Tag Archives: SAS predictive modeling training

How Predictive Analysis Could Have Saved the World from Ransomware

How Predictive Analysis Could Have Saved the World from Ransomware
 

Kudos to you, if you have stayed offline for the last couple of days, so you could actually spend the weekend well with your family and loved ones. The world is reeling under the shattering news surrounding WannaCry Ransomware this weekend. The situation was worse on Monday, after the offices opened. Going by the figures, revealed out on Monday evening by Elliptic, a Bitcoin forensics firm, which is keeping a watch overall – $57,282.23 in ransom has been shelled out to the hackers of Ransomware malware attack, who took over hundreds and thousands of computers worldwide on Friday and through the weekend.

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How to Simulate Multiple Samples From a Linear Regression Model

In this blog post, we will learn how to simulate multiple samples efficiently. In order to keep the discussion, easy we have simulated a single sample with ‘n’ number of observations, and ‘p’ amount of variables. But in order to use the Monte Carlo method to approximate the distribution sampling of statistics, one needs to simulate many specimens with the same regression model.

 

How to Simulate Multiple Samples From a Linear Regression Model

How to Simulate Multiple Samples From a Linear Regression Model


 

The data steps in SAS in  most blogs have 4 steps mentioned for so. However, to simulate multiple samples, put DO loop around these steps that will generate, the error term and the response variable for very observation made in the model.

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How to Use PUT and %PUT Statements in SAS: 6 Tips

The PUT statement in SAS for programmers who have completed a SAS certification in the DATA step and the %PUT macro statements are highly useful statements, which will help to enable you to display the values of variables and macro variables, respectively.

 

How to Use PUT and %PUT Statements in SAS: 6 Tips

 

And almost by default the output will appear in the SAS logs. In this article we will share a few tips which will allow you to make use of these statements more efficiently.

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How to Read Data With SAS JSON Libname Engine

To those of you unaware of the developments in the SAS world, JSON is the new XML and the number of users who need to access JSON has really grown in the recent times. This is mainly due to proliferation of REST-based APIs and other web services. A good reason for JSON being so popular is the fact that it is structured data in the format of text. We have been capable of offering simple parsing techniques, which make use of data step and most recently PROC DS2. Now with the SAS 9.4 and Maintenance 4 we now have a built-in libname engine for the JSON XML.

 

How to read data with SAS JSON libname engine

 

As a SAS training institute we strive to keep our students updated with the latest developments in the field of data analytics even after they move on.

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How Predictive Analysis Works With Data Mining

We know that you have probably heard many times that predictive analysis will further optimize and accentuate your marketing campaigns. But it is hard to envision that in more concrete terms what it will achieve. This makes it harder to choose and direct analytics technology.

 

How Predictive Analysis Works With Data Mining

How Predictive Analysis Works With Data Mining

 

Wondering how you can get a functional value for marketing, sales and product directions without being an expert? The solution to all your problems lies in how predictive analytics may offer with benefits for the current marketing operations. But to use it you must learn a few specifics about how it works.

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Celebrate Christmas in Data Analyst Style With SAS!

Christmas is just at the end of this week, so we at team DexLab decided to help our dear readers who love some data-wizardry, with some SAS magic! You can choose to flaunt your extra SAS knowledge to your peer groups with the below described SAS program.

 

Celebrate Christmas in Data Analyst Style With SAS!

Celebrate Christmas in Data Analyst Style With SAS!

We are taking things a tad backwards by trying to, almost idiosyncratically complicate things that are otherwise simple. After all some say, a job of a data analyst is to do so! However, be it stupid or unnecessary this is definitely by far the coolest way to wish Merry Christmas, in data-analyst style.

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Understanding the Difference Between ‘Sub-Setting IF’ and ‘IF-Then-Else-IF Statement’ in SAS Programming:

Winter is knocking at our doorstep and we are hoping to get our brains worked out with some rigorous learning.

 

Understanding the Difference Between ‘Sub-Setting IF’ and ‘IF-Then-Else-IF Statement’ in SAS Programming:
 
However the weather remains, as data analysts using SAS programming, we can definitely use the weather forecasts to provide the data for explaining the concepts of IF and IF-THEN-ELSE statements to our readers interested in learning SAS predictive modeling.

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Data Preparation using SAS

Before doing any data analysis, there are tasks which are critical to the success of the data analysis project. That critical task is known as data preparation. You may have heard that in the last years the data production is expanding at an astonishing pace. Experts now point to a 4300% increase in annual data generation by 2020. This can be due to the switch from analog to digital technologies and the rapid increase in data generation by individuals and corporations alike. The most of the data generated in the last few years are unstructured.

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In the above context, it is highly important to prepare your data from the unstructured dataset to a structured dataset to do a meaningful analysis.
“Data preparation means manipulation of data into a form suitable for further analysis and processing”
“Data Preparation techniques consists of Cleaning, Integration, Selection and Transformation”
We will discuss some of the data preparation techniques in SAS using SAS. INFORMAT is used to read the data with special characters. FORMAT is used to display the data with special characters.

Data DP.Practice;

length City $10.;
input City $ ID $ Age Salary DOJ Profit;
informat Salary dollar6. DOJ ddmmyy10. Profit dollar7.2;
format Salary dollar6. DOJ ddmmyy10. Profit dollar7.2;
label DOJ = “Date of Joining”;
rename Salary = Salary_of_Employee;
datalines;
Bangalore T101 24 $2,000 12/12/2010 $300.50
Pune T102 29 $3,000 11/10/2006 $400.50
Hyderabad T103 $5,000 12/10/2008 $500.70
Delhi T104 $6,000 12/12/2009 $450.00
Pune T105 $7,000 12/12/2009 $450.00
;
run;

On the above SAS code, we have used both the INFORMAT and FORMAT to read and display the data with special characters. The SAS INFORMAT statement read the salary as numeric variable and in a specific format i.e. $5,000 which is of 6 characters including $. The FORMAT statement displays the same in your input data. Rename and label statements helps modify the variables metadata for further understanding of the dataset.
We will apply some transformations techniques in a dataset which helps us to apply some advanced analytical techniques in the data. We have a dataset that has various attributes of a customer who has subscribed or not subscribed an edition. In our dataset we have a categorical variable status which holds the observation either “Subscribed” or “Not Subscribed”.  We can transform the categorical variable into a dichotomous variable to run a logistic regression on our dataset.

Data media01;
set DP.media;
length status $15;
If status =”subscribed” then status = “0”;
else status = “1”;
run;

On the above SAS code, we have applied simple If Else statements to transform our dataset called media. Transforming a categorical variable into a dichotomous variable helps us to apply the analytical techniques that we want to run in our dataset. Once after the transformation is done, the dataset is good to go for the next stage i.e. data analysis.

The more you torture your data i.e. Data Preparation, the more the success on the outcome of the data analysis.

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Import and Export of dataset using SAS and R

Import and Export of dataset using SAS and R
 

For an analyst, data is a primary raw material, which is used to draw conclusions and inferences for taking business decisions. Raw data is of less help to draw conclusions and inferences. Hence, we need to put the data into any statistical analysis software to slice and dice to bring inference for better decision making. In this post, we will discuss about the steps to import and export of a dataset using SAS and R.

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