Data Science

Data Science course consists of two semester (Fall, Spring) classes. Each class introduces related theoretical concepts and provides a hands on assignment including programming projects.
Audience
All are welcome including post high school students.
Eligibility
Familiarity with a programming language (Python, etc.). Strong general math skills.
Goal
Gain understanding of fundamental concepts. Acquire skills to apply relevant theories and models in practical situations to solve data problems.
Weekly classes are two semester long courses taken in the Fall and Spring terms. Each course is 12-weeks of 2 hours class time. Full urriculum is covered when both courses are taken. There are two weeks where students take exams and solutions are reviewed for practice.
The topics are as follows:

1) Introduction to Data Science

     AI: Artificial Intelligence

     Data Scientist

     Big Data

     Languages

     Trends

     Tools

     Lab: Introduction to iPython​

 2) Models

     Machine Learning

     Modeling principles

     Under-fitting, over-fitting

     Lab: Model Complexity

 3) Data Science Process

     Data Types

     Data Manipulations (Cleaning, ETL, Reduction, Transformation, etc.)

     Encodings

     Lab: Data Analysis

 4) Visualizations

     Data Presentation

     Dishonest Charts

     Lab: Visualizations

 5) Probability & Statistics

     Famous Probability Problems

     Statistical Formulas

     Markov Models

     Hidden Markov Models

     Monte Carlo Methods

     Lab: Probability & Statistics

6) Statistical Distributions / Models

     Statistical Models

     Distributions (Bernoulli, Binomial, Poisson, etc.)

     Normal Distribution

     Beta Distribution

     Student’s t-Distribution

     Sampling Methods

     Bias-Variance Trade-off

     Statistical Significance

     Confidence Interval

     z-test

     Student’s t-test

     (Statistical) Power

     Lab: Statistical Distributions

 7) Linear Models

     Linear Regression

     Lasso Regression

     L1 & L2 Regularization

     Logistic Regression

     Lab: Linear Models

 8) Dimensionality Reduction

     Curse of Dimensionality

     PCA: Principal Component Analysis

     SVD: Singular Value Decomposition

     t-SNE: t-Distributed Stochastic Neighbor Embedding

     Lab: Dimensionality Reduction

 9) Supervised Learning

     Perceptron

     kNN: k-Nearest Neighbors

     SVM: Support Vector Machines

     Multi-Class Classification

     Decision Tree (Entropy, Impurity, Information Gain, etc)

     Mutual Information

     MIC: Maximal Information Coefficient

     Importance, Relevance and Error Measures

         - Confusion Matrix, ROC Curve, AUC, Precision & Recall, F-score, tf-idf, etc.

     A/B testing

     Lab: Supervised Learning

The topics are as follows:

10) Time Series Analysis

     Lab: Time Series Analysis

 

11) Bayesian Methods

     Bayes' Theorem

     Bayesian Reasoning

     Naive Bayes Classifier

     Multi-Armed Bandit

     Lab: Bayesian Methods

 

12) Unsupervised Learning

      k-Means Clustering

      Expectation Maximization

      Lab: k-Means Clustering

 

13) Ensemble Methods

      Bootstrapping

      Bagging

      Boosting

      Random Forest

      Unbalanced Classes

      Lab: Ensemble Methods

 

14) Interesting Laws, Paradoxes and Models

      Benford’s Law

      Power Law

      Class Size Paradox

      Small World Models

      Community Detection Algorithms

      Lab: Interesting Laws, Paradoxes and Models

 

15) Deep Learning

      Loss/Error Functions

      SGD: Stochastic Gradient Descent

      FFN: Feed Forward Neural Networks

      CNN: Convolutional Neural Networks

      RNN: Recurrent Neural Networks

      LSTM

      Auto-Encoders

      GAN: Generative Adversarial Net(work)s

      Data Augmentation

      Dropout-Regularization

      Transfer Learning

      Tensorflow

      Keras

      Lab: Deep Learning

 

16) Advanced Topics

      Recommender Systems

      Market Basket Analysis

      Natural Language Processing (NLP)

      Reinforcement Learning (RL)

      Algorithm Complexity

      Big Data

      Map-Reduce

      Cloud (IBM Bluemix, Microsoft Azure, AWS, Google Cloud, etc.)

      Lab: Hadoop, Spark

Placement
Please call us for free evaluation and placement.
Registration
Early bird rates, discounts, and coupons will be available at the checkout screen.
Course Details
Tuition
$800.00
Schedule Mondays, 5:00 PM - 7:00 PM
Dates Oct. 08 - Jan. 14
Location