Master Data Analysis with Python - Intro to Pandas 2022

Course Overview

This course provides an introduction to the Pandas library for data analysis. Led by Ted Petrou, author of Master Data Analysis with Python and a pandas expert, participants will gain an understanding of the various data types available in a DataFrame, how to access DataFrame components such as the index, columns, and values, and how to create a meaningful index in a DataFrame. Additionally, participants will learn best practices for data analysis and complete a five-step data exploration process. By the end of the course, participants will have a better understanding of the Pandas library and how to use it to analyze data.

[Applications]
After completing this course, participants can apply their knowledge of the pandas DataFrame and Series to analyze data in a variety of ways. They can use the five-step data exploration process to gain insights into their data and create meaningful indexes for their DataFrames. Additionally, they can use the various data types available in a DataFrame to create visualizations and gain further insights into their data. Finally, they can use their knowledge of the pandas library to create powerful data analysis applications.

[Career Paths]
1. Data Scientist: Data Scientists use their knowledge of mathematics, statistics, and programming to analyze large datasets and uncover insights. They use their findings to develop strategies and solutions for businesses. Data Scientists are in high demand and the field is expected to grow rapidly in the coming years.

2. Business Intelligence Analyst: Business Intelligence Analysts use data to help businesses make better decisions. They analyze data from various sources, such as customer surveys, financial reports, and market research, to identify trends and patterns. They then use their findings to develop strategies and solutions for businesses.

3. Data Engineer: Data Engineers are responsible for designing, building, and maintaining data systems. They use their knowledge of programming, databases, and data analysis to create efficient data pipelines and systems. Data Engineers are in high demand and the field is expected to grow rapidly in the coming years.

4. Machine Learning Engineer: Machine Learning Engineers use their knowledge of mathematics, statistics, and programming to develop algorithms and models that can learn from data. They use their findings to develop strategies and solutions for businesses. Machine Learning Engineers are in high demand and the field is expected to grow rapidly in the coming years.

[Education Paths]
1. Master of Science in Data Science: This degree path focuses on the application of data science principles and techniques to solve real-world problems. It covers topics such as data mining, machine learning, artificial intelligence, and predictive analytics. It also provides students with the skills to develop and deploy data-driven solutions. This degree is becoming increasingly popular as businesses and organizations recognize the value of data-driven decision-making.

2. Master of Science in Business Analytics: This degree path focuses on the application of analytics to business problems. It covers topics such as data mining, predictive analytics, and optimization. It also provides students with the skills to develop and deploy data-driven solutions. This degree is becoming increasingly popular as businesses and organizations recognize the value of data-driven decision-making.

3. Master of Science in Computer Science: This degree path focuses on the application of computer science principles and techniques to solve real-world problems. It covers topics such as algorithms, data structures, software engineering, and artificial intelligence. It also provides students with the skills to develop and deploy data-driven solutions. This degree is becoming increasingly popular as businesses and organizations recognize the value of data-driven decision-making.

4. Master of Science in Artificial Intelligence: This degree path focuses on the application of artificial intelligence principles and techniques to solve real-world problems. It covers topics such as machine learning, natural language processing, and computer vision. It also provides students with the skills to develop and deploy data-driven solutions. This degree is becoming increasingly popular as businesses and organizations recognize the value of data-driven decision-making.