Coursera – Data Wrangling with Python Specialization

Coursera – Data Wrangling with Python Specialization
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Launch your career in Data Science. By mastering the skills and techniques covered in these courses, students will be better equipped to handle the challenges of real-world data analysis.

What you’ll Learn:
• Define techniques and methods for collecting data from various sources including files, web,
databases, etc.
• Identify statistical analysis and visualization techniques that can be used to gain insights into
the data.
• Calculate and apply techniques for data preprocessing such as dealing with missing values,
outliers, sampling, normalization, and discretization.

Skills you’ll Gain:
• Statistical Analysis
• Python Libraries
• Python Programming
• Pandas
• Seaborn
• Matplotlib
• Data Wrangling
• Numpy
• Data Visualization (DataViz)

Specialization – 5 course series:
This specialization covers various essential topics such as fundamental tools, data collection, data understanding, and data preprocessing. This specialization is designed for beginners, with a focus on practical exercises and case studies to reinforce learning. By mastering the skills and techniques covered in these courses, students will be better equipped to handle the challenges of real-world data analysis. The final project will give students an opportunity to apply what they have learned and demonstrate their mastery of the subject.

• Fundamental Tools of Data Wrangling
• Data Collection and Integration
• Data Understanding and Visualization
• Data Processing and Manipulation
• Data Wrangling with Python Project

Applied Learning Project:
The final project provides students with an opportunity to apply the knowledge gained throughout the specialization in a real-life data wrangling project of their interest. Participants will follow the data wrangling pipeline step by step, from identifying data sources to processing and integrating data, to achieve a fine dataset ready for analysis. This course enables students to gain hands-on experience in the data wrangling process and prepares them to handle complex data challenges in real-world scenarios.

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