Data visualization using python packages Course Overview

Data visualization using python packages Course Overview

The "Data Visualization using Python Packages" course is designed to equip learners with the skills to create compelling, informative visuals from data using popular Python libraries. Data visualization is essential for interpreting complex data and communicating findings effectively.

Module 1: NumPy package lays the foundation with array manipulation, enabling learners to handle multi-dimensional data structures. Module 2: Pandas introduces data manipulation and cleaning, which are crucial for preparing datasets for visualization.

Module 3: Matplotlib dives into creating basic to advanced plots, from line plots to histograms, and teaches how to customize and save visualizations. Module 4: Seaborn enhances the course by introducing statistical plotting capabilities for more sophisticated visuals.

Finally, Module 5: Plotly and Cufflinks offers an interactive charting experience, allowing for dynamic, web-based visualizations. Throughout the course, learners will gain proficiency in data science, visualization techniques, and the ability to present data insights effectively. This course is ideal for those looking to enhance their data analysis and data visualization skills.

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  • Live Online Training (Duration : 24 Hours)
  • Per Participant

♱ Excluding VAT/GST

Classroom Training price is on request

You can request classroom training in any city on any date by Requesting More Information

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Course Prerequisites

To ensure that students can successfully undertake training in the Data Visualization using Python packages course, the following minimum prerequisites are recommended:

  • Basic understanding of programming concepts, preferably in Python.
  • Familiarity with Python syntax and the ability to write and understand simple Python scripts.
  • Knowledge of basic data structures in Python, such as lists and dictionaries.
  • An understanding of fundamental mathematical concepts, including algebra and basic statistics.
  • Experience with using a text editor or an integrated development environment (IDE) to write code.
  • Comfort with installing software and managing packages in Python, using tools such as pip.

While prior experience with data analysis or visualization is not strictly necessary, it can enhance the learning experience. This course is designed to be accessible to beginners with a general background in Python programming.

Target Audience for Data visualization using python packages

Learn to visualize data with Python's top libraries—NumPy, Pandas, Matplotlib, Seaborn, Plotly, and Cufflinks—for insightful analytics.

  • Data Analysts
  • Data Scientists
  • Business Intelligence Professionals
  • Marketing Analysts
  • Financial Analysts
  • Research Scientists
  • Statisticians
  • Machine Learning Engineers
  • Python Developers interested in data visualization
  • Academic Researchers and Students
  • Data Journalism enthusiasts
  • BI and Data Visualization Engineers
  • Product Managers who want to understand data presentations
  • UX/UI Designers looking to present data more effectively

Learning Objectives - What you will Learn in this Data visualization using python packages?

Introduction to the Course's Learning Outcomes

This course is designed to empower students with the skills needed to create compelling data visualizations using Python. It covers key Python packages such as NumPy, Pandas, Matplotlib, Seaborn, and Plotly with Cufflinks.

Learning Objectives and Outcomes

  • Understand and perform array manipulations with NumPy, including creation, reshaping, and executing array operations.
  • Develop proficiency in mathematical functions within the NumPy package for scientific computing.
  • Master basic operations in Pandas for data manipulation and preliminary analysis, such as data cleaning and transformation.
  • Create various types of plots using Matplotlib, including line plots, histograms, bar charts, pie charts, and image displays with imshow.
  • Learn to organize visual information effectively using subplots and figure saving techniques in Matplotlib.
  • Gain proficiency in Seaborn for generating more aesthetically pleasing and complex visualizations like line plots, distribution plots, and scatter plots.
  • Understand advanced Seaborn plots, including KDE, jointplot(), pairplot(), boxplot, violin, and point plots, for detailed data distribution analysis.
  • Explore Plotly's interactive chart gallery and learn to create dynamic, web-friendly visualizations.
  • Customize visualizations with Cufflinks, leveraging color schemes and offline capabilities to enhance presentation and accessibility.
  • Acquire practical skills in financial analysis using Plotly's Quantfig for dynamic stock market visualizations.