Sunny Solanki
Tags choropleth-maps, ipyleaflet
Choropleth Maps using ipyleaflet [Python]
Data Science

Choropleth Maps using ipyleaflet [Python]

Choropleth Maps using ipyleaflet

Sunny Solanki  Sunny Solanki
Tags bqplot, data-visualization
bqplot [Python] - Interactive Plots using Internal Object Model API
Data Science

bqplot [Python] - Interactive Plots using Internal Object Model API

bqplot [Python] - Interactive Plots using Internal Object Model API

Sunny Solanki  Sunny Solanki
Tags matplotlib, ipywidgets
How to link ipywidgets widget with matplotlib chart to dynamically update charts in Python?
Data Science

How to link ipywidgets widget with matplotlib chart to dynamically update charts in Python?

How to link ipywidgets widget with matplotlib chart to dynamically update charts in Python?

Sunny Solanki  Sunny Solanki
Tags bqplot, ipywidgets
How to link ipywidgets widget with bqplot chart to dynamically update charts?
Data Science

How to link ipywidgets widget with bqplot chart to dynamically update charts?

How to link ipywidgets widget with bqplot chart to dynamically update charts?

Sunny Solanki  Sunny Solanki
Python Data Visualization Libraries

Python Data Visualization Libraries


Data Visualization is a field of graphical representation of information / data. It is one of the most efficient ways of communicating information with users as humans are quite good at capturing patterns in data.

Python has a bunch of libraries that can help us create data visualizations. Some of these libraries (matplotlib, seaborn, plotnine, etc) generate static charts whereas others (bokeh, plotly, bqplot, altair, holoviews, cufflinks, hvplot, etc) generate interactive charts. Majority of basic visualizations like bar charts, line charts, scatter plots, histograms, box plots, pie charts, etc are supported by all libraries. Many libraries also support advanced visualization, widgets, and dashboards.

Advanced Data Visualizations using Python

Advanced Data Visualizations using Python


Basic Data Visualizations like bar charts, line charts, scatter plots, histograms, box plots, pie charts, etc are quite good at representing information and exploring relationships between data variables.

But sometimes these visualizations are not enough and we need to analyze data from different perspectives. For this purpose, many advanced visualizations are developed over time like Sankey diagrams, candlestick charts, network charts, chord diagrams, sunburst charts, radar charts, parallel coordinates charts, etc. Python has many data visualization libraries that let us create such advanced data visualizations.

Dashboards using Python

Dashboards using Python


Dashboards are literally everywhere and everyone is using them. Dashboards are GUI with various visualizations and metrics that can be used to monitor key performance indicators. Dashboards have a very wide range of applications in all fields.

Python has a bunch of libraries (dash, panel, streamlit, bokeh, etc) that let us create dashboards using them. They let us include widgets and interactive data visualizations in dashboards.

Work with Time Series Data in Python

Work with Time Series Data in Python


Time series is a type of data where data points are recorded in time order or at specified time intervals. Many real-world datasets like stock prices, weather indicators, heights of ocean tides, retail sales, etc.

Time series analysis involves various tasks like resampling time series, trying moving window functions, forecasting, classification, etc.

Python has various libraries (pandas, statsmodels, etc.) that let us load and work with time series data efficiently. They even provide useful functionalities to work with time series data.

Visualize Maps using Python

Visualize Maps using Python


Maps are one of the best ways to display and analyze geospatial data. It helps us better see patterns and trends geographically. This can help us with better decision-making.

Many different types of maps have been developed over time to analyze data from different perspectives. Some common map visualization types are choropleth maps, scatter maps, bubble maps, connection maps, etc. Apart from these, we can also include pins on maps to identify locations.

Python has many different libraries (geopandas, folium, ipyleaflet, cartopy, geoviews, geoplot, bokeh, altair, plotly, hvplot, etc) that let us create static as well as interactive maps.

Exploratory Data Analysis using Python

Exploratory Data Analysis using Python


Exploratory data analysis (commonly referred to as EDA) is an initial analysis of data to look for various relationships, anomalies, missing values, distributions, basic statistics, etc. It helps us understand data better to make further decisions. Various stats are calculated and statistical visualizations are created during EDA to understand data.

Python provides many different tools / libraries (Sweetviz, missingno, seaborn, pandas, etc) for performing EDA. It's quite common to use more than one of these tools to perform EDA.