Business Solutions: It provides that the business analysis that can be used to us business products for different companies to use.

Analytics: It’s a SAS Analytics market leader in the analytics of multiple business products and serval different services.

Data Access & Management: It can be used to us DBMS Software.

Reporting & Graphics: It can help to imagine the analysis form of a summary and lists and graphic reports.

Visualization: It can be visualized the reporting data in the form of graphs range level from simple scatter plots and bar charts to multiple complex multi-page classification panels.

Access: As we have can get of that figure, SAS enables us to get data from various sources like an Excel file, raw database, Oracle database, and SAS Datasets.

Manage: We can before managing this data to subset data, create variables, verify and clean data.

Analyze: Further, analysis happens on this data. We can offer easy analyses like frequency and averages and complex analyses including regression and forecasting. SAS expects the gold standard for statistical analyses.

Present: Finally we can present our analysis in the form of the list, report and graphic reports. We can print these reports, compose them to a data file or publish them online.

You can use the OUTPUT statement to save summary statistics into a SAS data connected. This information can then be used to create customized articles or to save historical data on a method.

You can use benefits in the OUTPUT report to

1. Specify the statistics to defend in the output data set,
2. Specify the name of this output data set, and
3. Compute and save percentiles not automatically computed by the Capacity method.

Stop statement is the causes SAS to stop processing the current data step

quickly and resume processing statements next to the end of the current data step.

The main difference is that while reading the data an existing data set with the SET statement, SAS holds the data of the value of these variables from one observation to the next. Whereas when viewing the data from external data, only the notes are read. These variables will have to re-declared if they need to mean used.

Ease of Learning: SAS is easy to understand and provides an easy benefit for individuals who already know SQL while on the other support R has a very steep learning curve as it is each low-level programming language.

Data Handling Abilities: SAS is on standard with all leading tools including R & Python while it progresses to check this huge quantity of data also options for parallel counts.

Graphical Capabilities: SAS gives functional graphical proficiencies and adding some small bit of knowledge, it is possible to improve on these plots.

Advancements in Tool: SAS issues updates in a controlled situation, hence they are well tested. R & Python, on the other limitation, become an open contribution and there suggest lots of issues in the latest developments.

Job Scenario: Worldwide Global, SAS is the guide in possible corporate jobs. In India, SAS controls about from 70% of the data analytics market share related to 15% for R.

They Are two Data Type

1.Character

2.Numeric

AS programs are consist of :

DATA step is to recover manipulates data.

PROC step is which reports the data.

It is an area of memory where SAS makes a data set, one view at a time. It is also a logical idea and created after input offer. It also holds two automatic and temporary variables that are used for manipulation but which are no recorded to single data set as the role of observation.

The identical notes are evaluated and removed through NODUP option. NODUPKEY option checks for all BY changeable values and if detected, it will eliminate that.

Proc Summary is same as Proc Means. it will give detailed statistics though that will no give output as default, we ought to give an option number then only it will return the output.

1.DATA statement, which describes your data set.

2. The names of specific variables in your data set are reported by an INPUT statement.

3. The statement should be ended through semi-colon(;).

4. Space between word including statement should be there.

Removes tracking blanks from a character representation

Str1 = ‘my’;

Str2 = ‘Image’;

Result = TRIM (Str1) (Str2);

Result = ‘myimage’

1.SAS creates a dataset one note through a time.

2. The input buffer means created at the moment of compilation, as being a record about that external file.

3.PDV is created followed by the creation of input buffer.

4.SAS builds dataset into the PDV area of memory creates a dataset unity comment at a time.

5. The input buffer is created at the time of compilation, for holding each record of that external file.

6.PDV is created served by this creation of input buffer.

7.SAS makes dataset in the PDV area of memory

Each package offers its own individual strengths and gaps. As one complex, SAS, Stata, and SPSS form a set of tools that can be applied for a wide variety of statistical analysis. With Stat/Transfer it is very easy to convert data files of one package to another in just a matter of seconds about conditions. Therefore, their package is quite an asset before switching from one analysis unit to differ depending on the nature of your problem. For example, if you were performing analysis using different types you might choose SAS, but if you were doing logistic regression you depth choose Stata, and if you meant doing an outline of variance you might choose SPSS. If you are frequently doing statistical analysis, we would strongly urge you to view giving each one of these cases part of your toolkit for data analysis.

The MOD function returns is the remainder the division of elements and it’s the first argument by elements of the second argument.

As the name submits, DESCRIBE is used to explain something. Since in database we have reported, that’s why we use DESCRIBE or DESC(both are same) command to define the composition of a table. Syntax: DESCRIBE one; OR DESC one;

The FLOOR is a function of rounds down.

The CEIL is the function of rounds up.

The ROUND is the function of rounds to the nearest integer.

The INT is the function of rounds towards zero.

