CoderzColumn : Data Science Tutorials (Page: 2)

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 pivot-tables, pandas-dataframe
Guide to Create Pivot Tables from Pandas DataFrame
Data Science

Guide to Create Pivot Tables from Pandas DataFrame

Tutorial provides detailed guide on how we can use pivot() and pivot_table() function available from pandas to create pivot tables. The pivot_table() function also let us perform many simple stats on aggregate data.

Sunny Solanki  Sunny Solanki
Tags geoviews, scatter-maps, bubble-maps
Geoviews - Scatter & Bubble Maps [Python]
Data Science

Geoviews - Scatter & Bubble Maps [Python]

Geoviews - Scatter & Bubble Maps using Bokeh and Matplotlib [Python]

Sunny Solanki  Sunny Solanki
Tags geoviews, choropleth-maps
Geoviews - Choropleth Maps using Bokeh and Matplotlib  [Python]
Data Science

Geoviews - Choropleth Maps using Bokeh and Matplotlib [Python]

Geoviews - Choropleth Maps using Bokeh and Matplotlib [Python]

Sunny Solanki  Sunny Solanki
Tags geoplot, scatter-maps, bubble-maps
Geoplot - Scatter & Bubble Maps [Python]
Data Science

Geoplot - Scatter & Bubble Maps [Python]

Geoplot - Scatter & Bubble Maps [Python]

Sunny Solanki  Sunny Solanki
Tags geoplot, choropleth-maps
Geoplot - Choropleth Maps [Python]
Data Science

Geoplot - Choropleth Maps [Python]

Geoplot - Choropleth Maps [Python]

Sunny Solanki  Sunny Solanki
Tags sweetviz, EDA
Sweetviz: Automate Exploratory Data Analysis (EDA)
Data Science

Sweetviz: Automate Exploratory Data Analysis (EDA)

Sweetviz: Automate Exploratory Data Analysis (EDA)

Sunny Solanki  Sunny Solanki
Tags plotnine, quick-plots
Plotnine: Quick Plots with One Function Call [Python]
Data Science

Plotnine: Quick Plots with One Function Call [Python]

Plotnine: Quick Plots with One Function Call

Sunny Solanki  Sunny Solanki
Tags dashboard, streamlit, matplotlib
Basic Dashboard using Streamlit and Matplotlib
Data Science

Basic Dashboard using Streamlit and Matplotlib

A simple guide to create a dashboard using Python libraries streamlit and Matplotlib. Streamlit is a framework that let us create dashboards/web-apps using data visualization libraries bokeh, Altair, matplotlib, plotly, vega, folium, etc. It let us add components like drop-downs, radio buttons, checkboxes, multi-select, sliders, tables, code, etc.

Sunny Solanki  Sunny Solanki
Tags maps, plotnine, choropleth
Maps using Plotnine (Choropleth, Scatter, and Bubble Maps)
Data Science

Maps using Plotnine (Choropleth, Scatter, and Bubble Maps)

Maps using Plotnine (Choropleth, Scatter, and Bubble Maps)

Sunny Solanki  Sunny Solanki
Tags plotnine, grammer-of-graphics, charts
Plotnine: Simple Guide to Create Charts using Grammar of Graphics [Python]
Data Science

Plotnine: Simple Guide to Create Charts using Grammar of Graphics [Python]

Plotnine: Simple Guide to Create Charts using Grammar of Graphics

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.