Become a Data Science professional in just 10 weeks
Dive into the world of Data Science, data modeling, machine learning, and more in this advanced Deep Dive Coding Bootcamp. You will learn to solve critical business problems within your domain of expertise with new skills in programming, modeling, and data analysis. Students will work on labs and projects to build and maintain data models. Graduates will be able to design and implement a real-world model producing actionable information.
Data scientists are in short supply, with a projected demand in roles for data scientists to spike 28% by 2020 according to Burning Glass. According to IBM, by 2020 the number of data science job listings is projected to grow by nearly 364,000 listings, to about 2,720,000 openings. This demand will only grow further to an astonishing 700,000 openings. Glassdoor estimates that Data Scientists are well compensated, with an average salary in Albuquerque of $75-$125K, and up to $140K nationwide.
Dates: February 24 – May 1
Application Deadline: February 1, 2020
Specific dates and times TBD.
Learn the Technology
We’ll teach you the languages, tools, and techniques you’ll be using as a data scientists in the real world. This bootcamp focuses on…
- Critical thinking and project selection
- Python and open source libraries for data science
- Visualization tools to explore data sets for further processing
- Data wrangling techniques to prepare and refine raw sets
- SQL queries and useful syntax for access to data
- Machine learning algorithms
- Cloud based Jupyter notebooks and other deployment tools
- GITHUB, Slack, Agile development tools
- Expert level knowledge of their declared application domain. A domain expert has at least 2-5 years of relevant experience and education and understands their application area well enough to:
- Solve significant problems in their area
- Write papers or blog posts about their work
- Speak about their work at a conference of their peers
- Recognize results in a model that are either invalid or trivial (this is the important one for our purposes!)
- Basic knowledge of statistical concepts, probability distributions, and linear algebra will be required. To meet this pre-requisite, it is required that you take our short assessments on linear algebra and probability and statistics.
- Coding experience will be required.
Pre-bootcamp (before the program starts)
Pre-work will be assigned to you before the bootcamp starts. All the resources used in the pre-work are accessible online or will be provided to you. During pre-work, you will set up your system accounts, meet with the Bootcamp Success Manager, prep your software environment, work through multiple tutorials on DataCamp*, and will make other final preparations. Pre-work is mandatory and must be completed prior to orientation, which is generally a few days before bootcamp begins. The sooner you start and the more time you spend on your pre-work, the more prepared you’ll be. We recommend that you give yourself at least a month to complete this work prior to the start of the bootcamp.
*This class is supported by DataCamp, the most intuitive learning platform for data science. Learn R, Python and SQL the way you learn best through a combination of short expert videos and hands-on-the-keyboard exercises. Take over 100+ courses by expert instructors on topics such as importing data, data visualization or machine learning and learn faster through immediate and personalized feedback on every exercise.
Data Science Fundamentals (Weeks 1-3)
- Learn project selection and use case development – critical thinking
- Overview of the Python computer language
- Learn and practice cloud-based processing using Jupyter notebooks
- Learn about data sources and collection methods
- Conduct extensive exploratory data analysis
- Survey of data visualization tools
- Practice using the probability and statistics needed by Data Scientists
Statistical and Machine Learning (Weeks 4-7)
- Learn and practice database queries with SQL
- Clean up dirty data sets (data wrangling)
- Learn and practice regression techniques (linear, logistic, regularized)
- Supervised learning techniques: K-nearest neighbors, decision trees, random forests, and boosting
- Unsupervised learning techniques: Clustering, dimensionality reduction
- Deep learning: Image identification using Convolutional Neural Networks
- Learn performance improvement techniques: regularization, avoiding over-fitting, data augmentation
Capstone Project (Weeks 8-10)
- Immerse yourself in your team capstone project
- Create clean, functional, and documented code for your Github resume
- Demonstrate your team capstone projects to employers, staffing agencies and others in the tech community
$9,995 is the discounted price for NM residents.
$10,995 for out-of-state applicants.
Reserve Your Seat Now: A $1,500 non-refundable deposit is required prior to the application deadline in order to reserve your seat. The remaining course fees must be paid in full by the application deadline.