The Data Science Bootcamp is an intensive, part-time evening program designed to prepare highly motivated adult learners with prior experience in statistical reasoning, quantitative research, and/or software engineering for a job as a Data Scientist, Analytics Consultant, Data Engineer or related position.
Data Science lies at the intersection of statistics/quantitative analysis, business acumen/domain expertise, and programming/“hacking” skills. The bootcamp focuses on hands-on training in software engineering skills required to do data science as well as applying the necessary stats/math/analytical skills against real world problems drawn from a wide range of problem domains.
We believe that the MAKENOW has an untapped supply of latent talent for data analytical work. But, that talent needs access to accelerated, focused, real-world training. We’ve designed the Data Science Bootcamp to help address this need.
The Web Developer Bootcamp is an intellectually stimulating, horizon broadening, energizing, and fun experience. Students emerge with the skills necessary to build ideas from concept to production. By the end of the program, you will find yourself prepared for an entry-level software developer position. This bootcamp is the ultimate challenge on the road to a new career as a programmer. It's intense—as in five days per week, hands-on, writing lots of code. There are regular individual and group projects, and the daily introduction of new material and best practices. It’s a fast-moving and rigorous journey where you will learn a lot about programming and, coincidentally, about yourself.
Part-time evening program that will meet two evenings a week and on Saturdays for a total of nine months..
Hands-on training in software engineering skills required to do data science. Students will learn to apply the Python and R languages to data analytics problems.
Application of stats/math/analytical skills against real-world problems drawn from a wide range of problem domains, including: digital marketing, supply chain, healthcare, retail and financial services.
Hands-on experience applying data engineering skills to sourcing, cleaning, and aggregating data for data science projects. Training on major “Big Data” tools such as Hadoop and Spark, as well as cloud-based data management through services such as AWS.
Student prerequisite knowledge/experience
Ideal applicants will have experience in quantitative science or programming. Students will be expected to demonstrate prior training in and/or experience applying the math skills relevant to data science. In particular we will be looking for students with training in statistics/probability. This prior knowledge can be gained through classes, research project experience, work experience, and/or our Statistics for Data Science course.
Personal laptop meeting our hardware requirements. See this blog post for full details of our laptop specs.
Accepted students will be given requirements for software to be downloaded prior to the start of class. It is not expected that any such software will have a cost as the class will focus on teaching data science using open source technologies.
Data Camp
Thanks to DataCamp for partnering with us through their Education program to support learning in our data science bootcamp. DataCamp is an interactive learning platform for data science. Their partnership provides our Data Science students access to their content in R, Python, and SQL on importing data, data visualization, machine learning, deep learning & more.
The foundational concepts you’ll learn are universal. The technologies are important and may influence what companies would hire you for your first job, but all are widely used in globally. This is just the beginning of a lifetime of learning! You can build a great career on and around any of the technologies that we teach. If your goal is to switch careers and you don’t already know a specific technology that makes the most sense for you, then just get started. As a professional software developer, you’ll need to learn multiple languages, and many, many new technologies. Many of our graduates learned two languages in class and then went on to learn a third, or even fourth, language since they graduated and went to work.
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Course Features |
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| Lectures | 6 |
|---|---|
| Quizzes | 1 |
| Duration | 3 hours |
| Skill Level | All Levels |
| Language | English |
| Student | Nill |
| Certification | Yes |
| Assessment | Yes |
You will learn to create data analytic workflows in the two most widely used programming languages in data science. You will learn the editors, IDEs, source code control systems, etc. used by Data Scientists.
You will gain facility in using the most popular libraries and packages associated with Python and R, e.g. pandas, scikit-learn, the tidyverse. You’ll learn to use RStudio and Jupyter Notebooks for writing code and documenting workflow.
You’ll learn the project life-cycle of a typical data science project. You’ll learn how to identify the business question, how to create and refine your hypothesis, build models, test and iterate the analysis, and ultimately deploy the resulting data product and communicate the results.
You’ll learn the process of collecting, extracting, querying, cleaning, and aggregating data for analysis. You will apply the tools in both the Python and R toolkits to this process in multiple projects. You’ll spend time wrangling data, understanding data quality issues, and learning how to clean data. You will work with a variety of data sources from unstructured/semi-structured text files to delimited/structured file formats such excel, csv, json, xml etc. You’ll also learn about web scraping and working with APIs.
SAS has a powerful programming feature called "Macros" which allows us to avoid repetitive sections of code and to use them again and again when needed.
You’ll master the use of SQL to query relational databases. You’ll also be introduced to the 4 main types of NoSQL systems and understand the tradeoffs of using each one.
You’ll be introduced to techniques for working with Big Data in a cloud environment.
You’ll learn to use training data to develop supervised machine learning models and apply them to a range of different problems. Some of the concepts you’ll learn about include:
1) Cost Function
2) Overfitting/underfitting
3) Optimization techniques
4) Linear and Logistic regression
5) Decision Trees
6) Classification models
7) Recommender Systems
You’ll learn to apply unsupervised machine learning algorithms to uncover trends and patterns in data. You'll get familiar with:
1) Clustering methods, including K-Means and Affinity Propagation
2) PCA & Dimension Reduction
3) Anomaly Detection
You will learn to extract meaning from text by applying techniques such as:
1) Tokenization
2) Topic Identification
3) Named Entity Recognition
4) Text classification
A key skill for data scientists is presenting the results of their projects to business decision makers and other stakeholders. You’ll learn common tools and techniques of data visualization and how to use them for effectively communicating the story of your data and your analysis.
You will be able to apply the skills you are learning to real-world datasets and problems from various domains, such as healthcare, financial services, entertainment, consumer marketing/retail, and government. Projects will be executed primarily in a team environment so you’ll get experience working with others in a multi-disciplinary project team.
Your capstone project will be an individual effort that demonstrates your ability to take a data science project through the entire data science process. This project will demonstrate to potential employers your ability to apply the skills learned in this class to a real-world problem and present your findings.
Throughout the bootcamp you will also be preparing to move into a data science job. You’ll meet working data scientists from several industries. We’ll hold workshops on resume preparation/marketing yourself, interview preparation, negotiating, and more. And we’ll introduce you to prospective employers at your class Demo Day and support you after graduation during your job search.
Months 1-3 of the Web Development Bootcamp prepares students to work on building the front-end code associated with websites, web apps and hybrid mobile applications. Students learn to connect their front-end code to server code through open web APIs or custom web services. For many of our students this course is also their introduction to programming. The focus of this course is on the three standard languages of the web: HTML, CSS and JavaScript.
Months 4-6 of the program are focused on the Software Development Foundations. All of these courses cover the same general software engineering and server-side development concepts, such as test-driven development, object-oriented programming, pair programming, source code control in a team environment, common code patterns, SQL database access, and more. However, each course teaches those concepts in the context of a different programming language and associated technology stack.