Welcome to CS250: Python for Data Science

Specific information about this course and its requirements can be found below. For more general information about taking Saylor Academy courses, including information about Community and Academic Codes of Conduct, please read the Student Handbook.

Course Description

Learn data science using the Python programming language by looking at data processing, data analysis, visualization, data mining, and statistical models. By the end of this course, you will be able to implement Python code for these data science topics.

Course Introduction

[p]This course attempts to strike a balance between presenting the vast set of methods within the field of data science and Python programming techniques for implementing them. Problem-solving and programming implementation will be emphasized throughout the course. All techniques presented will be introduced using real-world programming examples. A major goal of the course is to ensure that when you finish the course, you will have the programming and conceptual expertise you need to join the field of data science.[/p] [p]Several Python modules such as pandas, scikit-learn, scipy.stats, and statsmodels will be introduced that are useful for data analysis, data visualization, and data mining. The course will gradually shift from introductory topics such as a review of Python, matrix operations, and statistics to applications and implementing programs involving data mining, visualization, statistical models, and time series analysis.[/p]

This course includes the following units:

Unit 1: What is Data Science?. Unit 2: Python for Data Science. Unit 3: The numpy Module. Unit 4: Applied Statistics in Python. Unit 5: The pandas Module. Unit 6: Visualization. Unit 7: Data Mining I – Supervised Learning. Unit 8: Data Mining II – Clustering Techniques. Unit 9: Data Mining III - Statistical Modeling. Unit 10: Time Series Analysis.

Course Learning Outcomes

Upon successful completion of this course, you will be able to:

[1] use Google Colaboratory notebooks to implement and test Python programs; [2] explain how Python programming is relevant to data science; [3] construct and operate on arrays using the numpy module; [4] apply Python modules for basic statistical computation; [5] construct and operate on dataframes using the pandas module; [6] apply the pandas module to interact with spreadsheet software; [7] implement Python scripts for visualization using arrays and dataframes; [8] apply the scikit-learn module to perform data mining; [9] explain techniques for supervised and unsupervised learning; [10] apply supervised learning techniques; [11] apply unsupervised learning techniques; [12] apply the scikit-learn module to build statistical models; [13] implement Python scripts to perform regression analyses; [14] apply the statsmodels module to build and analyze models for time series analysis; [15] explain similarities and differences between AR, MA, and ARIMA models

Throughout this course, you will also see learning outcomes in each unit. You can use those learning outcomes to help organize your studies and gauge your progress.

Course Materials

The primary learning materials for this course are readings, lectures, and videos.

All course materials are free to access and can be found in each unit of the course. Pay close attention to the notes that accompany these course materials, as they will tell you what to focus on in each resource and will help you understand how the learning materials fit into the course as a whole. You can also see a list of all the learning materials in this course by clicking on Resources in the navigation bar.

Evaluation and Minimum Passing Score

Only the final examination is considered when awarding you a grade for this course. To pass this course, you will need to earn 70% or higher on the final exam. Your score on the exam will be calculated as soon as you complete it. If you do not pass the exam on your first try, you may take it again as many times as you want, with a 14 days waiting period between each attempt. Once you have successfully passed the final exam, you will be awarded a free Course Completion Certificate.

There are also end-of-unit assessments in this course. These are designed to help you study and do not factor into your final course grade. You can take these as many times as you want until you understand the concepts and material covered. You can see all of these assessments by clicking on Quizzes in the course's navigation bar.

Tips for Success

CS250: Python for Data Science is a self-paced course, meaning you can decide when to start and complete the course. We estimate the "average" student will take 67 hours to complete. We recommend studying at a comfortable pace and scheduling your study time in advance.

Learning new material can be challenging, so here are a few study strategies to help you succeed:

  • Take notes on terms, practices, and theories. This helps you understand each concept in context and provides a refresher for later study.
  • Test yourself on what you remember and how well you understand the concepts. Reflecting on what you've learned improves long-term memory retention.

Technical Requirements

This course is delivered entirely online. You will need access to a computer or web-capable mobile device and consistent internet access to view or download resources and complete auto-graded assessments and the final exam.

To access the full course, including assessments and the final exam, log into your Saylor Academy account and enroll in the course. If you don’t have an account, you can create one for free here. Note that tracking progress and taking assessments require login.

For additional guidance, check out Saylor Academy's FAQ.

Optional Saylor Academy Mobile App

You can access all course features directly from your mobile browser, but if you have limited internet connectivity, the Saylor Academy mobile app provides an option to download course content for offline use. The app is available for iOS and Android devices.

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Fees

This course is entirely free to enroll in and access. All course materials, including textbooks, videos, webpages, and activities, are available at no charge. This course also contains a free final exam and course completion certificate.

Last modified: Friday, 22 November 2024, 1:04 PM