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

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]

Mar 29, 2020 ~45 mins

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)?

Mar 28, 2020 ~30 mins

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)

Mar 11, 2020 ~35 mins

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)?

Mar 04, 2020 ~30 mins

datascience, datavisulisation, seaborn

Seaborn Tutorial - Color Pallete

Data Science

Seaborn Tutorial - Color Pallete

Seaborn Tutorial - Color Pallete

Jan 28, 2020 ~13 mins

datascience, datavisulisation, seaborn

Seaborn Tutorial

Data Science

Seaborn Tutorial

Seaborn Tutorial

Jan 14, 2020 ~12 mins

datascience, datavisulisation, seaborn

Seaborn - Pair Grid

Data Science

Seaborn - Pair Grid

Seaborn - Pair Grid

Jan 11, 2020 ~15 mins

datascience, datavisulisation, seaborn

Seaborn - Linear Relationships

Data Science

Seaborn - Linear Relationships

Seaborn - Linear Relationships

Jan 08, 2020 ~12 mins

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

Dec 27, 2019 ~8 mins

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

Dec 25, 2019 ~14 mins

Most Popular Data Science Tutorials

1. Candlestick Chart in Python (mplfinance, plotly, bokeh, bqplot & cufflinks)
2. How to Plot Sankey Diagram in Python Jupyter Notebook [holoviews & plotly]?
3. Time Series - How to Remove Trend & Seasonality from Time-Series Data
4. Basic Dashboard using Streamlit and Matplotlib
5. cufflinks - How to create plotly charts from pandas dataframe with one line of code?

Tutorial Categories

Artificial Intelligence (81)
Data Science (73)
Digital Marketing (8)
Machine Learning (38)
Python (130)

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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.

matplotlib (Static Charts)

matplotlib (Static Charts)

bokeh (Interactive Charts)

bokeh (Interactive Charts)

bqplot (Interactive Charts)

bqplot (Interactive Charts)

plotnine (Static Charts)

plotnine (Static Charts)

altair (Interactive Charts)

altair (Interactive Charts)

seaborn (Static Charts)

seaborn (Static Charts)

holoviews (Static & Interactive Charts)

holoviews (Static & Interactive Charts)

cufflinks (Interactive Charts)

cufflinks (Interactive Charts)

hvplot (Interactive Charts)

hvplot (Interactive Charts)

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.

Candlestick Charts

Candlestick Charts

Sankey Diagrams

Sankey Diagrams

Sunburst Charts

Sunburst Charts

Chord Diagrams

Network Charts

Parallel Coordinates Charts

Parallel Coordinates Charts

Radar Charts

Treemap Charts

Connection Maps

Connection Maps

Choropleth Maps

Choropleth Maps

Scatter & Bubble Maps

Scatter & Bubble Maps

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.

Dashboard using Dash & Plotly

Dashboard using Dash & Plotly

Dashboard using Panel & Matplotlib

Dashboard using Panel & Matplotlib

Dashboard using Bokeh

Dashboard using Bokeh

Dashboard using Streamlit & Cufflinks

Dashboard using Streamlit & Cufflinks

Dashboard with Widgets using Dash & Plotly

Dashboard with Widgets using Dash & Plotly

Dashboard using Streamlit & Matplotlib

Dashboard using Streamlit & Matplotlib

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.

Dates, Timestamps, Timedeltas, Periods & Time Zones using Pandas

Dates, Timestamps, Timedeltas, Periods & Time Zones using Pandas

Resampling & Moving Window Functions using Pandas

Resampling & Moving Window Functions using Pandas

Remove Trend & Seasonality to Make Time-Series Stationary

Remove Trend & Seasonality to Make Time-Series Stationary

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.

Geopandas

Folium

ipyleaflet

Cartopy

Geoviews

Geoplot

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.

Sweetviz: Automate EDA

Sweetviz: Automate EDA

missingno: Analyze Missing Data

missingno: Analyze Missing Data

Seaborn: Statistical Visualization to Analyze Relationships

Seaborn: Statistical Visualization to Analyze Relationships

Pivot Tables using Pandas

Pivot Tables using Pandas

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