Data Science Immersive

12-week program starting on Sept 11, 2017 Email to Download SyllabusApply for the Immersive

The Data Science Immersive Program  a 12 weeks immersive bootcamp program.

Our (approved) Data Science immersive program is your direct path to a career as a Data Scientist. This 480-hour immersive curriculum includes eight courses, two weeks of project capstone, interview preparation and white boarding skills, extensive hands-on skill building, and guided product training.

Data Science Immersive students graduate with the sought after knowledge and trade-craft for immediate employment as Machine Learning Engineers, Business Analysts, Data Analysts, Product Managers, and Consultants.

Data Science Foundation - Data Wrangling and Exploratory Data Analysis

Master cleanup of datasets using Python language and Pandas library, exploratory data analysis to generate hypotheses and intuition, and communication of results through visualization, stories, and summaries.

Statistical Modeling and Inference

Develop approaches to performing inference, and acceptance of results; master concepts in causal inference and motivate the need for experiments; apply statistical tools to help plan experiments: exploratory analysis, power calculations, and the use of simulation; apply statistical methods to estimate causal quantities of interest and construct appropriate confidence intervals.

Supervised Learning I - Regression and Classification

Develop a modeling life-cycle – from specification, fit, and accuracy thru reliability; apply feature selection methods, finding “optimal” model parameters based on data; master Linear Regression – Bias-variance Trade-off, and Logistic Regression including multi-class modeling (Multinomial, Bernoulli, and Gaussian).

Supervised Learning II and Natural Language Processing

Apply visualization of model performance under various kinds of uncertainty; further consideration of what is desired from data mining results using Decision Trees, Random Forests, and Ensembles; Implement Natural Language Processing (NLP) processes into projects and software applications; Programmatically extract data stored in common formats; critically assess options for cleaning data in different contexts; store, retrieve, and analyze data using NoSQL databases

Unsupervised Learning

Continue to apply feature selection methods such as – Filtering and wrapping algorithms; master unsupervised methods in predictive analytics, in network and text analytics; apply Dimension reduction of predictor space and Graphing analysis algorithms for clustering (community detection in graph networks)

Working at Scale

Use Hadoop ecosystem for Pre-processing; and then apply Exploratory Data Analysis and Predictive Modeling; develop Mappers, Reducers and jobs using Hive, Sqoop, and Pig scripting; master Hadoop data workflows and jobs with Python; read and write data to HDFS; and apply the next generation framework i.e. Spark (in-memory), for Filtering, Aggregating and Searching.

Supervised Learning II and Natural Language Processing

Describe loading and saving models to plot intermediate results for supervised optimization models for Deep learning using Keras, and H20 Deep Water; apply Feed-forward neural net trained with backpropagation; Data Visualization using Tableau.

Recommendation Systems and Forecasting

Implement recommenders from scratch and use software libraries and tools to implement more advanced recommenders; develop REST API for predictive models; deploy models into production using various methods including Predictive Modeling Markup Language (PMML), develop web applications that consume predictive models, understand Platform-as-a-service offerings to deploy web applications, review additional uses cases such as Anomaly Detection, Customer Churn, and Time series Forecasting.

Capstone Project I

This module integrates DATA SCIENCE skills through an application to a project focusing on real-world open data. The course serves as the capstone of the student’s 8-weeks of learning. The student works alone with support from staff to tailor the data science process steps to develop a minimum viable data product within two weeks. The student will be evaluated on their problem hypothesis, statistical model, insights delivered through use of the model, flexibility of the model including bias and variance, and communication of the end-to-end approach through an oral presentation.

Capstone Project 2

This module integrates Data Science skills through an application to a project focusing on real-world open data. The course serves as the capstone of the student’s 8-weeks of learning. The student works alone with support from staff to tailor the data science process steps to develop a minimum viable data product within two weeks. The student will be evaluated on their problem hypothesis, statistical model, insights delivered through use of the model, flexibility of the model including bias and variance, and communication of the end-to-end approach through an oral presentation. In addition, students will develop an effective interviewing skills white boarding, connecting technical options to the business problems to solve, and answering questions succinctly.

Project Demonstration - Employment Skills

During Employment Skills students will develop an effective LinkedIn Profile, Showcase Project portfolio, prepare for interviews by revisiting their Toy Problems, share their Capstone project results.

Business Use Cases and Whitboarding - Employment Skills

During Employment Skills students will develop an effective interviewing skills white boarding, connecting technical options to the business problems to solve, and answering questions succinctly.

MODULES

HOURS OF INSTRUCTION

HOURS OF LABS

Full Time Schedule

The Data Science Immersive is available only as full time program. All upcoming cohort start dates can be found on the Academy Calendar.

Full Time Schedule

THE CORE FULL TIME PROGRAM MEETS M-F OVER 10 WEEKS AND INCLUDES 200 HRS. OF THEORY AND 200 HRS. OF HANDS-ON LABS.

Tuition and Assistance

The Academy supports a full range of Financial Aid, Scholarship, Education Loan and local Grant Programs for CORE students. Follow the link below for details on each option.

CLICK FOR TUITION AND ASSISTANCE INFO

Job Placement Assistance

The Academy supports a full range of Financial Aid, Scholarship, Education Loan and local Grant Programs for CORE students. Follow the link below for details on each option.

CLICK TO VIEW OUR HIRING PARTNERS

Admissions Process

The Academy supports a full range of Financial Aid, Scholarship, Education Loan and local Grant Programs for CORE students. Follow the link below for details on each option.

CLICK TO VIEW OUR ADMISSIONS PROCESS

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Are You Ready?

The following are recommended characteristics for Data Science Immersive applicants. None of these is absolute and we encourage all potential students with general alignment to these criteria to apply for our programs.

We encourage all potential students with general alignment to these criteria to apply for our programs.

The Data Science is more technical than any of our other programs.

Personal Attributes

  • Python programming expected
  • Natural interest in analytics
  • Determined to excel at what you do
  • Curiosity

Programming Experience

Education

  • Technical and analytical ability trump formal education
  • AS, BS or MS in: Comp Sci, Math, EE or related

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Please contact us at hello@divergence.academy or call (214)-997-6100 for additional information about the course.