Learn Data Analysis using Pandas and Python (Module 2&3)
This free course provides an introduction to data analysis using Python and the powerful Pandas library. Participants will learn to analyze and manipulate data with ease. Module 2/3 focuses on this. ▼
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Course Feature
Cost:
Free
Provider:
Udemy
Certificate:
No Information
Language:
English
Start Date:
Self Paced
Course Overview
❗The content presented here is sourced directly from Udemy platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.
Updated in [April 29th, 2023]
Data Analysis using Pandas and Python (Module 2&3) is an online learning course that provides learners with the skills and knowledge to analyse data quickly and effectively. This course covers the fundamentals of data analysis and manipulation with Pandas, a powerful open-source library for data analysis and manipulation. Learners will gain an understanding of the basics of data analysis, including data types, data structures, and data manipulation. They will also learn how to use Pandas to perform data analysis tasks such as filtering, sorting, and summarizing data. Additionally, learners will gain an understanding of the different types of data visualizations and how to create them. Finally, learners will learn how to use Python to create data-driven applications. This course is ideal for learners who are interested in data analysis and manipulation, and who want to gain the skills and knowledge to become proficient in this field.
[Applications]
After completing this course, students will be able to apply their knowledge of data analysis and manipulation with Pandas to their own projects. They will be able to use the techniques learned to analyse data quickly and effectively. Additionally, they will be able to use the Pandas library to create data visualizations and to perform statistical analysis. Finally, they will be able to use the Python programming language to create scripts to automate data analysis tasks.
[Career Paths]
1. Data Analyst: Data Analysts are responsible for collecting, organizing, and analyzing data to help inform business decisions. They use a variety of tools and techniques, such as statistical analysis, data mining, and machine learning, to uncover patterns and trends in data. Data Analysts are in high demand as businesses increasingly rely on data-driven decision making.
2. Data Scientist: Data Scientists are responsible for extracting insights from large datasets. They use a variety of techniques, such as machine learning, natural language processing, and predictive analytics, to uncover patterns and trends in data. Data Scientists are in high demand as businesses increasingly rely on data-driven decision making.
3. Business Intelligence Analyst: Business Intelligence Analysts are responsible for analyzing data to help inform business decisions. They use a variety of tools and techniques, such as data mining, statistical analysis, and predictive analytics, to uncover patterns and trends in data. Business Intelligence Analysts are in high demand as businesses increasingly rely on data-driven decision making.
4. Data Engineer: Data Engineers are responsible for designing, building, and maintaining data systems. They use a variety of tools and techniques, such as database design, data warehousing, and data integration, to create efficient and reliable data systems. Data Engineers are in high demand as businesses increasingly rely on data-driven decision making.
[Education Paths]
1. Bachelor of Science in Data Science: This degree path focuses on the development of skills in data analysis, data mining, machine learning, and artificial intelligence. It also covers topics such as data visualization, data engineering, and data management. This degree path is becoming increasingly popular as businesses and organizations are relying more and more on data-driven decisions.
2. Master of Science in Business Analytics: This degree path focuses on the application of data analysis and analytics to business decisions. It covers topics such as predictive analytics, data mining, and machine learning. This degree path is becoming increasingly popular as businesses and organizations are relying more and more on data-driven decisions.
3. Master of Science in Data Science: This degree path focuses on the development of skills in data analysis, data mining, machine learning, and artificial intelligence. It also covers topics such as data visualization, data engineering, and data management. This degree path is becoming increasingly popular as businesses and organizations are relying more and more on data-driven decisions.
4. Doctor of Philosophy in Data Science: This degree path focuses on the development of advanced skills in data analysis, data mining, machine learning, and artificial intelligence. It also covers topics such as data visualization, data engineering, and data management. This degree path is becoming increasingly popular as businesses and organizations are relying more and more on data-driven decisions.
Pros & Cons
Clear explanation with examples
Understand what can be done with Pandas
Excellent course
No certification provided
Nil feedback given
No updates provided
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1. What is Pandas?
2. What is the main purpose of Module 2?
3. What is the main purpose of Module 3?
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