This course serves as an introduction to the data science principles required to tackle real-world, data-rich problems in business & academia, including:
Data acquisition, cleaning, & aggregation
Exploratory data analysis & visualization
Model creation & validation
Basic statistical & mathematical foundations for data science
Who the course is designed for:
You have a strong desire to learn data science through top-quality instruction, a basic understanding of data analysis techniques & an interest in improving their ability to tackle data-rich problems in a systematic, principled way. This course provides structure & accountability to ensure you stay on track, finish strong, & achieve your desired outcomes.
An understanding of problems solvable with data science & an ability to attack them from a statistical perspective.
An understanding of when to use supervised & unsupervised statistical learning methods on labeled & unlabeled data-rich problems.
The ability to create data analytical pipelines & applications in Python.
Familiarity with the Python data science ecosystem & the various tools needed to continue developing as a data scientist.
What you'll receive upon completion:
Certificate of completion
Certificate link & instructions on how to add to your LinkedIn profile
3.3 Continuing Education Units
Students should have some familiarity with basic statistical & linear algebraic concepts such as mean, median, mode, standard deviation, correlation, & the difference between a vector & a matrix. Additionally, Python is a requirement for the course. In Python, it will be helpful to know basic data structures such as lists, tuples, & dictionaries, & what distinguishes them (that is, when they should be used). Python v2 is currently used in the course.
To ensure everyone begins the course on the same page, students are encouraged to complete the following pre-work (approximately 8 hours) before the first day of instruction:
Exercises 1-7, 13, 18-21, 27-35, 38,39 of Learn Python The Hard Way.
Videos 1-6 of Linear Algebra review from Andrew Ng's Machine Learning course (labeled as: III. Linear Algebra Review (Week 1, Optional).
The exercises in Chapters 2 & 3 of OpenIntro Statistics.