Data Science Bootcamp

Prepare For A Data Science Career

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.

PROGRAM HILIGHTS

• 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.

Python & R

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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.

Data Science Toolkit

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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.

Data Science Process

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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.

Managing & Curating Data

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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.

Exploring Data Analysis

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You will learn exploratory techniques for visualizing & summarizing data. These techniques are typically applied before formal modeling. Exploratory techniques are important for eliminating or sharpening potential hypotheses as well as identifying problems with the data that need to be addressed before modeling. We will cover plotting libraries (matplotlib, seaborn, ggplot2, etc.) as well as some of the basic principles of constructing data visualizations.

SQL,Data Management & Big Data

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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.

Machine Learning : Supervised

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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:

• Cost Function
• Overfitting/underfitting
• Optimization techniques
• Linear and Logistic regression
• Decision Trees
• Classification models
• Recommender Systems

Machine Learning : Unsupervised

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You’ll learn to apply unsupervised machine learning algorithms to uncover trends and patterns in data. You'll get familiar with:

• Clustering methods, including K-Means and Affinity Propagation
• PCA & Dimension Reduction
• Anomaly Detection

Natural Language Processing

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You will learn to extract meaning from text by applying techniques such as:

• Tokenization
• Topic Identification
• Named Entity Recognition
• Text classification

Data Visualization & Comunication

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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.

Real World Projects

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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.

Career Preparation

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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.

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.

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