Rating 3.25 out of 5 (2 ratings in Udemy)
What you'll learn- Learn key analytical skills (data cleaning, analysis, & visualization)
- Understand how to clean and organize data for analysis, and complete analysis and calculations using Python programming
- Learn how to visualize and present data findings in visualization platforms (Pandas, Seaborn, Plotly express)
- Understand and practice the use of Pandas DataFrames
- Visualize data with Seaborn and Plotly
- Use matplotlib to plot basic graphs …
Rating 3.25 out of 5 (2 ratings in Udemy)
What you'll learn- Learn key analytical skills (data cleaning, analysis, & visualization)
- Understand how to clean and organize data for analysis, and complete analysis and calculations using Python programming
- Learn how to visualize and present data findings in visualization platforms (Pandas, Seaborn, Plotly express)
- Understand and practice the use of Pandas DataFrames
- Visualize data with Seaborn and Plotly
- Use matplotlib to plot basic graphs and charts
- Create common visualization charts using open source tools
- Learn how to create geographic plots using plotly express and geopandas
- Learn how to use pandas to sort, filter, import and clean data
- students will learn how to import xcell, CSV and custom data to Jupyter notebooks
- Understand key data analysis terms and definitions
DescriptionWelcome to the Data wrangling and visualization course with Python. This course is intended for beginners who are interested in the wonderful world of data wrangling and visualization. This course assumes you don't have experience with python and it attempts to demystify and make it as clear as possible using basic and concise examples. The course begins with an introduction to the python programming. Next, we move on and learn about common visualization tools and some popular python Data Visualization plugins (pandas, seaborn, plotly express) libraries with some practical examples. In this course we chosen to use python because is a powerful language that, is free, beginner friendly. We will use open-source data manipulation libraries such as pandas, geopandas, plotly, plotly express, sci-py, matplotlib. We would also learn about popular data manipulation libraries such as Pandas, NumPy, and matplotlib. These libraries will be used to understand the basics of data wrangling with numerous practical examples. Furthermore, we will investigate using python for Geographic Plots , using JSON files and the popular geopandas library for creating map plots. To wrap things up, we look at some practical projects and solutions to enforce, solidify and strengthen the concepts we have learnt throughout the course.
