Class Introduction

The data growth predicted by International Data Corporation (IDC) for 2025: 163 zettabytes. Machine learning patents grew at a 34% Compound Annual Growth Rate (CAGR) between 2013 and 2017, the third-fastest growing category of all patents granted. Additionally, IDC forecasts that spending on AI and ML will grow from $12B in 2017 to $57.6B by 2021.

The new opportunity is massive data growth, advanced computing power, and then cheap storage. The combination of these three propels us into more careers into data-driven projects using machine learning work.

Data Science Applied Labs is a project-driven course that will teach students the practical aspects of data science, such as collecting data by web scrapping, validation of information in data by data analysis, comparing models created by machine learning algorithms by interpreted metrics, and more.

Class Duration: 2 weeks

Days: Saturday & Sunday

Time: 10AM – 6PM

Locations: Los Angeles, Orange County

Price: $4995 (Financial options available)

Prerequisites and Requirements: You can take our PreWork materials for Python & statistics ahead of time to prep for the course, or even to brush up on your skills.

Do you offer certification? Yes

Why you should take this class? We offer the first four days of our 16-week course to help students have a general idea of where to applied data science, as well, as decide if they wish to keep pursuing the rest of the material.

Additional FAQs about the course? N/A

How to Register? Apply for initial data science career advising in this page form.

Related Courses: N/A

Teachers & Credentials: N/A

Average Salary Post Graduation: N/A

Who is this class best suited for? This class is for those who do not know whether they can or will be able to continue throughout the entire 16-week course & would like to decide if data science is the right fit for them.

Class Size: 5:1, student-to-teacher ratio.

Data Science Boot Camp


Any Questions Call : 888-713-9711

Data Science Applied Labs

Session I : Introduction to Data Science with Python

In our first class, we will go over some Python fundamentals, which will cover syntax and built-in functions. We will move onto practicing For Loops and introducing the packages that will be covered over the course and how to install them.

Session II : Exploratory Data Analysis

We will start by introducing NumPy and Pandas and showcasing how to clean, manipulate, and analyze data. Students will practice on the Titanic dataset before moving onto web scraping techniques and extracting data from APIs.

Session III : Advanced NumPy and Pandas

We will begin by reviewing NumPy and Pandas before delving deeper into more advanced techniques to clean and munge data. Using Matplotlib and Seaborn packages, students will learn to visualize data and identify trends.

Session IV : Data Mining and Machine Learning

We will be introducing the Cross Industry Standard Process for Data Mining (CRISP-DM) and data mining with supervised learning and unsupervised learning. Afterwards, students will explore machine learning algorithms such as Regression (Linear, Multivariable, and Logistic), Naïve Bayes, Decision Trees, and Clustering.

Session V : Machine Learning Concepts

Students will review machine learning concepts and will start by building their own recommendation system with a MovieLens dataset, understanding dimension reduction with Principal Component Analysis, exploring Support Vector Machines, and learning A/B Testing with T-Tests and P-Values.

Session VI : Natural Language Processing and Sentiment Analysis

Students will explore the Natural Language Toolkit to process and extract text data. Students will then start a Natural Language Processing project with Yelp data before we move onto Sentimental Analysis to predict positive versus negative Yelp reviews.

Session VII : Big Data with Spark

Students will be introduced to Big Data and data engineering with the Hadoop ecosystem, the MapReduce paradigm, and the up-and-coming Apache Spark.

Session VIII : Deep Learning and Time Series

We will be introducing deep learning and training neural network and visualizing what a neural network has learned using TensorFlow Playground. Students will also learn time series, what makes them special, loading and handling time series in Pandas. Understand how seasonality affects trends.

Session IX : Computer Vision with OpenCV and Hack Project

Students will be introduced to computer vision fundamentals using OpenCV to detect faces, people, cars, and other objects. We will conclude the day with a hack challenge. Students will be grouped into teams and will showcase their group project at the end of class.

Session X : Hack Day

In the last session, we will host a private Kaggle competition amongst the students. Students will be grouped into teams and will showcase their group project at the end of class.