Data Science Adaptive

This course is 12-week, mentor-driven course that teaches you how to complete up to three Machine Learning projects through application of Supervised and Unsupervised Learning techniques. You will learn how to apply Machine Learning algorithms to real-world applications from examples such as Amazon Movies, City Bike sharing, Airline Flight Delays, Yelp Restaurant reviews, and Datasets from Kaggle Competitions. This course is designed specifically for intermediate to advanced technology professionals.

• Duration: Rolling program admissions that start at the beginning of each month
• Schedule: 2-hour per week scheduled with a mentor; Plus optional office hours
• Level: Intermediate to Advanced
• Pricing: $3,500

• Scrape, filter and analyze large data sets
• Take advantage of practical tools and scale analysis using Hadoop and Cloud Infrastructures
• Apply Data Wrangling, Exploratory Data Analysis, Model Selection and Diagnostics methods

SUPERVISED & UNSUPERVISED LEARNING WITH TABULAR DATA

WEEK 1: CASE STUDY INTRODUCTION PART 1, SUPERVISED LEARNING TECHNIQUES
WEEK 2: PRE-PROCESSING TECHNIQUES
WEEK 3: FEATURE ENGINEERING PART I
WEEK 4: MODEL EVALUATION AND MODEL SELECTION

SUPERVISED & UNSUPERVISED LEARNING WITH TIME SERIES DATA

WEEK 5: CASE STUDY INTRODUCTION PART II, UNSUPERVISED LEARNING TECHNIQUES
WEEK 6: FEATURE ENGINEERING PART II
WEEK 7: DATA VISUALIZATION, DIMENSIONALITY REDUCTION
WEEK 8: CLOUD AND CONTAINERS

SUPERVISED & UNSUPERVISED LEARNING WITH TEXT AND IMAGE DATA

WEEK 9: CASE STUDY INTRODUCTION PART III, DEEP LEARNING TECHNIQUES PART 1
WEEK 10: DEEP LEARNING TECHNIQUES PART 2
WEEK 11: BIG DATA TOOLS
WEEK 12: MODEL MAINTENANCE, DEVOPS

• Do I need any programming experience to attend?
  Yes – you will need previous experience in Python language. You will also receive a Personalized Learning Plan (PLP) to guide you through the 12-lessons while applying the concepts through project-based work.
• What will I be doing?
  In 12-weeks you will learn the advanced topics of data science, Supervised and Unsupervised learning techniques applied to Unstructured Text and Time-series data both locally and on big data clusters.
• What age is this for?
  This course is suitable for Data Scientists, Big Data Engineers, and Technology Consultants.
• Do I need a computer?
  It is best if you bring your own laptop with 16GB RAM and 100-150GB free space on your hard drive.
• What’s the experience like?
  The course is for those that want to learn the apply advanced Data science frameworks via projects with guidance from a mentor. Student meets a mentor once a week for two hours. Our mentors are always there to help you if you get stuck.