SPSS (Statistical Package for the Social Sciences)



SPSS is a comprehensive, interactive, general-purpose package for data analysis and it includes most routine statistical techniques. SPSS is a true Windows package being mouse-driven with movable, scalable windows, drop-down menus and dialog boxes. Underlying the graphical interface is a command language consistent with previous versions of the package. A very important and useful feature is the Data Editor, which can be used to enter, view or edit data. The Data Editor makes your data immediately visible so that you can see what you are dealing with. The package also has high-resolution graphics which are easily edited and an extensive help system.

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SPSS is a computer program used for survey authoring, data mining, text analytics, statistical analysis etc. SPSS (Statistical Package for the Social Sciences) was released in 1968, developed by Norman. SPSS is one of the widely used programs for statistical analysis in social science. SPSS manual is considered as “sociology’s most influential books”.
Some of the statistics function used in the base software is Descriptive Statistics: Cross tabulation, Frequencies, Explore and Ratio Statistics. Bivariate Statistics: Mean, t-test, ANOVA, Correlation. Prediction for numerical outcomes and prediction for identifying groups. Students who are struggling with these concepts can make use of SPSS Homework Help services.
The first version of SPSS was designed for batch processing and mainframes. Now the version 16.0 runs under Windows, Mac OS X and Linux. SPSS was developed IBM corporations. It runs on the JAVA platform.
SPSS is used widely in most of the industrial sectors. It is used by market researchers, health researchers, survey companies, government educational researchers, marketing organizations and others.
SPSS is probably one of the easiest major statistics package to use. It allows even inexperienced users to run complicated statistical analyses at the click of a few buttons. When you are at the PC you are in charge of the package and it will attempt to do whatever you ask it, whether your instructions are sensible or not! The adage of garbage in, garbage out applies! It is therefore essential that you get a good understanding of the commands that you need to use and what the results mean.

Interfaces of SPSS


Interfaces of SPSS:
More than one way exists for you to command SPSS to do your bidding. And you don’t have to choose one and stick with it — you can perform tasks using whichever of the four interfaces you prefer. You can use any of the four approaches to perform any of the SPSS functions, but which one is best for you depend, to an extent, on the task to be performed and which interface you prefer.
GUI (graphic user interface): SPSS has a windowing interface and commands can be issued by the mouse through menu selections that cause dialog boxes to appear. This is a fill-in-the-blanks approach to statistical analysis that guides you through the process of making choices and selecting values. The advantage of the GUI approach is that, at each step, SPSS will make sure that you enter everything necessary before proceeding to the next step. This is the preferred interface for those just starting out — and if you don’t do much with SPSS, this may be the only interface you ever use.

Syntax: This is the internal language used to command actions from SPSS. It was known as the command syntax of SPSS, hence its name. It is often referred to as the command language. You can write Syntax a command to directly command SPSS to do anything it is capable of doing. In fact, when you use menu and dialog box selections to command SPSS, you are actually generating Syntax commands internally that do your bidding. That is, the GUI is nothing more than the front end of a Syntax command-writing utility. Writing (and saving) command language programs are a good way to store processes that you expect to repeat. You can even grab a copy of the Syntax commands generated by the GUI and save them to be repeated later.

Python: This is a general-purpose language that has a collection of SPSS modules written for it, making it possible to write programs that work inside SPSS. It can be run with the Syntax language to command SPSS to perform statistical functions. One advantage of using Python is the fact that it is a modern language and gives you the power and convenience that come with languages today, including the ability to construct a more readable program. In addition, because it’s a general-purpose language, you can read and write data from other applications and from other files.

Scripts: The items that SPSS calls scripts are actually programs written in BASIC. This language is simple and many people are familiar with it. Also, a BASIC program can be written as an autoscript — a script that executes automatically when SPSS produces certain output.




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Work for a Data Analyst


A data analyst is a person who has to search for information related to the particular requirements of a client. Therefore, you must have the ability to question yourself in relation to the content of a topic. You then have to keep researching until an appropriate solution is found. You have to discover the source of the original data and be able to evaluate it.

Job Titles:
Junior Data Analyst: A junior analyst searches for the appropriate data and provide sufficient material to be analyzed. They have to prepare statistical diagrams and flowcharts.
Senior Data Analyst: A senior analyst has to consult and communicate with the client. He/she has to prepare the final report and present it.
Data Analysis Project Manager: Duties include project organization and methodology. The project manager has to audit the reports and make sure that they fulfill the needs of the client
Since all these are specialized skills, which are in great demand in today's technological world, data analysts have high growth prospects. If you have the required qualifications and skills, and are keenly interested in pursuing a career in this field, then being a data analyst is a good option for you.




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Data Analyst Career


Today there is an increasing demand for data analysts in the world job market. The demand for data analysts is very high all across the United States.
Qualifications for data analyst job:
To be an ideal candidate for the job of data analyst, you need to have knowledge and experience in analyzing the statistical data, and should be a graduate degree in any field related to statistics. You also need to be well versed in data analyze software like Excel, SAS, SPSS, and Minitab.
Skills for data analyst job:
Data analysis requires intensive research work. A data analyst should also have very strong problem solving skills. The main skill required by a data analyst is to extract information related to a topic from raw data. Therefore, you should possess abilities with reference to data mining, data mapping and data warehousing. Data tracking and identifying the trends and patterns in the marketplace are very important tools in the hands of a data analyst.
Statistical and mathematical knowledge is also required. You also need to have knowledge in specialized data management software, and should be able to analyze data statistically, and have the ability to generate reports, and audit and validate them. This will help to analyze the data accurately, which in turn will produce the report according to client requirements.
Other skills required are verbal and writing skills, along with data interpretation and presentation skills.
Work for a Data Analyst
A data analyst is a person who has to search for information related to the particular requirements of a client. Therefore, you must have the ability to question yourself in relation to the content of a topic. You then have to keep researching until an appropriate solution is found. You have to discover the source of the original data and be able to evaluate it.
You should be able to compare data statistically and provide appropriate solutions. Therefore, as a data analyst you have to refer to a large number of data sources and work on your reporting skills, so that you can present it in a simple and effective manner. You also have to audit the report, and the report has to be presentable and appealing to the client.
Job Titles:
Junior Data Analyst: A junior analyst searches for the appropriate data and provide sufficient material to be analyzed. They have to prepare statistical diagrams and flowcharts.
Senior Data Analyst: A senior analyst has to consult and communicate with the client. He/she has to prepare the final report and present it.
Data Analysis Project Manager: Duties include project organization and methodology. The project manager has to audit the reports and make sure that they fulfill the needs of the client
Since all these are specialized skills, which are in great demand in today's technological world, data analysts have high growth prospects. If you have the required qualifications and skills, and are keenly interested in pursuing a career in this field, then being a data analyst is a good option for you.

A Data Analyst


A Data Analyst

 

The term analyst is related to the term scientist. A data analyst collects data, organize it (by using computer or other process), analyze data and present the result of analysis for others.

Or other consideration

The term data analyst refers to somebody who works with data or makes sense of the information buried in raw data or draws inferences and conclusions from it.
A data analyst is somebody who performs queries or mines data for information or nuggets of insight and information.
According to Wikipedia, data analysis is the process of looking at or summarizing data with the intent of extracting useful information and developing conclusions.
Data analyst is a good job in all over the world. We can earn by data analysis. In all over the world, there are millions of data analyst companies. We can join with them.