Data Science Bootcamp

Python-Based • Challenging and Rewarding • In-Demand Skills

April 3rd - September 1st
4 modules. 23 weeks. Monday, Wednesday, Thursday.
6:30 pm - 9:30 pm

The Python Data Science Bootcamp is an intensive project-based course tailored for new and established engineers who want to deepen their existing knowledge to specialize in data science and machine learning.

Students may sign up for individual modules, sign up for one at a time or sign up for all four modules and save!

Modules

Module 1
Python Data Ecosystem for Data Science

April 3rd. 5 Weeks
Prerequisite: Basic Python Programming Language Proficiency

Technology: SQL programming, MySQL database design and implementation, MySQL Connector/Python APIs, Pandas DataFrames, Regular Expressions, BeautifulSoup, lxml, Requests, and more.

  • Week 1:
    Data science project organization, SQL programing using MySQL database, CRUD database operations using MySQL Connector/Python APIs, etc.

  • Week 2:
    Pandas: easy-to-use and high-performance data structures in Python. Selecting, aggregating, and merging datasets. Selecting data from MySQL database, CSV and data-formatted files, etc.

  • Week 3:
    Data cleansing and transformation using Pandas DataFrames, quick-simple data visualization.

  • Week 4:
    Web data scraping and manipulation using Pandas DataFrames, Regular Expressions, BeautifulSoup, lxml, Requests, etc.

  • Week 5
    Instructor will help with student capstones. – students will use class time to work on capstones and get help from instructor as needed.

Module 2
Business Statistics and Analytical Thinking for Data Science

May 8th. 6 Weeks
Prerequisite: Module 1 or equivalent skill level

Technology: Data definitions and presentation, data format and types. Frequency Distribution Analysis, Numerical Descriptive Statistics, Normal Distribution Analysis, Hypothesis testing, Analysis of Variances, Bayesian methods for data modeling, Chi-Squared test of independence, Regression Analysis, Time Series, and more.

  • Week 1:
    Data definitions and presentation, data format and types, etc.

  • Week 2:
    Numerical Descriptive Statistics using Pandas DataFrames, Normal Distribution, etc.

  • Week 3:
    Hypothesis testing, one and two sample tests, Analysis of Variances (ANOVA), etc.

  • Week 4:
    Bayesian methods for data modeling, Chi-Squared test of independence, etc.

  • Week 5:
    Regression Analysis, linear and nonlinear regression, Analytical thinking for Data Science: how to think about data problems, form concrete problem statements, critical thinking skills, and asking the right questions of data, etc.

  • Week 6
    Instructor will help with student capstones. – students will use class time to work on capstones and get help from instructor as needed.

Module 3
Data Visualization and Interpretation

June 12th. 4 Weeks
Prerequisite: Modules 1 and 2 or equivalent skill level

Technology: Pandas DataFrames for quick data visualization, matplotlib 2D/3D plotting library, seaborn statistical graphics library, and more.

  • Week 1:
    Data visualization using Pandas DataFrames.

  • Week 2:
    Data visualization using matplotlib 2D plotting library.

  • Week 3:
    Data visualization using seaborn statistical graphics library.

  • Week 4:
    Instructor will help with student capstones. – students will use class time to work on capstones and get help from instructor as needed.

Module 4
Machine Learning

July 10. 8 Weeks
Prerequisite: Modules 1, 2 and 3 or equivalent skill level

Technology: Machine Learning definitions, algorithms and classification and scikit-learn library. Data preprocessing, manipulation and cleansing using Pandas DataFrames, Linear and Logistic Regression Analysis. Decision trees and Random Forest. Artificial Neural Networks, Deep learning for image processing, and more.

  • Week 1:
    Machine Learning definitions, algorithms and classification. scikit-learn library, etc.

  • Week 2:
    Data preprocessing, manipulation and cleansing.

  • Week 3:
    Linear and Logistic regression models

  • Week 4:
    Data classification, k-nearest neighbors and support vector machine.

  • Week 5:
    Clustering analysis for unlabeled data.

  • Week 6:
    Decision trees and Random Forest.

  • Week 7:
    Artificial neural networks.

  • Week 8:
    Deep learning.

  • Week 9:
    Instructor will help with student capstones. – students will use class time to work on capstones and get help from instructor as needed.

Post Bootcamp

  • Capstone Demo Day Party

Pre-bootcamp

Students have the freedom to use any Python Integrated Development Environment (IDE) or other editor tools based on experience. Students will be expected to have all software installed before 1st day of bootcamp. Staff will be available the Friday before bootcamp to help any students who need assistance.

Instructor

Ernest Bonat, Ph.D.
Senior Software Engineer | Senior Data Scientist
15 IT Resources, LLC | IT Staffing and Consulting Services

Tuition

Pay-as-you-go Full Bootcamp: $9192.00

Sign up for the Full Bootcamp and save 10%:
Full Bootcamp up-front: $8272.80

Sign up for the Full Bootcamp by March 19 and Save 20%!
Save $1,838.00 - Register and pay by March 19th and your tuition will be only $7,354.00!

À la carte tuition:

  • module 1
    $2087.00

  • module 2
    $2504.00

  • module 3
    $1697.00

  • module 4
    $3339.00

Prerequisites

Incoming students are expected to have basic python proficiency or at least one of the following:

  • Experience writing code in another language
  • Graduation from the Python Full Stack Developer Bootcamp
  • and/or
  • Pass a proficiency exam

Where Our Graduates Work