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Weekends Mastering Applied Data Science Program
Sat, Apr 22, 2017, 10:00 AM – Sun, Jul 9, 2017, 5:00 PM PDT
Our Mastering Applied Data Science course combines both our Data Science Bootcamp and our Project Based Learning to give students not only a solid foundation in data science, but also project experience.
In Data Science Bootcamp, you will cover the fundamentals of data science and hone your skills through various projects and assignments. Upon completion, you will compete with your peers in a private Kaggle competition to showcase what you have learned throughout the course.
In Project Based Learning, you will put your knowledge to use by building your own end-to-end machine learning projects from scratch. Deepen your knowledge by solving real-world problems and learning best practices so you can start taking on your own projects.
This course is for professionals aspiring to be Data Scientists, students who want to learn about Data Science, Statisticians, Mathematicians and Project Managers who want to expand their horizon into Data Science, Business and Data Analyst wanting to advance their career or any person who is interested in Data Science.
This is a weekends course with instruction from 10am - 6pm Saturday and Sunday.
In this program you’ll learn how to approach a data science problem from start to finish :
- Collect data from a variety of sources (e.g., Excel, databases, web scraping, APIs and others)
- Explore large data sets
- Learn to use Python for executing Data Science Projects
- Master analyzing datasets and Machine Learning techniques
- Know how to use matplotlib and seaborn libraries to create beautiful data visualization.
This is a very practical and hands-on program that has lots of class exercises and assignments. Through this course, we strive to make you fully equipped to become a Data Scientist who can execute full-fledged Data Science projects.
Introduction to Data Science with Python
In our first class we will go over some Python fundamentals, which will cover syntax, data structures, and built-in functions. We will move on to practicing for loops, functions, and introducing the packages that will be covered over the course and how to install them.
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 on to web scraping techniques and extracting data from APIs.
Fundamental Modeling Techniques and Data Visualization
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.
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 Linear Regression, Multivariable Regression, and Logistic Regression, Naive Bayes, Decision Trees, and ensemble techniques.
Machine Learning Concepts and Recommendation Systems
Students will review machine learning concepts including K-Nearest Neighbors Classification, K-Means Clustering, and will start building their own recommendation system with a MovieLens dataset, understanding dimension reduction with Principal Component Analysis, exploring Suport Vector Machines, and learning A/B testing with T-Tests and P-Values.
Natural Language Processing and Sentiment Analysis
Students will explore the natural language toolkit (NLTK) to process and extract text data. Students will then start a Natural Language Processing project with Yelp data before we move on to Sentimental Analysis to predict positive versus negative Yelp reviews.
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.
Deep Learning and Time Series
We will be introducing deep learning and training neural networks 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.
Computer Vision with OpenCV
Students will be introduced to computer vision fundamentals using OpenCV to detect faces, people, cars, and other objects.
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.
WEEK 6 - 12
Project Based Learning
- Project 1: Skill Building Project - Students will apply the Cross Industry Standard Process for Data Mining (CRISP-DM) standard in a provided dataset to understand the process behind starting a new project.
- Project 2: End-to-End Development - Students will undertake a new project from start to finish. This project will allow students to demonstrate their skills in data acquisition, data cleaning, data enrichment, modeling, evaluation, and deployment.
- Project 3: Domain-Specific Project - Students are given the option of choosing a project in a domain of their choosing.
Prereqs & Preparation
Students must bring a laptop and should install Anaconda, which is a free package that includes python and a number of tools that will be used in class (http://continuum.io/downloads).
Anyone taking this course should have some minimum experience with programming with R, Python, or any other programming language.
If not, we offer our Python Foundation Course for Free!!!