In this course, students learn the foundational skills of data science, including data collection, scrubbing, analysis, and visualization with modern tools and libraries. Students gain a strong grounding in statistical concepts, utilize statistical techniques and master the science and art of data exploration and visualization to tell stories and persuade decision makers with data-driven insights.
The skill set gained in this course will jump start your career as a Data Scientist, Data Engineer or Data Analyst, if that is your goal. If you are a Software Engineer, these skills will enable you to effectively interface with a Data Team.
Course Delivery: Synchronous | 7 weeks | 19 sessions
Course Credits: 3 units | 37.5 Contact Hours/Term | 92 Non-Contact Hours/Term | 129.5 Total Hours/Term
By the end of this course, students will be able to...
- Perform exploratory data analysis and visualization of a data set using histograms, correlations and other mathematical tools
- Understand and use descriptive statistics, probability distributions and confidence intervals
- Carry out hypothesis tests, and understand when to reject or accept a null hypothesis
- Describe and implement a plan for cleaning and standardizing a data set
- Implement a machine learning pipeline for a simple model
Course Dates: Monday, May 31 – Friday, July 16, 2021 (7 weeks)
Class Times: Monday, Wednesday, Friday at 9:30am–11:15am (19 class sessions)
We will be using Gradescope, which allows us to provide fast and accurate feedback on your work. All assigned work will be submitted through Gradescope, and assignment and exam grades will be returned through Gradescope.
As soon as grades are posted, you will be notified immediately so that you can log in and see your feedback. You may also submit regrade requests if you feel we have made a mistake.
Your Gradescope login is your Make School email, and your password can be changed at https://gradescope.com/reset_password. The same link can be used if you need to set your password for the first time.
- Pandas and Numpy
- Data Visualization
- Probability and Statistics
- Probability Distributions and Outliers
- Hypothesis Testing
- Machine Learning project: You will choose a dataset to clean, investigate, and make predictions on
- All projects will be submitted on Gradescope
To pass this course you must meet the following requirements:
- Actively participate in class and abide by the Attendance Policy
- Complete and pass all Assignments and Projects with a score of above 70%
- Make up classwork from all absences
- Data Exploration
- Data Visualization
- Data Cleaning
- Outlier identification
- Data Preprocessing
- Applied Probability and statistics
- Probability distributions
- Hypothesis Testing
- Confidence intervals
- Machine Learning Model
- Machine Learning Pipeline
Students are expected to practice academic integrity in all of its forms, including abstaining from plagiarism, cheating, and other forms of academic misconduct. Make School reserves the right to determine in any given instance what action constitutes a violation of academic honesty and integrity. Plagiarism, defined as the practice of presenting another's work or ideas as one’s own, is an act of academic dishonesty and is a serious ethical and scholarly violation.
Copying text or ideas, whether verbatim or by paraphrasing from a source without using proper citation, is not accepted at Make School. Any materials incorporated into your work, regardless of format, must be properly acknowledged using a citation style appropriate for the discipline of the course.
Though plagiarism may be the most common form, other violations of scholarly integrity also constitute cheating, including:
- Using or copying information from another student’s code or written work;
- Copying information from another student’s test or using unauthorized materials during an examination, whether an in-class or take-home exam;
- Buying, selling, or stealing test questions, answers, or term papers;
- Doing work or taking tests on behalf of another student or submitting work done by another person;
- Falsifying data or laboratory results; and
- Submitting the same work for more than one course without explicit instructor approval.
If an incident of plagiarism or cheating occurs, the instructor will investigate the incident and consult with the Dean. If the student has been found to have committed an act of academic dishonesty, an Academic Misconduct Report will be filed and the student will be placed on a Participation Improvement Plan (PIP). A student who believes they have been wrongly accused of plagiarism or cheating, or that the instructor’s resolution of the alleged incident is unjust, may file a Request for Appeal of Disciplinary Action.
- Program Learning Outcomes - What you will achieve after finishing Make School, all courses are designed around these outcomes.
- Grading System - How grading is done at Make School
- Code of Conduct, Equity, and Inclusion - Learn about Diversity and Inclusion at Make School
- Academic Honesty - Our policies around plagerism, cheating, and other forms of academic misconduct
- Attendance Policy - What we expect from you in terms of attendance for all classes at Make School
- Course Credit Policy - Our policy for how you obtain credit for your courses
- Disability Services (Academic Accommodations) - Services and accommodations we provide for students
- Online Learning Tutorial - How to succeed in online learning at Make School
- Student Handbook - Guidelines, policies, and resources for all Make School students