Data Science Foundations to Core Bootcamp

You’ll build your Python skills further by exploring object-oriented programming and Python strings.

Probability is the science of uncertainty,  and anyone in data science, machine learning, or AI has to be comfortable with it. In this unit, you’ll learn the fundamentals of probability and how it relates to data science.

You’ll explore Python further by studying data structures and algorithms to understand how they can be used to solve problems in programmable ways.

The Technical Skills Survey (TSS) will determine if you’re ready to move into the Core units.. You’ll be able to take the TSS as many times as you like. A rundown of the TSS:

Once you pass the foundational units and the TSS, you will unlock a powerful unit that will take you deeper into the world of data science, including learning the necessary tools to succeed. 

This unit encompasses everything you’ve learned into a hands-on project where you’ll look at real-world datasets. You’ll practice using data science tools to analyze and visualize data, and learn how to present your insights. A breakdown of the steps:

Before moving on to the technical units of the Core curriculum, you’ll work through exercises on Python, the most popular programming language for data science tasks, and get a crash course in statistics from Khan Academy.

In this opening unit of Core, you’ll receive an overview of the Data Science Method and learn the skills needed to thrive in the field. You’ll hear about your day-to-day work duties, including data cleaning and building models from those in the field. 

To start a data science project, you need to know the problem you need to solve, clearly define it, then break the problem down into manageable pieces. In this unit, you’ll work through the first step of the data science method — identify the correct problem to solve and set goals for a project.

Python is a must-have programming skill in the data science world. You’ve already laid the foundation in pre-work, but this unit will teach you the language in-depth and also help you leverage pandas for data cleaning and manipulation. 

This unit gives you an introduction to the steps of the Data Science Method (DSM) and closes with a guided capstone where you’ll present to stakeholders. Throughout the Core part of the program, you’ll learn each step in depth and then apply what you learned through three robust capstone projects. 

This unit explores wrangling — or how to clean, organize, and structure raw data — in a hands-on way by having you wrangle data for your second capstone.

In this unit, you’ll learn the inner workings of Structured Query Language (SQL) to query relational database management systems. Querying helps you understand the data contained in the databases. You’ll work through Datacamp courses and then a case study using your new-found knowledge of databases. 

Statistics is the mathematical foundation of data science. It allows you to draw useful conclusions from data. In this unit, you'll learn concepts from David Spiegelhalter’s book, “The Art of Statistics.” You’ll read through one or two chapters, work on an exercise, test your knowledge with a quiz, and review takeaway notes.

Inferential statistics is a set of techniques that helps you identify significant trends and characteristics of a data set. Not only is it useful to explore the data and tell a good story, but it also paves the way for deeper analysis and actual predictive modeling. In this unit, you’ll learn several inferential statistics techniques, then take your learnings and apply the Exploratory Data Analysis (EDA) step to your second capstone.

Machine learning combines aspects of computer science and statistics to extract useful insights and predictions from data. In this unit, you'll begin to learn the foundations of machine learning and understand best practices and common challenges when working on machine learning applications. 

Supervised learning is one of the most commonly used forms of machine learning.  In supervised learning, you give the machine your labeled training data and encode procedures for the machine to learn to assign those labels itself. 

Unsupervised learning requires minimal human supervision. Unlike supervised learning, the machine looks for patterns in a dataset with no pre-existing labels.  In this unit, you’ll perform clustering techniques and then complete a case study on k-clustering. 

Feature engineering consists of converting data into a feature matrix to look for patterns and create features from raw data. It’s a vital skill that improves the performance of machine learning models. In this unit, you’ll work through completing exercises and honing the pre-processing and training data development side of the DSM.

Furthering your understanding of machine learning, this unit takes you behind the scenes of modeling metrics and hyperparameter tuning. You’ll complete exercises on model evaluation metrics and learn which model metric to use based on the business problem you’re trying to solve. You’ll also learn how hyperparameter tuning can make or break your model. You’ll finish up the unit by working on the modeling stage in capstone two.

A data story is a powerful way to present insights to your clients, combining visualizations and text into a narrative. This final core unit will get your creative juices flowing by suggesting some interesting questions you can ask of your dataset. You’ll also execute the last stage of the DSM (Documentation) by developing a final project report.

Hone your skills in a specific area of expertise by choosing one of our three specialization track options. You’ll be able to talk to your mentor and career coach before deciding.

Option 1 — Generalist Track:
If you’re interested in gaining a wide range of skills that will help you land a job in various industries (and in various locations), the Generalist track may be right for you.

Option 2 — Business Insider Tracker:
If you’re keen to learn how to draw business-focused insights from data and make actionable recommendations that can impact the company you work for, the Business Insider track may be right for you.

Option 3 — Advanced Machine Learning:
If you loved the machine learning units and want to continue to learn advanced machine learning skills, including how to deploy a model to production, then the Advanced Machine Learning track may be the right choice.

You’ll work on three Capstone projects to give you the hands-on knowledge of working like a data scientist.

Capstone 1 (Guided Capstone):
You’ll be introduced to the Data Science Method (DSM) early on in the program, then execute each of these important steps through guidance from your mentor. You’ll practice each step before applying your knowledge to your second capstone.

Capstone 2:
Similar to the guided capstone one, you’ll execute the steps of the DSM but with less guidance. You’ll develop a project idea and proposal, find and wrangle data, use exploratory data analysis techniques, pre-process and create a training dataset, build a working model, then document and present your work. You’ll submit each step separately.

Capstone 3:
This capstone runs through the steps of the DSM, but you’ll choose your project idea depending on the specialization track you’re enrolled in.

Career units throughout the bootcamp will help you create a tailored job search strategy based on your background and goals. Topics include: