CoderzColumn : Data Science Tutorials (Page: 6)

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 holoviews, basic-plots
Getting Started with Holoviews - Basic Interactive Plotting [Python]
Data Science

Getting Started with Holoviews - Basic Interactive Plotting [Python]

Getting Started with Holoviews - Basic Interactive Plotting [Python]

Sunny Solanki  Sunny Solanki
Tags panel, matplotlib
How to create interactive dashboard using Python(Matplotlib and Panel)?
Data Science

How to create interactive dashboard using Python(Matplotlib and Panel)?

How to create interactive dashboard using Python(Matplotlib and Panel)?

Sunny Solanki  Sunny Solanki
Tags maps, choropleth-maps, geopandas
Plotting Static Maps using geopandas (Working with Geospatial data)
Data Science

Plotting Static Maps using geopandas (Working with Geospatial data)

Plotting Static Maps with geopandas (Working with Geospatial data)

Sunny Solanki  Sunny Solanki
Tags dashboard, plotly, dash, pythonanywhere
How to build dashboard using Python (Dash & Plotly) and deploy online (pythonanywhere.com)?
Data Science

How to build dashboard using Python (Dash & Plotly) and deploy online (pythonanywhere.com)?

How to build dashboard using Python (Dash & Plotly) and deploy online (pythonanywhere.com)?

Sunny Solanki  Sunny Solanki
Tags datascience, datavisulisation, seaborn
Seaborn Tutorial - Color Pallete
Data Science

Seaborn Tutorial - Color Pallete

Seaborn Tutorial - Color Pallete

Dolly Solanki  Dolly Solanki
Tags datascience, datavisulisation, seaborn
Seaborn Tutorial
Data Science

Seaborn Tutorial

Seaborn Tutorial

Dolly Solanki  Dolly Solanki
Tags datascience, datavisulisation, seaborn
Seaborn - Pair Grid
Data Science

Seaborn - Pair Grid

Seaborn - Pair Grid

Dolly Solanki  Dolly Solanki
Tags datascience, datavisulisation, seaborn
Seaborn - Linear Relationships
Data Science

Seaborn - Linear Relationships

Seaborn - Linear Relationships

Sunny Solanki  Sunny Solanki
Tags datascience, datavisulisation, seaborn
Seaborn - How To Work With Distribution of Observations
Data Science

Seaborn - How To Work With Distribution of Observations

Seaborn - How To Work With Distribution of Observations

Sunny Solanki  Sunny Solanki
Tags datascience, datavisulisation, seaborn
Seaborn - How To Check Kernel Density Estimates
Data Science

Seaborn - How To Check Kernel Density Estimates

Seaborn - How To Check Kernel Density Estimates

Dolly Solanki  Dolly 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.