Applied Data Science Workshop (ADS)

A comprehensive 8-day training program for hands-on introduction to big data, data science, and machine learning models, methods and algorithms.

The workshop will take participants through the conceptual and applied foundations of the subject. Topics covered include:

• Data Science techniques, models, methods and best practices.
• Machine Learning theory, types of learning, and models
• Practical examples of applying most frequently used Machine Learning industry models

Labs are developed to practically learn how to use the R and Python programming languages and packages for applying the main concepts and techniques of data science and machine learning.

The Workshop will provide participants with an applied introduction to data science industry practices and models of machine learning. The workshop has a strong focus on gaining hands-on experience implementing algorithms and building predictive models on real datasets. By the end of the workshop, participants will be ready to implement the machine learning algorithms using data science on their own data, and immediately generate business value.

workshop Course outline

Module 1: Introduction to the course

• Recap of Data Science Fundamentas
• Comparing R and Python: read this Infoworld article.

Session Outline related to R Basics

Used Resources during session

• Quick overview to data science fundemntals workshop

• getting started with R

• R Basics

  • Variables
  • Data Types
  • Vectors
  • Vector operations
  • Vector indexing and slicing
  • Getting help with R
  • Comparision operators

• Matrices in R

  • Creating a matrix
  • matrix arithmetic
  • matrix operation
  • matrix selection and indexing
  • Factor and catagorical matrices

• Data frame in R

  • Data frame basics
  • Data frame indexing and selection
  • Data frame operations

• R lists basics

• Data Input and Output

  • CSV files in R
  • Excel files in R
  • Web Scraping in R

• Programming in R

  • logical operators
  • Conditional statments
  • loops in R, for loop, while loops
  • Functions

• Advanced R programming

  • Builtin R features
  • Apply
  • Math functions with R
  • Regular Expressions in R
  • Date and timestamp

• Data Minipulation in R

  • Guide to use dplyr
  • Guide to use tidyr

• Data Visualization in R

  • Overview of ggplot2
  • Histograms
  • Scatterplot
  • Barplots
  • Box plots
  • 2 variable plotting
  • coordinates and faceting
  • themes

• Exercises & Solutions

  • R Basics
  • R Matrix
  • R Dataframes
  • R Conditional statments
  • R Functions
  • R visualization
  • Data Manipulation using R

Module 2: Manchine learning algorithms and techniques

• linear regression

  • Theory
  • Math student prediction final grade exam Project (Practice)

• logistic regression

  • Theory
  • classify person "deceased or not" using titanic data Project (Practice)

• Assignment: Titanic Data Set

For this assignment you will be working with the Titanic Data Set from Kaggle. This is a very famous data set and very often is a student's first step in machine learning!

you will be trying to predict a classification- survival or deceased by using python this time. you need to do some additional cleaning in able to use the kaggle data in your learning model.

after your finish upload your work in the notebook format into your github account

Module 3 & 4: Supervised learning

• Module 3 Supervised learning methods (KNN, Naive Bayes)

• Theory behind the two methods and when they are used?
  • KNN
  • Naive Bayes
• Project assignment Solution: KNN Project Data

• Module 4: Supervised learning methods (Decision Trees and Random Forests)

  • Theory
  • Two Project example on Random forests used as a classifier and in regression
  • Project Assignment: lending club solution

• Cross Validation

Capstone Project 1

In this project we will be learning how do we combine everything we learn about your R and Python programming skills and your data visualization, data engineering (dataframe operations) and apply machine learning methods in order to solve a historical data problem.and in this case we will explore a local data taken from civil engineering department at An-Najah National University (ANNU) statics record.xlsx

Module 5- Special Topics: Natural language processing (NLP)

• What is NLP?
• spam filter using python: introducing the NLTK package,spam dataset
• Project Portfolio Solution:Yelp review dataset, yelp dataset

Module 6: Support Victor Machine (SVM) & Principle Component Analysis

• SVM in theory?
• practice example on how using SVM in Python
• SVM Classifier Project :Breast Cancer dataset
• PCA Classifier Project:Breast Cancer dataset

Module 7: Neural Networks and deep learning

Deep Learning with Tensorflow and Keras

• Intro to Deep Learning and ANN

• getting started with tensorflow

• MNIST Project: feedforward neural network in TF

• getting started with keras

• Keras hellow world: Iris data

• MNIST Project: using Keras Interface

• Convoluation Neural Network

  • Introduction to CNN
  • MNIST Project using CNN

• Installing Tensorflow

• After installing Tensorflow type pip install keras to install Keras

Module 8: Data Science ethics