Data Science & Analytics Bootcamp

Introductory Course

In their first 30 hours, learners are introduced to core data science and analytics topics, such as computing basics and the data life cycle. Course participants will have the opportunity to learn the basics of a Jupyter notebook and interact with a Titanic dataset. They will pull the project materials from a GitHub repository and walk through it in a Google Colaboratory notebook. Learners can then look forward to completing three more projects throughout the remainder of the program to add to their personal portfolios.

SQL and Databases

In one of their first courses, learners are introduced to SQL, which is a standardized programming language used to query databases. Learners apply how to import data into databases, query data, join data together, filter and sort data, create views, and export data using SQL language. Further, learners are introduced to database design and learn how to manage their own databases.

Statistics and Probability

This course aims to enlighten learners on how, to what extent, and in what way statistics and probability are used in business decision-making. This course aids the learner in building a strong foundation in descriptive statistics, conditional probability, and advanced modeling techniques. Learners develop the ability to approach real-world problems from an analytical perspective with confidence, using Microsoft Excel to provide practical application to theoretical class problems and data discussions.

Data Storytelling

Learners discover the power of a story and how to develop a story arc around their data goals. Successfully communicating data insights depends on the audience of stakeholders and the story points that speak to their needs and expectations. Learners continue to keep a data story thread throughout their entire data wrangling adventure as they frame their data goals with purpose.

Milestone Project 1: Building & Presenting Data Stories

This milestone project allows learners to explore their skills in the areas of statistics, Excel, SQL, and data storytelling. Learners are able to demonstrate their ability to clean and manipulate a dataset. Additionally, learners perform advanced statistical analysis on the data using summary statistics, linear regression, and modeling. Finally, they act as professional data analysts to put their visualizations and insights into a coherent data story to present to their classmates. The data analytics milestone project is formally reviewed by the instructional team. Learners then incorporate their projects into their GitHub portfolio.

Advanced SQL Programming

In this course, learners revisit their previous knowledge from the SQL and Databases course to build upon advanced concepts that delve into SQL programming and covers specialized topics such as stored procedures, functions, common table expressions (CTEs), and query optimization. Learners also demonstrate the aptitude of a data scientist to develop ETL scripts and data pipelines combining the use of SQL and Python.

Python Programming

Learners explore the fundamental concepts of programming and how to structure their data analyses based on data science industry best practices. Topics include core programming concepts such as expressions, data types, variables, functions, loops, and arrays. Learners practice their coding skills through building highly structured and maintainable code using Jupyter notebooks.

Data Wrangling

Learners develop core data wrangling skills by expanding their Python programming skills. Learners explore a series of data analysis processes from sourcing, curating, and importing data to exploratory data analysis, data cleansing techniques, and data visualization techniques. Next, learners expand their toolbox by using industry-standard software to automate data wrangling processes.

Visual Communications

Just as data is crucial for metrics, visual representation of data through different mediums, graphics, charts are also necessary for skilled data analysts to acquire through training to be an effective member of a data science team. In this course, learners explore visual dynamics and principles to produce effective data visualizations that show the most important parts of data to stakeholders in a clear and simplified way.

Milestone Project 2: Data Integration, Prep- aration, Reporting, and Presentation

This milestone project focuses on developing the learner’s ability to attain, transform, investigate, and present data throughout a data project life cycle. Learners demonstrate their ability to build data pipelines and wrangle data into a usable format for downstream data visualization and analytics. For final evaluation by the instructional team, learners present their reports and findings to classmates and then incorporate their projects into their GitHub portfolio.

Business Intelligence

Learners build upon prior visual communication concepts by application of popular industry business intelligence tools to create insightful analyses and visualizations. Learners also develop and apply best practices for data analysis reporting, graphs and charts, and dashboards in a way that can be applied in any business intelligence application.

Machine Learning

Learners analyze a variety of use cases in a business context for determining appropriate machine learning methods to apply. Through a series of Python lectures and labs, and using Jupyter notebooks, learners investigate and apply supervised and unsupervised machine learning algorithms, including classification, clustering, association rules, and time-series forecasting. Next, learners explore several advanced methods, including natural language processing, neural networks, and deep learning.

Big Data

In this course, learners examine core concepts and methods used for big data and IoT, including characteristics of big data, data warehousing, data lakes, data virtualization, and cloud-based data infrastructure services. Learners build upon their previous knowledge of Python by using PySpark to access big data and create analytics models.

Applied AI

Following an introduction to machine learning-created AI in the previous module, learners gain exposure to a variety of common tools used by data science analyst professionals such as pre-packaged AI cloud services offered by leading providers like Microsoft, Amazon, and Google. Learners later easily identify and understand the ways in which AI provides a more targeted data analytics experience, producing a greater amount of data insights through various applications of computer vision, speech recognition, natural language processing, and robotics.

Milestone Project 3: Capstone Project: Delivering Insights and Presentations

For their capstone project, learners assume the professional lens of a data science analyst to approach the challenge of presenting their data insights and visualizations both clearly and with ease to diverse types of stakeholders. As learners individually synthesize their knowledge and skills into a tangible asset, they actively practice how to organize goal-oriented data visualizations and integrate a story arc to keep their audience engaged. Learners achieve this milestone in a culminating project presentation that showcases their data analysis, data storytelling, and presentation skills, which they share by uploading to their professional-grade GitHub portfolios.

Career Services

This course is dedicated to supporting the learner’s job search, pairing learners with the career curriculum team to acquire the knowledge and skills to be successful in the digital job market. Learners emerge confident in job placement and are equipped with the tools to readily vet, apply, and interview for roles in in-demand professional data science careers and data analyst careers.