Advanced skills for the job-ready data scientist
A fully online, self-directed program
A Gallup study predicted that by 2021, 69% of employers expected that candidates with data science skills would be given preference for jobs in their organizations. Yet only 23% of college and university leaders say their graduates will have those skills by 2021. Pick up the data science solutions for you new career.
Data Scientist specialization objectives
- Use Python and SQL to access and analyze data from several different data sources.
- Use principles of statistics and probability to design and execute A/B tests and recommendation engines to assist businesses in making data-automated decisions.
- Deploy a data science solution to a basic flask app
- Manipulate and analyze distributed datasets using Apache Spark
- Communicate results effectively to stakeholders
A month-to-month subscription program
How much can you complete in a month?
Most students take four to five months to complete this specialization. In this self-directed, online format, you can pause your studies for up to two months (at the end of any month) and pick up where you left off. You have up to six months to complete this program. Learn more about the month-to-month subscription plan.
The benefits of an Udacity/UCSC program
The curriculum is designed by Udacity, our global online education partner. Here are some of the perks of choosing this program
- Top University of California/Udacity technical curriculum.
- A certificate of completion from UC Santa Cruz, an accredited university with a global reputation and a 50+-year history of exceptional professional training.
- A UCSC Silicon Valley-branded digital badge to show off your acquired skills on LinkedIn and provide aptitude verification for employers.
- 12 continuing education units (CEUs).
- A self-paced, soft skills curriculum (0.5 CEUs) to strengthen your critical interpersonal skills for the job market.
- One-on-one career counseling session and workshops with one of our workforce partners for those who are eligible.
- Online job search tools and recruitment partners with Handshake, the No. 1 national virtual job fair platform for college students.
- A 24/7 online resume-building tool that leverages data science, machine learning, and natural language processing to provide you with instant personalized feedback on your resume based on criteria gathered from employers and global best practices.
- Engagement with the UC Santa Cruz community through live, topical webinars and career-related events.
- A Udacity Nanodegree certificate.
Estimated Cost: Varies. (You pay only for courses you enroll in)
Required Credits: 12 CEUs
Duration: Month-to-month program. Usually four to five months.
Specialization Inquiry Form
The curriculum is provided in sequence. You may progress at your own pace.
Segment 1: Solving data science problems
- The Data Science Process
- Communicating with Stakeholders
Segment 2: Software engineering for data scientists
- Software Engineering Practices
- Object Oriented Programming
- Web Development
Segment 3: Data engineering for data scientists
- ETL Pipelines
- Natural Language Processing
- Machine Learning Pipelines
Segment 4: Experiment design and recommendations
- Experiment Design
- Statistical Concerns of Experimentation
- A/B Testing
- Introduction to Recommendation Engines
- Matrix Factorization for Recommendations
Segment 5: data science projects
- Elective 1: Dog Breed Classification
- Elective 2: Starbucks
- Elective 3: Arvato Financial Services
- Elective 4: Spark for Big Data
- Elective 5: Your Choice
Python • SQL • Statistics
Python programming skills
Writing functions, logic, control flow, and building basic applications, as well as common data analysis libraries like NumPy and pandas • SQL programming: Querying databases using joins, aggregations, and subqueries • Comfortable with using the Terminal, version control in Git, and using GitHub
Probability and statistics skills
Descriptive Statistics: Calculating measures of center and spread, estimation distributions • Inferential Statistics: Sampling distributions, hypothesis testing • Probability: Probability theory, conditional probability
Calculus: Maximizing and minimizing algebraic equations • Linear Algebra: Matrix manipulation and multiplication
Accessing database, CSV, and JSON data • Data cleaning and transformations using pandas and Sklearn
Data visualization with matplotlib
Exploratory data analysis and visualization • Explanatory data visualizations and dashboards
Feature Engineering • Supervised Learning: Regression, classification, decision trees, random forest • Unsupervised Learning: PCA, Clustering