Data Science Immersive

12-week program from Jan 16 - Mar 24, 2017. Enroll by Dec 16, 2016. Email to Download SyllabusApply for Immersive Program

QUICK INFO

DURATION: 12-weeks (50 hours/week)
PRICING: $13,300

WEEK 1 | MODULE ONE

DATA SCIENCE FOUNDATIONS, DATA WRANGLING AND EXPLORATORY DATA ANALYSIS

OUTCOMES

Students will learn to setup the process of Data science through:

  • Cleanup of datasets using Python language and Pandas library
  • Exploratory data analysis to generate hypotheses and intuition
  • Communication of results through visualization, stories, and summaries
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FUNDAMENTAL CONCEPTS

  • Version control – Fork repository, push & pull code
  • Pair programming and Test Driven Development
  • Data analysis – types of statistics and analytical methods and their relationship
  • Where and how to acquire data, methods for evaluating source data, and data transformation and preparation
  • Use Python’s Requests package to obtain data from web pages
  • Use Python’s Beautiful Soup to parse the content of a web page to find useful data for subsequent analysis

EXEMPLARY TECHNIQUES

  • Python, Pandas, GitHub, UNIX Bash scripts, SQL
  • Optional – coverage of contemporary Web scraping and Data wrangling tools.
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PROJECT 1

AMAZON RECOMMENDER

In thefirst week, students work in small groups using Amazon Reviews dataset to apply the Exploratory Data Analysis, Data Wrangling and basic Feature Engineering concepts to answer a few sentiment analysis questions from the product review data for a product category of student’s choice.

WEEK 2 | MODULE TWO

STATISTICAL MODELING FOR INFERENCE

OUTCOMES

Students will learn to draw conclusions based on data. Upon completion of this module, students will be able to describe:

  • Approaches to performing inference, and acceptance of results
  • Concepts in causal inference and motivate the need for experiments
  • Statistical tools to help plan experiments: exploratory analysis, power calculations, and the use of simulation
  • Statistical methods to estimate causal quantities of interest and construct appropriate confidence intervals
  • Scalable methods suitable for “big data”, including working with weighted data and clustered bootstrapping

Students will also be able to:

  • Design, plan, implement, and analyze online experiments using contemporary tools
  • Implementation of basic “A/B tests”, within-subjects designs and sophisticated experiments
  • Make and interpret predictions from a Bayesian perspective.
  • Understand the Explore-Exploit strategies related to Multi-armed Bandits
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FUNDAMENTAL CONCEPTS

  • Contexts in which inference is desirable
  • Modeling for Inference vs Modeling for Prediction
  • Key statistics concepts – Distributions, Sampling, Confidence Intervals, Hypothesis Testing
  • Statistical model selection
  • Applied Probability for Statistical Inference
  • Understand the cycle: model, apply, predict, setup experiments and observe

EXEMPLARY TECHNIQUES

  • Python packages – NumPy, SciPy, PyMC
  • Optional – coverage of contemporary A/B Testing tools.
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PROJECT 2

MULTI-ARMED BANDITS

Multi-armed bandit approach to Internet display advertising to maximize sales; or find the best treatment out of many possible treatments while minimizing losses.

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