Data Science Mini Courses

part-time 24 WEEKS

Evenings, 2x per week

Start: April 25, 2017

Location: New York

295 Madison Ave., 35 FL

Our data science courses are available in flexible, part-time formats for those who want to learn specialized topics, or only get their feet wet rather than take our full bootcamp. Students can sign-up for our mini-courses in 12, 18, and 24-hour formats. Courses are organized by level with 100 being beginner levels.

Curriculum is designed to prepare students for jobs such as entry-level data scientists or data analysts, or in data science related roles. Women and veterans receive 20% of all “mini-courses.”


Data Sci 210: Natural Language Processing with Data Science

Natural Language Processing is a rapidly growing field because of its application across all disciplines. In this 3 week course, learn about the basics of text analysis, regular expressions, different topic models, and much more. This course is designed to give you a thorough understanding of the field.

Topics Include:

  • Regular Expressions
  • Parts of Speech Tagging
  • Bag of Words Models
  • Stemmers, Stop Words, Lemmatization
  • Sentiment Analysis
  • WordNets
  • Word2vec & Tensorflow
  • Chunking & Named Entity Extraction
  • Sentence Structure Analysis
  • Nltk & glove

Pre-Requisites: Fluency in Python is recommended.  A basic statistics background is preferred

Prerequisite: Data Sci 101 or Data Sci 109, and Data Sci 203

Start Date: February 21st, 2017

Schedule: Tuesday & Friday, 6:00 pm – 9:00 pm (on holidays classes will be held the following day)

Total In-Class Hours: 18

Register

 


Data Sci 209: Fundamentals of Machine Learning and Regression Analysis

Machine Learning and Prediction Analysis are at the heart of many leading technology companies, including Google, Facebook and Amazon. Over three weeks, you’ll learn the fundamentals of machine learning and further your knowledge of the field with Regression Analysis. A proficiency in either Python or R and at least a basic statistics background is required.

 Topics include:

  • Intro to ML (supervised vs unsupervised, training vs test data, etc)
  • Fitting
  • Covariance & Correlation Coefficient
  • Simple Linear Regression
  • Hypothesis Testing
  • Parameter Estimation
  • Confidence Intervals
  • Prediction Analysis
  • Multiple Linear Regression
  • Weighted Least Squares
  • Correlation Errors
  • Logistic Regression

Prerequisite: Data Sci 101 or Data Sci 109

Start Date: February 22nd, 2017

Schedule: Monday & Thursday, 6:00 pm – 9:00 pm (on holidays classes will be held the following day)

Total In-Class Hours: 18

Register

 


Data Sci 309: Machine Learning with Data Science

Join Byte Academy for a course in the exciting field of Machine Learning. In this course, learn about different prediction models, such as Support Vector Machines, Decision Trees, Clustering, Nearest Neighbors, and so much more. We’ll be using Python for implementation.

Topics include:

  • Intro to ML Fundamentals
  • Naive Bayes
  • Support Vector Machines
  • Decision Trees
  • Clustering
  • Maximum Likelihood Estimation
  • AdaBoost
  • Optimization
  • Nearest Neighbors
  • Regularization
  • Dimension Reduction
  • Reinforcement Learning

Requires: Fluency in Python and a solid statistics background.

Recommended Prerequisite: Data Sci 203 and  Data Sci 209 and Data Sci 109 and/or Data Sci 101

Start Date: March 13th, 2017

Schedule: Monday & Thursday, 6:00 pm – 9:00 pm (on holidays classes will be held the following day)

Total In-Class Hours: 24

Register

 


Data Sci 303: Databases & Big Data

To work with data, we must be able to store that data. Join us for a course on one of the most lucrative components of Data Science: Big Data & Databases.

Topics Include:

  • Big Data fundamentals
  • SQL, NoSQL, MySQL
  • Hadoop & Apache Spark
  • AWS

Requires: Programming experience required. General Computer Science fluency preferred.

