Practical Data Science with R
By: Sravan Cynixit
Tags: Database, Data Visualization, Data Science

Data Science is the study of Data in Scientific analysis. It has concepts such as recording, storing and analyzing data to get effective useful information.

A data Scientist has to collect, manage data and select the modeling method, write thecode and check the results.

Let us see how we can get data fast and work with data using R.

Data Science with R can be done by following the below process

Loading Data into R:

In a Data Science Project, If there is a lot of data to analyze, then you may think to use spreadsheets or shell tools to filter into it, but that process takes more time in using the tools than analyzing the data. For more Additional info at Data Science Online Training

But there is a much better way than using those tools, is by using R to analyze data. To change data using R is easy, as R’s main data frame is suitable to work with structured data, and R has devices that can read data from different data patterns.

2. Exploring Data:

Let us assume that our goal is to form a model to see, who doesn’t have health insurance in our customers?. So that you want to market less expensive health insurance to them.

If you collect data from customers and found out properties like age, income, employment status, and so on. Keep all data in one data frame called as cost data, which you need to input to R.

Data exploration concentrates on knowing your data, and know any major issues using techniques like summary statistics, using graphics and visualization.

3. Managing the data

After collecting and finding any issues in data, the next important step is to manage the data that involves arranging the data, for further steps. In this first, we will find any missing values and then do data transformations.

In data transformations, we will make data to represent in an easy way to understand.

Selecting and Evaluating Models:

It’s important to find suitable modeling methods, to solve the particular business issue and find the quality and usefulness of the models.

Memorization methods:

These methods forecast mainly, from summary statistics of your data.

These methods include lookup tables, nearest-neighbor techniques, Naive Bayes classification, and decision trees.

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Linear and logistic regression:

This method gives you the information of similarity, between the input values and the results along with the plansfrom the data.

Unsupervised methods:

Unsupervised methods like clustering and association rule mining, don’t give exact plansbut, find the similarities and inner structure in the data.

Exploring Advanced methods:

We also use different advanced methods, like generalized extra models, kernels, decision trees, and random forests and support vector machines.

Delivering Results:

Documentation of the results is important for sharing your output, to others who will use your model. Delivering results include documentation, demo placements, version control reports and code comments.

Documentation and deployment:

We will use Knitr to design meaningful project documentation, get computerized graphs and results. We will also use Git for performance management and for connecting and deploying models, like HTTP and handling model outcomes.

Producing effective presentations

After preparing documentation on your daily project, works effectively and placing a model for production, then it’s important to present the outcomes of your work to people who are interested.

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For this, we need to make short notes of the inspiration behind, the project and its goals, then explain the project’s outcomes, maintain a backup of the outcome details that are needed and then finally speak about suggestions, important issues, that may occur in future.

For running data science projects in R, we require RStudio and another database, version control tools. Java is important for RStudio and database tools.

CRAN is the most popular R libraries. It is the central role for R. It’s better to install RStudio and Git before using R.