Tag: Data visualization

Course content

• Introduction
• Module 02 – Data Visualization In Streamlit
• Module 02- Streamlit Components & Themes
• Module 03 – Project Section – Building Streamlit Apps – NLP Apps
• Module 03 – Project Section – Building Streamlit Apps – Text Analysis Apps
• Project – Building Streamlit Apps -Data Apps
• Module 03 – Project Section – Building Streamlit Apps – Machine Learning Apps
• Project – Building Streamlit Apps – CRUD Apps (Create Read Update Delete)
• Project – Building a CRUD(Create Read Update Delete) App In Streamlit
• Project – Build Streamlit Apps – End to End (StreamBible)
• Module 04 – Deploying Streamlit Apps
• Module 05 – Streamlit Session States
• Streamlit Projects – Misc Apps

Course content

• Course Introduction
• Introduction to Data Basics
• Plotly Basics
• Dash Basics – Layout
• DashBoard Exercise
• DashBoard Components
• Interactive Components
• Callbacks with State
• Interacting with Visualizations
• Code Along Milestone Project
• Live Updating
• Deployment
• BONUS SECTION: THANK YOU!

Course content

• Introduction
• Python Programming Tutorials
• Numpy Library for Data Science, Machine Learning and Artificial Intelligence
• Pandas Library for Data Science and Data Analytics
• Matplotlib Library for Data Science and Data Visualization
• Seaborn Library for Data Science and Data Visualization
• Plotly Library for Data Science and Data Visualization
• Tkinter Tutorial for GUI
• Speech Recognition Fundaments and Projects
• Different Sources for Dataset
• Statistical Data Analysis
• What is Machine Learning and it’s types
• Data Science with Machine Learning Algorithms Case Study
• Scikit-learn Library for Machine Learning and Data Science
• A1 Case Study on Unmanned Ground Vehicle
• Additional and New Learning

Course content

• Deep Learning – Deep Understanding by Visual Exploration

Introduction

• SciPy vs NumPy
• Overview of SciPy features and components

Getting Started

• Installing SciPy
• Understanding basic functions

Implementing Scientific Computing

• Using SciPy constants
• Calculating integrals
• Solving linear equations
• Creating matrices with sparse and graphs
• Optimizing or minimizing functions
• Performing significance tests
• Working with different file formats (Matlab, IDL, Matrix Market, etc.)

Visualizing and Manipulating Data

• Implementing K-means clustering
• Using spatial data structures
• Processing multidimensional images
• Calculating Fourier transformations
• Using interpolation for fixed data points

Troubleshooting

Summary and Next Steps

Introduction

Data Science in Depth

• What is Plotly? What is Dash?
• Pandas overview
• Numpy overview

Plotly Basics

• Plots
• Heatmaps

Preparing the Development Environment

• Installing and configuring Plotly
• Installing and configuring Dash

Dash Core Components

• Using drowdown and slider components
• Uploading CSV, XLS, and images
• Working with Dash layouts
• Converting Plotly plots to dashboards
• Using callbacks
• Working with inputs and outputs

Dash Dashboards

• Pulling API data
• Building a binance dashboard
• Connecting Dash components
• Using alpha vantage
• Cleaning data
• Controlling callbacks
• Updating graphs
• Working with layout updating

Deployment

• Working with app authorization
• Deploying with Heroku

Summary and Conclusion

Introduction

Google Analytics Overview

• What is Google Analytics?
• Google Analytics features

Preparing the Environment

• Setting up Google Analytics

Administration

• Working with settings

Reporting

• Importing and exporting data
• Creating custom reports
• Creating and sharing dashboards
• Adding shortcuts

Data Visualizations

• Adding and comparing metrics
• Sorting data
• Creating charts and graphs
• Using the search features

Summary and Conclusion

Introduction

Drone Mapping

• Workflows

Post-Processing

• What is post processing?
• GIS, CAD, and more

Preparing the Development Environment

• Installing and configuring QGIS
• Installing and configuring AutoCAD

QGIS and AutoCAD

• Post-processing data and images
• Clipping data and images
• Using plug-ins

Google Earth Pro

• Visualizing drone data
• Post-processing data and images

GIMP

• Post-processing data and images

Reports

• Generating reports

Summary and Conclusion

Overview

deck.gl is an open-source, WebGL-powered library for exploring and visualizing data assets at scale. Created by Uber, it is especially useful for gaining insights from geospatial data sources, such as data on maps.

This instructor-led, live training introduces the concepts and functionality behind deck.gl and walks participants through the set up of a demonstration project.

By the end of this training, participants will be able to:

• Take data from very large collections and turn it into compelling visual representations
• Visualize data collected from transportation and journey-related use cases, such as pick-up and drop-off experiences, network traffic, etc.
• Apply layering techniques to geospatial data to depict changes in data over time
• Integrate deck.gl with React (for Reactive programming) and Mapbox GL (for visualizations on Mapbox based maps).
• Understand and explore other use cases for deck.gl, including visualizing points collected from a 3D indoor scan, visualizing machine learning models in order to optimize their algorithms, etc.

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Course Outline

To request a customized course outline for this training, please contact us to arrange.

Introduction

• Master-node architecture

Installation

• Requirements
• Master and node setup

Monitoring with Munin

• Visualizing resources and trends
• Graphing system
• Data logging
• Integration with Nagios
• Monitoring Windows nodes

Working with Plugins

Working with the API

Troubleshooting Munin

Closing Remarks