Getting And Cleaning Data Course Project Code Book !!INSTALL!! Download

One of the most exciting areas in all of data science right now is wearable computing - see for example this article . Companies like Fitbit, Nike, and Jawbone Up are racing to develop the most advanced algorithms to attract new users. The data linked to from the course website represent data collected from the accelerometers from the Samsung Galaxy S smartphone.

The aim of the project is to clean and extract usable data from the above zip file. R script called run_analysis.R that does the following: - Merges the training and the test sets to create one data set. - Extracts only the measurements on the mean and standard deviation for each measurement. - Uses descriptive activity names to name the activities in the data set - Appropriately labels the data set with descriptive variable names. - From the data set in step 4, creates a second, independent tidy data set with the average of each variable for each activity and each subject.

Getting And Cleaning Data Course Project Code Book Download

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The purpose of this project is to demonstrate your ability to collect, work with, and clean a data set. The goal is to prepare tidy data that can be used for later analysis. You will be graded by your peers on a series of yes/no questions related to the project. You will be required to submit: 1) a tidy data set as described below, 2) a link to a Github repository with your script for performing the analysis, and 3) a code book that describes the variables, the data, and any transformations or work that you performed to clean up the data called CodeBook.md. You should also include a README.md in the repo with your scripts. This repo explains how all of the scripts work and how they are connected.

See the README.txt file for the detailed information on the dataset. For the purposes of this project, the files in the Inertial Signals folders are not used. The files that will be used to load data are listed as follows:

A significant part of your role as a data analyst is cleaning data to make it ready to analyze. Data cleaning (also called data scrubbing) is the process of removing incorrect and duplicate data, managing any holes in the data, and making sure the formatting of data is consistent.

Example exploratory data analysis project: This data analyst took an existing dataset on American universities in 2013 from Kaggle and used it to explore what makes students prefer one university over another.

2. Google Charts: This gallery of interactive charts and data visualization tools makes it easy to embed visualizations within your portfolio using HTML and JavaScript code. A robust Guides section walks you through the creation process.

Data scientists spend a large amount of their time cleaning datasets and getting them down to a form with which they can work. In fact, a lot of data scientists argue that the initial steps of obtaining and cleaning data constitute 80% of the job.

So far, we have removed unnecessary columns and changed the index of our DataFrame to something more sensible. In this section, we will clean specific columns and get them to a uniform format to get a better understanding of the dataset and enforce consistency. In particular, we will be cleaning Date of Publication and Place of Publication.

Note: At this point, Place of Publication would be a good candidate for conversion to a Categorical dtype, because we can encode the fairly small unique set of cities with integers. (The memory usage of a Categorical is proportional to the number of categories plus the length of the data; an object dtype is a constant times the length of the data.)

In taking the Data Science: Foundations using R Specialization, learners will complete a project at the ending of each course in this specialization. Projects include installing tools, programming in R, cleaning data, performing analyses, as well as peer review assignments.

This week covers the basics to get you started up with R. The Background Materials lesson contains information about course mechanics and some videos on installing R. The Week 1 videos cover the history of R and S, go over the basic data types in R, and describe the functions for reading and writing data.

Some simple steps can easily do the procedure of Data Cleaning in Excel by using Excel Power Query. This tutorial will help you learn about some of the fundamental and straightforward practices for cleaning data in excel.

One of the easiest ways of cleaning data in Excel is to remove duplicates. There is a considerable probability that it might unintentionally duplicate the data without the user's knowledge. In such scenarios, you can eliminate duplicate values.

Another good way of cleaning data in excel is to ensure even formatting or, in some cases, even removing the formatting. The formatting can be as simple as coloring your cells and aligning the text in the cells. It can be a logical condition applied to your cells using Excel's conditional formatting option from the home tab.

Now, you must learn how to eliminate conditional formatting for cleaning data in Excel. This time, consider a different sheet. You must use the student's details sheet, which includes conditional formatting in Excel.

Then check out the Business Analytics certification course offered by Simplilearn, which is career-oriented training and certification. This training will guide you with the fundamental concepts of data analytics and statistics that will enable you to devise insights from data to present your findings using executive-level dashboards and help you come up with data-driven decision-making.

Within your Jupyter notebook, begin by importing the pandas and numpy libraries, two common libraries used for manipulating data, and loading the Titanic data into a pandas DataFrame. To do so, copy the code below into the first cell of the notebook. For more guidance about working with Jupyter notebooks in VS Code, see the Working with Jupyter Notebooks documentation.

This problem can be corrected by replacing the question mark with a missing value that pandas is able to understand. Add the following code to the next cell in your notebook to replace the question marks in the age and fare columns with the numpy NaN value. Notice that we also need to update the column's data type after replacing the values.

Now that the data is in good shape, you can use seaborn and matplotlib to view how certain columns of the dataset relate to survivability. Add the following code to the next cell in your notebook and run it to see the generated plots.

These graphs are helpful in seeing some of the relationships between survival and the input variables of the data, but it's also possible to use pandas to calculate correlations. To do so, all the variables used need to be numeric for the correlation calculation and currently gender is stored as a string. To convert those string values to integers, add and run the following code.

Next, you'll normalize the inputs such that all features are treated equally. For example, within the dataset the values for age range from ~0-100, while gender is only a 1 or 0. By normalizing all the variables, you can ensure that the ranges of values are all the same. Use the following code in a new code cell to scale the input values.

There are many different machine learning algorithms that you could choose from to model the data. The scikit-learn library also provides support for many of them and a chart to help select the one that's right for your scenario. For now, use the Nave Bayes algorithm, a common algorithm for classification problems. Add a cell with the following code to create and train the algorithm.

With a trained model, you can now try it against the test data set that was held back from training. Add and run the following code to predict the outcome of the test data and calculate the accuracy of the model.

Most statistical datasets are data frames made up ofrows and columns. The columns arealmost always labeled and the rows are sometimes labeled. The followingcode provides some data about an imaginary classroom in a formatcommonly seen in the wild. The table has three columns and four rows,and both rows and columns are labeled.

While the order of variables and observations does not affectanalysis, a good ordering makes it easier to scan the raw values. Oneway of organising variables is by their role in the analysis: are valuesfixed by the design of the data collection, or are they measured duringthe course of the experiment? Fixed variables describe the experimentaldesign and are known in advance. Computer scientists often call fixedvariables dimensions, and statisticians usually denote them withsubscripts on random variables. Measured variables are what we actuallymeasure in the study. Fixed variables should come first, followed bymeasured variables, each ordered so that related variables arecontiguous. Rows can then be ordered by the first variable, breakingties with the second and subsequent (fixed) variables. This is theconvention adopted by all tabular displays in this paper.

The following code shows a subset of a typical dataset of this form.This dataset explores the relationship between income and religion inthe US. It comes from a report produced by the Pew Research Center, anAmerican think-tank that collects data on attitudes to topics rangingfrom religion to the internet, and produces many reports that containdatasets in this format. be457b7860

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