CoderzColumn : Data Science Tutorials (Page: 4)

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 matplotlib
How to show the connection line between the two charts in matplotlib [Python]?
Data Science

How to show the connection line between the two charts in matplotlib [Python]?

How to show the connection line between the two charts in matplotlib [Python]?

Sunny Solanki  Sunny Solanki
Tags dashboard, plotly-dash, widgets
How to Create Basic Dashboard in Python with Widgets [plotly & Dash]?
Data Science

How to Create Basic Dashboard in Python with Widgets [plotly & Dash]?

How to Create Basic Dashboard in Python with Widgets [plotly & Dash]?

Sunny Solanki  Sunny Solanki
Tags dashboard, bokeh-dash, widgets
How to Create Simple Dashboard with Widgets in Python [Bokeh]?
Data Science

How to Create Simple Dashboard with Widgets in Python [Bokeh]?

How to Create Simple Dashboard with Widgets in Python [Bokeh]?

Sunny Solanki  Sunny Solanki
Tags network-chart, holoviews
How to Plot Network Charts in Python [Holoviews]?
Data Science

How to Plot Network Charts in Python [Holoviews]?

How to Plot Network Charts in Python [Holoviews]?

Sunny Solanki  Sunny Solanki
Tags chord-chart
How to Plot Chord Diagram in Python [holoviews]?
Data Science

How to Plot Chord Diagram in Python [holoviews]?

How to Plot Chord Diagram in Python [holoviews]?

Sunny Solanki  Sunny Solanki
Tags parallel-coodinates-chart, matplotlib, plotly
How to Plot Parallel Coordinates Plot in Python [Matplotlib & Plotly]?
Data Science

How to Plot Parallel Coordinates Plot in Python [Matplotlib & Plotly]?

How to Plot Parallel Coordinates Plot in Python [Matplotlib & Plotly]?

Sunny Solanki  Sunny Solanki
Tags radar-chart, plotly
How to Plot Radar Charts in Python [plotly]?
Data Science

How to Plot Radar Charts in Python [plotly]?

How to Plot Radar Charts in Python [plotly]?

Sunny Solanki  Sunny Solanki
Tags data-visualizaton, bokeh
Bokeh - How to Layout Charts to Create Figure
Data Science

Bokeh - How to Layout Charts to Create Figure

Bokeh - How to Layout Charts to Create Figure

Sunny Solanki  Sunny Solanki
Tags network-analysis, networkx
Networkx - Network Analysis in Python : Important Structures and Bipartite Graphs
Data Science

Networkx - Network Analysis in Python : Important Structures and Bipartite Graphs

Network Analysis in Python - Important Structures and Bipartite Graphs [networkx]

Sunny Solanki  Sunny Solanki
Tags cartopy, maps
Cartopy - Basic Maps [Scatter Map, Bubble Map and Connection Map]
Data Science

Cartopy - Basic Maps [Scatter Map, Bubble Map and Connection Map]

Cartopy - Basic Maps [Scatter Map, Bubble Map and Connection Map]

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.