Overview
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More and more organizations are turning to data science to help guide business decisions. Regardless of industry, the ability to extract knowledge from data is crucial for a modern business to stay competitive. One of the tools at the forefront of data science is the Python® programming language. Python's robust libraries have given data scientists the ability to load, analyze, shape, clean, and visualize data in easy to use, yet powerful, ways. This Specialization will teach you the skills you need to successfully use these key libraries to extract useful insights from data, and as a result, provide great value to the business.
The target student for this Specialization understands the principles and benefits of data science and has used basic data-driven tools like Microsoft® Excel® and Structured Query Language (SQL) queries, but wants to take the next steps into more advanced applications of data science.
So, the target student may be a programmer or data analyst looking to solve business problems using powerful programming libraries that go beyond the limitations of prepackaged GUI tools or database queries.
Note: This Specialization requires that you use the provided virtual machine, which includes an installation of Python and data science tools. The course setup instructions provided in the first course go into more detail about the hardware and software requirements.
Syllabus
- Course 1: Python Data Science: Environment Setup
- Course 2: Python Data Science: NumPy
- Course 3: Python Data Science: pandas, Matplotlib, and Seaborn
Courses
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This course will be useful to any programmer or data analyst who wants to expand their ability to extract knowledge from business data. You will learn how to set up a Python data science environment, particularly using Anaconda and Jupyter. This is the first course in a multi-course Specialization. All of the courses in this Specialization require that you use the provided virtual machine, which includes an installation of Python and data science libraries. The course setup instructions provided in the first module of this course go into more detail about the hardware and software requirements.
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In this course, you'll continue developing your data science skills in Python by working with one of the most fundamental data science libraries—NumPy. You'll create NumPy arrays, load and save NumPy data, and analyze data in arrays. You'll also manipulate and modify data in those arrays. This is the second course in a multi-course Specialization. All of the courses in this Specialization require that you use the provided virtual machine, which includes an installation of Python and data science libraries. The course setup instructions provided in the first course go into more detail about the hardware and software requirements.
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To round out your exploration of data science in Python, in this course, you'll work with the pandas DataFrame—one of the most prominent data structures in data science. You'll create DataFrames, load and save data, analyze data, and slice and filter data in DataFrames. Then, you'll manipulate, modify, and plot DataFrame data. Lastly, you'll work with specialized plotting libraries Matplotlib and Seaborn to create common types of plots and format those plots so they are visually appealing and optimal for analysis. This is the third and final course in a multi-course Specialization. All of the courses in this Specialization require that you use the provided virtual machine, which includes an installation of Python and data science libraries. The course setup instructions provided in the first course go into more detail about the hardware and software requirements.
Taught by
Bill Rosenthal