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Practical Data Science Cookbook

  • Course Code: Data Science - Practical Data Science Cookbook
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 3 Days Audience: This course is geared for those who wants to complete real-world data science projects in R and Python

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Practical Data Science Cookbook skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to complete real-world data science projects in R and Python 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don’t. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this course covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each lesson, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python. 

Working in a hands-on learning environment, led by our Practical Data Science Cookbook expert instructor, students will learn about and explore: 

  • Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data 
  • Get beyond the theory and implement real-world projects in data science using R and Python 
  • Easy-to-follow recipes will help you understand and implement the numerical computing concepts 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Learn and understand the installation procedure and environment required for R and Python on various platforms 
  • Prepare data for analysis by implement various data science concepts such as acquisition, cleaning and munging through R and Python 
  • Build a predictive model and an exploratory model 
  • Analyze the results of your model and create reports on the acquired data 
  • Build various tree-based methods and Build random forest 

Audience & Pre-Requisites 

This course is designed for beginners who wants to complete real-world data science projects in R and Python 

Pre-Requisites:  Students should have familiar with  

  • Basics of Python  
  • Knowledge of Python is assumed. 

Course Agenda / Topics 

  1. Preparing Your Data Science Environment 
  • Preparing Your Data Science Environment 
  • Understanding the data science pipeline 
  • Installing R on Windows, Mac OS X, and Linux 
  • Installing libraries in R and RStudio 
  • Installing Python on Linux and Mac OS X 
  • Installing Python on Windows 
  • Installing the Python data stack on Mac OS X and Linux 
  • Installing extra Python packages 
  • Installing and using virtualenv 
  1. Driving Visual Analysis with Automobile Data with R 
  • Driving Visual Analysis with Automobile Data with R 
  • Introduction 
  • Acquiring automobile fuel efficiency data 
  • Preparing R for your first project 
  • Importing automobile fuel efficiency data into R 
  • Exploring and describing fuel efficiency data 
  • Analyzing automobile fuel efficiency over time 
  • Investigating the makes and models of automobiles 
  1. Creating Application-Oriented Analyses Using Tax Data and Python 
  • Creating Application-Oriented Analyses Using Tax Data and Python 
  • Introduction 
  • Preparing for the analysis of top incomes 
  • Importing and exploring the world’s top incomes dataset 
  • Analyzing and visualizing the top income data of the US 
  • Furthering the analysis of the top income groups of the US 
  • Reporting with Jinja2 
  • Repeating the analysis in R 
  1. Modeling Stock Market Data 
  • Modeling Stock Market Data 
  • Introduction 
  • Acquiring stock market data 
  • Summarizing the data 
  • Cleaning and exploring the data 
  • Generating relative valuations 
  • Screening stocks and analyzing historical prices 
  1. Visually Exploring Employment Data 
  • Visually Exploring Employment Data 
  • Introduction 
  • Preparing for analysis 
  • Importing employment data into R 
  • Exploring the employment data 
  • Obtaining and merging additional data 
  • Adding geographical information 
  • Extracting state- and county-level wage and employment information 
  • Visualizing geographical distributions of pay 
  • Exploring where the jobs are, by industry 
  • Animating maps for a geospatial time series 
  • Benchmarking performance for some common tasks 
  1. Driving Visual Analyses with Automobile Data 
  • Driving Visual Analyses with Automobile Data 
  • Introduction 
  • Getting started with IPython 
  • Exploring Jupyter Notebook 
  • Preparing to analyze automobile fuel efficiencies 
  • Exploring and describing fuel efficiency data with Python 
  • Analyzing automobile fuel efficiency over time with Python 
  • Investigating the makes and models of automobiles with Python 
  1. Working with Social Graphs 
  • Working with Social Graphs 
  • Introduction 
  • Preparing to work with social networks in Python 
  • Importing networks 
  • Exploring subgraphs within a heroic network 
  • Finding strong ties 
  • Finding key players 
  • Exploring the characteristics of entire networks 
  • Clustering and community detection in social networks 
  • Visualizing graphs 
  • Social networks in R 
  1. Recommending Movies at Scale (Python) 
  • Recommending Movies at Scale (Python) 
  • Introduction 
  • Modeling preference expressions 
  • Understanding the data 
  • Ingesting the movie review data 
  • Finding the highest-scoring movies 
  • Improving the movie-rating system 
  • Measuring the distance between users in the preference space 
  • Computing the correlation between users 
  • Finding the best critic for a user 
  • Predicting movie ratings for users 
  • Collaboratively filtering item by item 
  • Building a non-negative matrix factorization model 
  • Loading the entire dataset into the memory 
  • Dumping the SVD-based model to the disk 
  • Training the SVD-based model 
  • Testing the SVD-based model 
  1. Harvesting and Geolocating Twitter Data (Python) 
  • Harvesting and Geolocating Twitter Data (Python) 
  • Introduction 
  • Creating a Twitter application 
  • Understanding the Twitter API v1.1 
  • Determining your Twitter followers and friends 
  • Pulling Twitter user profiles 
  • Making requests without running afoul of Twitter’s rate limits 
  • Storing JSON data to disk 
  • Setting up MongoDB for storing Twitter data 
  • Storing user profiles in MongoDB using PyMongo 
  • Exploring the geographic information available in profiles 
  • Plotting geospatial data in Python 
  1. Forecasting New Zealand Overseas Visitors 
  • Forecasting New Zealand Overseas Visitors 
  • Introduction 
  • The ts object 
  • Visualizing time series data 
  • Simple linear regression models 
  • ACF and PACF 
  • ARIMA models 
  • Accuracy measurements 
  • Fitting seasonal ARIMA models 
  1. German Credit Data Analysis 
  • German Credit Data Analysis 
  • Introduction 
  • Simple data transformations 
  • Visualizing categorical data 
  • Discriminant analysis 
  • Dividing the data and the ROC 
  • Fitting the logistic regression model 
  • Decision trees and rules 
  • Decision tree for german data 

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