Understanding and Visualizing Data with Python

Course Overview

This course, Understanding and Visualizing Data with Python, provides learners with an introduction to the field of statistics. Learners will gain an understanding of where data come from, study design, data management, and exploring and visualizing data. They will learn to identify different types of data, and how to visualize, analyze, and interpret summaries for both univariate and multivariate data. Learners will also be introduced to the differences between probability and non-probability sampling from larger populations, the idea of how sample estimates vary, and how inferences can be made about larger populations based on probability sampling.

At the end of each week, learners will apply the statistical concepts they’ve learned using Python within the course environment. Tutorial videos are provided to walk learners through the creation of visualizations and data management, all within Python. This course utilizes the Jupyter Notebook environment within Coursera.

[Applications]
At the conclusion of this course, learners should be able to apply the concepts of data visualization and analysis to their own data sets. They should be able to use Python to create visualizations and analyze data, as well as understand the differences between probability and non-probability sampling. Learners should also be able to identify different types of data and use the Numpy, Pandas, Statsmodels, Matplotlib, and Seaborn libraries to create visualizations and analyze data.

[Career Paths]
[Job Position Path]Data Scientist
[Description]Data Scientists are responsible for collecting, analyzing, and interpreting large amounts of data to identify trends and patterns. They use a variety of methods, including statistical analysis, machine learning, and data mining, to uncover insights from data. Data Scientists must be able to communicate their findings to stakeholders in a clear and concise manner. They must also be able to develop data-driven solutions to business problems.

[Development Trend]Data Science is an ever-evolving field, and the demand for Data Scientists is growing rapidly. As more organizations move towards data-driven decision making, the need for Data Scientists with the skills to analyze and interpret data will continue to increase. Additionally, the development of new technologies such as artificial intelligence and machine learning will create new opportunities for Data Scientists to explore.

[Education Paths]




A recommended educational path for learners of this course is to pursue a degree in Data Science. Data Science is an interdisciplinary field that combines mathematics, statistics, computer science, and other related fields to analyze and interpret data. This degree program will provide learners with the skills and knowledge necessary to work with data in a variety of contexts, from business to research.




The development trend of Data Science degrees is to focus on the application of data science principles and techniques to solve real-world problems. This includes the use of machine learning, artificial intelligence, and other advanced technologies to analyze and interpret data. Additionally, many programs are now incorporating courses in ethical considerations, such as data privacy and security, to ensure that data is used responsibly.