Recommended Prerequisite: Data Sci 203 and  Data Sci 109 and/or Data Sci 101

Start Date: March 14th, 2017

Schedule: Tuesday & Friday, 6:00 pm – 9:00 pm (on holidays classes will be held the following day)

Total In-Class Hours: 24

Register

 


Data Sci 403: Deep Learning with Data Science

Deep Learning is a sub-field of Machine Learning. In this course, we’ll cover the mathematical skills required to understand the material, as well as the fundamentals of Neural Networks and different deep learning models.

Topics include:

  • Select Calculus, Linear Algebra, and Statistics topics
  • Neural Networks
  • Recurrent Neural Networks
  • Convolution Neural Networks
  • Feedforward Neural Networks
  • LTST Neural Networks

 

Requires: Programming experience required. Solid background in Statistics & Calculus.

Recommended Prerequisite: Data Sci 309 and  Data Sci 203 and Data Sci 109 and/or Data Sci 101

Start Date: April 10th, 2017

Schedule: Monday & Thursday, 6:00 pm – 9:00 pm (on holidays classes will be held the following day)

Total In-Class Hours: 12

Register

 


Data Sci 500: Projects

In this last segment of the Data Science Mini-Course series, we will build final projects with all that we’ve learned!

 

Requires: Programming experience required. Solid background in Statistics & Calculus.

Recommended Prerequisite: Data Sci 403 and Data Sci 309 and  Data Sci 203 and Data Sci 109 and/or Data Sci 101

Start Date: April 11th, 2017

Schedule: Tuesday and Friday, 6:00 pm – 9:00 pm (on holidays classes will be held the following day)

Total In-Class Hours: 12

Register


Python 101: Python for Data Science

Python is a prominent programming language for data science due to its strong analytical capability and speed. It’s also a great first language, which is why our very first mini-course is an introduction to Python and its data science data types. Proficiency in Python is required for the rest of our courses, so this Python 101 course will develop your Python skills so you can hit the ground running.

Start Date: April 25, 2017

Schedule: Tuesday & Friday, 6:00 pm – 9:00 pm (on holidays classes will be held the following day)

Total In-Class Hours: 18

Register

 


Data Sci 109: Intro to Data Science & Statistics using R

Along with Python, R is an important knowledge for Data Science, especially for those interested in statistical analysis. Join us for a three-week course in which we introduce you to Data Science and Statistics using R. No prior experience is necessary.

Topics include:

  • Syntax & Control Flow
  • Lists, Vectors, & DataFrames
  • Reading & Writing Files
  • Data Science Fundamentals
  • Intro Probability & Statistics
  • Hypothesis Testing
  • T-Tests & Estimation
  • Correlation & Chi-Squared Tests

Start Date: April 24, 2017
Schedule: Monday & Thursday, 6:00 pm – 9:00 pm (on holidays classes will be held the following day)

Total In-Class Hours: 18

 

Register

 


Data Sci 201: Data Visualization with Python

Data Visualization is at the core of many Data Science projects. From ggplot to D3 to seaborn, Data Visualization in Python is an incredibly important skill-set to have for data science in all industries. In this course, learn the intricacies of learning how to plot different datasets. Proficiency in Python and preferably experience with Data Fundamentals is recommended.

Topics include:

  • Matplotlib
  • Seaborn
  • Bokeh
  • Ggplot
  • D3
Prerequisite: Data Sci 101 or Data Sci 109

Start Date: May 15, 2017

Schedule: Monday & Thursday, 6:00 pm – 9:00 pm (on holidays classes will be held the following day)

Total In-Class Hours: 12

Register

 


Data Sci 203: Data Preparation Using Python & R

Data Scientists spend the majority of their time setting their data up for analysis. This course focuses on the methodology behind data acquisition, preparation, and cleaning. Proficiency with either Python or R is required.

Topics include:

  • Data Science Fundamentals
  • Web scraping
  • DataFrames: Pandas & Dplyr
  • APIs
  • Missing Values & Errors
  • Merging Data
  • String manipulation
  • Data types
 Prerequisite: Data Sci 101 or Data Sci 109

Start Date: May 16, 2017

Schedule: Tuesday & Friday, 6:00 pm – 9:00 pm (on holidays classes will be held the following day)

Total In-Class Hours: 12