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Data Sciences

Practical Data Science Cookbook

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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.

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Course Access

Unlimited Duration

Last Updated

July 29, 2021

Students Enrolled

20

Total Reviews

Posted by

Ernesto Lee

Certification

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.

Course Curriculum

    • Preparing Your Data Science Environment 00:00:00
    • Understanding the data science pipeline 00:00:00
    • Installing R on Windows, Mac OS X, and Linux 00:00:00
    • Installing libraries in R and RStudio 00:00:00
    • Installing Python on Linux and Mac OS X 00:00:00
    • Installing Python on Windows 00:00:00
    • Installing the Python data stack on Mac OS X and Linux 00:00:00
    • Installing extra Python packages 00:00:00
    • Installing and using virtualenv 00:00:00
    • Driving Visual Analysis with Automobile Data with R 00:00:00
    • Introduction 00:00:00
    • Acquiring automobile fuel efficiency data 00:00:00
    • Preparing R for your first project 00:00:00
    • Importing automobile fuel efficiency data into R 00:00:00
    • Exploring and describing fuel efficiency data 00:00:00
    • Analyzing automobile fuel efficiency over time 00:00:00
    • Investigating the makes and models of automobiles 00:00:00
    • Creating Application-Oriented Analyses Using Tax Data and Python 00:00:00
    • Introduction 00:00:00
    • Preparing for the analysis of top incomes 00:00:00
    • Importing and exploring the world’s top incomes dataset 00:00:00
    • Analyzing and visualizing the top income data of the US 00:00:00
    • Furthering the analysis of the top income groups of the US 00:00:00
    • Reporting with Jinja2 00:00:00
    • Repeating the analysis in R 00:00:00
    • Modeling Stock Market Data 00:00:00
    • Introduction 00:00:00
    • Acquiring stock market data 00:00:00
    • Summarizing the data 00:00:00
    • Cleaning and exploring the data 00:00:00
    • Generating relative valuations 00:00:00
    • Screening stocks and analyzing historical prices 00:00:00
    • Visually Exploring Employment Data 00:00:00
    • Introduction 00:00:00
    • Preparing for analysis 00:00:00
    • Importing employment data into R 00:00:00
    • Exploring the employment data 00:00:00
    • Obtaining and merging additional data 00:00:00
    • Adding geographical information 00:00:00
    • Extracting state- and county-level wage and employment information 00:00:00
    • Visualizing geographical distributions of pay 00:00:00
    • Exploring where the jobs are, by industry 00:00:00
    • Animating maps for a geospatial time series 00:00:00
    • Benchmarking performance for some common tasks 00:00:00
    • Driving Visual Analyses with Automobile Data 00:00:00
    • Introduction 00:00:00
    • Getting started with IPython 00:00:00
    • Exploring Jupyter Notebook 00:00:00
    • Preparing to analyze automobile fuel efficiencies 00:00:00
    • Exploring and describing fuel efficiency data with Python 00:00:00
    • Analyzing automobile fuel efficiency over time with Python 00:00:00
    • Investigating the makes and models of automobiles with Python 00:00:00
    • Working with Social Graphs 00:00:00
    • Introduction 00:00:00
    • Preparing to work with social networks in Python 00:00:00
    • Importing networks 00:00:00
    • Exploring subgraphs within a heroic network 00:00:00
    • Finding strong ties 00:00:00
    • Finding key players 00:00:00
    • Exploring the characteristics of entire networks 00:00:00
    • Clustering and community detection in social networks 00:00:00
    • Visualizing graphs 00:00:00
    • Social networks in R 00:00:00
    • Recommending Movies at Scale (Python) 00:00:00
    • Introduction 00:00:00
    • Modeling preference expressions 00:00:00
    • Understanding the data 00:00:00
    • Ingesting the movie review data 00:00:00
    • Finding the highest-scoring movies 00:00:00
    • Improving the movie-rating system 00:00:00
    • Measuring the distance between users in the preference space 00:00:00
    • Computing the correlation between users 00:00:00
    • Finding the best critic for a user 00:00:00
    • Predicting movie ratings for users 00:00:00
    • Collaboratively filtering item by item 00:00:00
    • Building a non-negative matrix factorization model 00:00:00
    • Loading the entire dataset into the memory 00:00:00
    • Dumping the SVD-based model to the disk 00:00:00
    • Training the SVD-based model 00:00:00
    • Harvesting and Geolocating Twitter Data (Python) 00:00:00
    • Introduction 00:00:00
    • Creating a Twitter application 00:00:00
    • Understanding the Twitter API v1.1 00:00:00
    • Determining your Twitter followers and friends 00:00:00
    • Pulling Twitter user profiles 00:00:00
    • Making requests without running afoul of Twitter’s rate limits 00:00:00
    • Storing JSON data to disk 00:00:00
    • Setting up MongoDB for storing Twitter data 00:00:00
    • Storing user profiles in MongoDB using PyMongo 00:00:00
    • Exploring the geographic information available in profiles 00:00:00
    • Plotting geospatial data in Python 00:00:00
    • Forecasting New Zealand Overseas Visitors 00:00:00
    • Introduction 00:00:00
    • The ts object 00:00:00
    • Visualizing time series data 00:00:00
    • Simple linear regression models 00:00:00
    • ACF and PACF 00:00:00
    • ARIMA models 00:00:00
    • Accuracy measurements 00:00:00
    • Fitting seasonal ARIMA models 00:00:00
    • German Credit Data Analysis 00:00:00
    • Introduction 00:00:00
    • Simple data transformations 00:00:00
    • Visualizing categorical data 00:00:00
    • Discriminant analysis 00:00:00
    • Dividing the data and the ROC 00:00:00
    • Fitting the logistic regression model 00:00:00
    • Decision trees and rules 00:00:00
    • Decision tree for german data 00:00:00

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