A common task in data use is to take all observations that appear multiple times in a data set – in other words, to get the duplicates. It turns out that there is no method or function use that will directly give the duplicates of a data set in SAS*.

1. By using nodups in the method proc sort data=SAS-Dataset nodups; by var;

run;

2. By using an SQL query inside a dataset method value

Proc sql;

Create SAS-Dataset as select * from Old-SAS-Dataset where var=distinct(var); quit;

3. By cleaning the data

Set temp;

By group;

If first.group and last.group then

Run;

1.SAS scans each statement in the SQL procedure and checks syntax errors, before -mentioned as needing semicolons and wrong statements.

2.SQL optimizer scans the query inside the statement. The SQL Optimizer selects how the SQL query should be performed in order to reduce run time.

3. Any tables in the FROM statement are loaded into the data engine where they can then be entered in thought.

4. Code and Calculations are executed.

5. Final Table is created in memory.

6. Final Table involves sent to the output table described in the SQL record.

Data is central to every data set. In SAS, data is available in a tabular form where variables fill the column space and shows keep the row space.

SAS treats numbers as numeric data and everything else comes following quality data.

Hence SAS has two data types numeric also quality.

Apart from those, dates in SAS are designed in a special way connected to different languages.

A most important difference between the DO UNTIL and DO WHILE statements mean that the DO WHILE invention is measured at the top of the DO loop. If the expression value is false the first time it is evaluated, then the DO loop is never executed. Whereas DO UNTIL does at least once.

The number of data value observations is limited only by computer’s ability to handle and store them. Prior to SAS 9.1, SAS data sets could get up to 32,767 variables. In SAS 9.1, the most number of variables in a SAS data set is confined by the resources accessible on your computer.

Merging combines is to observations from two or more than SAS data set value into a single observation in a new data set.

A one-to-one merge, shown in the following figure, links to observations based on their location in the data sets. You use the MERGE statement for one-to-one merging dataset value.

Interleaving combines only sorted SAS data set value into one sorted data set. You interleave dataset contains a SET statement and a BY statement in a DATA step. The number of observations value in the new data set function is the sum of the number of views in the original data sets.

If you convert into the data for Excel date to a SAS date, subtract 21916: the difference in the opening points of the programs. Translation of an Excel time value within a SAS time preference means any question of increasing by 86400, that number of moments in a day.It depends on the version of Excel used, mac vs windows have new editions, your SAS version and the bitness between particular two. That sad fact is that proc import opinions…and its opinions aren’t worth betting on. Excel is not a database and makes not have fixed types that it enforces for cells. This is created for excel, but not for data management.

It is intended for SAS programmers who have no prior exposure to this SQL system as well as those new to SAS. The Structured Query Language (SQL) is a graded language used to retrieve and update data stored in relational tables

A small p-value (typically ≤ 0.05) means strong evidence upon the null hypothesis, so you refuse the null hypothesis. A large p-value (> 0.05) means weak evidence upon the negative hypothesis, so you fail to reject the null hypothesis.

The normal distribution is the most important value of probability distribution in statistics because it fits many natural events. It is also called the Gaussian distribution and the bell curve. The normal distribution is a probability function data that describes how the calculate the value’s data of a variable are distributed.

Linear regression is a basic and usually used type of predictive analysis. … These regression calculations are used to explain the relationship between one conditional variable and one or more independent variables.

• The variable is automatically set to 0 before SAS reads the first dataset observation value. The variable’s value is maintained from one iteration to the next as if it had arrived in a RETAIN statement.

• To initialize a sum of dataset variable to a value other than 0, include it in a RETAIN statement with an initial value.

“Cost” is an expression

• The expression is assessed and the result added to the accumulator variable.

• SAS treats an expression that returns a lost value as zero.

The SAS software suite has more than 200 components Some of the SAS parts include: Base SAS – Basic procedures and data management. SAS/STAT – Statistical analysis. SAS/GRAPH – Graphics and presentation.

SAS at this time contains a suite of different applications which can perform Analytics, Reporting, Business Intelligence, Data Handling, Statistical Modelling, and several other stuff. SAS is advanced statistical and data analysis software. It can be used in too many valuable uses to list.

It just gets some dedicated time and hard work. I spent six days in the class wrote lots of SAS Programs, and read for ten hours/week for five weeks.

SAS, Google, Facebook, Twitter, Netflix, Accenture, WNS, Genpact.

SAS (“Statistical Analysis System”) is a software suite developed by SAS Institute for advanced analytics, multivariate analyses, business intelligence, data management, and predictive analytics.

Whether it’s common data in operational systems or big data in a Hadoop cluster, the data value is an asset that each organization has. And maintaining that data is no longer a preference – it’s a necessity. SAS Data Management is the solving the answer to your data integration and the quality of data challenges.