SAS (Statistical Analysis System) is a leader in business analytics.

SAS is software that transforms data into insight which can give a fresh perspective on business.

Unlike other BI tools available in the market, it takes an extensive programming approach to data transformation and analysis that makes it stand out from the crowd and finally it gives much finer control over data manipulation.

It is specifically used for :

• Data entry, retrieval, and management
• Report writing and graphics design
• Statistical and mathematical analysis
• Business forecasting and decision support
• Operations research and project management
• Applications development

Why SAS?

Features intrinsic to SAS which makes it a market leader are :

• Data Security/Authenticity: SAS is a closed source so it is completely secured. Data security prevents it from manipulation.
• Handling volumes of Data: SAS has a strong ability to handle large databases very easily.
• Easy to debug: SAS is a very comprehensible language. The process of debugging is easy. We can understand and correct the error that the log window clearly states.
• SAS GUI: SAS is one such language that has made statistical computing easier for non-programming users. It has an amazing Graphical User Interface (GUI) which provides various tools like graphs, plots, and a highly versatile library.

Why SAS is a preferred skill?

Comparing with R and Python

• Data managing capabilities: In the case of data handling and management, SAS is better, smooth and safe. R comes with a significant disadvantage that it only works on RAM, which is a big problem since little exercises will also take time to run based on your machine’s RAM. Therefore, manipulating data is more natural when it comes to packages such as Plyr and DPlyr. We also face the same in the case of Python. When extensions like Numpy and Pandas compared to others, fundamental analysis and data handling work are similar to Python.
• Simplified coding: SAS is the most comfortable language across all three to learn and can be picked up by anyone without prior programming experience. The ability to parse SQL codes, integrated with macros and other native flavors makes SAS discovering a child’s play for individuals with primary SQL knowledge. Its learning curve is low-to-medium.

Applications of SAS

The image below shows a few applications :

• Multivariate Analysis: For instance, consider a person who wishes to buy stock in bulk. So he or she would weigh various factors like price, quantity, quality, etc. The multivariate analysis does the same thing. It detects and analyses various statistical variables of an outcome at the same time. It uses various studies which depict the effect of variable factors on single result. It includes analysis of factor analysis, bivariate analysis, and multiple regressions.
• Business Intelligence (BI): It refers to strategies and technologies used by any enterprise for data analysis of business information. It provides insights regarding predictive, current and historical views of business working. The analysis of data helps the senior board with scope for decision making. These technologies include reporting, data mining, process mining, complex event processing, benchmarking, etc.
• Predictive Analysis: As the name suggests it uses already available data to predict the future. It uses various statistical techniques to draw inferences. For instance, in a company, the trend in sales of product ‘A’ has been constant over the years. So, it suggests non-changing demand for the product. But for product ‘B’ with changing demand every month, it analyses all the factors causing the variation, hidden inferences in the text, the customer thought process, etc. Here predictive model exploits patterns found in historical data to identify risk.

Components of SAS

Let us take a look at a few important SAS components:

• Base SAS: It is the most widely used component. It has a data management facility. You can do data analysis using Base SAS.
• SAS/GRAPH: With the use SAS/Graph you can represent data as graphs. This makes data visualization easy.
• SAS/STAT: It lets you perform Statistical analysis, such as Variance, Regression, Multivariate, Survival, and Psychometric analysis.
• SAS/ETS: It is suited for Time Series Analysis.

Disadvantages of SAS

Now, let us look at some of the disadvantages of SAS:

• SAS is an expensive tool.
• Algorithms used in SAS procedures are not open to the public
• Less number of graphical capabilities available.
• Compared to its competitors, SAS is still an old software.
• Software like R provides many of the same features free of cost.
• 500 lines of SAS code can be equivalent to 100 lines of R code.
• For doing Time Series Forecasting we need to purchase SAS ETS Module.

I hope you enjoyed reading my post and got an idea of what SAS is. This was my new medium story, if you find any technical or grammatical mistake then you can comment the same and don’t forget to clap.

Introduction to SAS was originally published in DATA HAS A BETTER IDEA on Medium, where people are continuing the conversation by highlighting and responding to this story.

In this blog, I will be covering the following topics :

1. The need for Data Science.
2. What is Data Science?
3. The lifecycle of Data Science.

Let’s understand the need for Data Science

Till the 1990s we were dealing with the structured data which was small in size and could be easily analyzed using simple Business Intelligence (BI) tools. But today, we are dealing with the unstructured data or semi-structured data which is bigger in size and can’t be analyzed precisely with simple BI tools. Hence, there is a need for more complex and advanced analytical tools for processing, analyzing and drawing meaningful insights from the unstructured data.

Let’s have a look at the data trends in the image given below. This shows that by 2020, more than 80% of the data will be unstructured.

Wait..! This is not the only reason why Data Science has become so popular. Let’s dig deeper and see how it is needed in other domains too:

1. Just imagine that how amazing it would be if you were working in an organization as a salesperson and by looking at the existing data of your customers like customer’s past browsing history, purchase history, age and income. You can recommend the product to your customer with more precision. Well, this could bring more business to your organization!
2. How about self-driving cars? Self-driving cars collect live data from sensors, including radars, cameras and lasers to create a map of its surroundings. Based on this data, it decides when to speed up, when to speed down, when to overtake, where to take a turn. Isn’t it amazing?
3. Data Science is also used in the predictive analysis. For example in weather forecasting, data from ships, aircraft, radars, satellites can be collected and analyzed to build models. These models will forecast the weather as well as help in predicting the occurrence of any natural calamities. Thank God! It could save thousands of lives.

Let’s have a look at the below infographic to see applications of Data Science in more domains :

Since you are now familiar with the need for Data Science, now let’s understand what it really is.

What is Data Science?

Every human is a born Data Scientist.

Do you want to know how? Let’s see it.

Let me ask a question from you. What do you usually do while travelling from a metro? The one thing I do is OBSERVE! OBSERVE people, OBSERVE surroundings, OBSERVE everything. In one way or the other, you are observing your surroundings. You are taking in details from your surroundings and feeding it to your brain and then your brain processes these observations into data and use it to understand the things around you. You even make predictions out of this data that what is likely to happen next.

Similarly, if we define Data Science technically, then it is a perfect combination of Statistics, Mathematics, Computer Science and Business knowledge that allows you to extract meaningful insights from the raw and unstructured data. Data Science is the science which uses various tools, algorithms and machine learning principles with the goal to discover the hidden patterns from raw data.

So, as you are now familiar with the term Data Science, let’s move onto the life cycle of Data Science.

Life Cycle of Data Science

1. Discovery : When you begin with the project, the very first thing you must do is try to understand the various specifications, requirements, priorities and required budget. Try to gather the required information by asking the right questions. This is the phase where you frame the business problem and formulate an initial hypothesis to test.
2. Data Preparation: It means cleaning of the data. You clean data by treating missing values and outliers (extreme values). You can achieve this using R. In this phase you will explore, preprocess and condition data prior to modelling. Once data is cleaned or prepared then it is ready to do exploratory analysis on it.
3. Model Planning: In this phase, you will determine the methods and techniques to draw the relationships between the variables. Common tools for model planning are-SQL, R, SAS and many more. So, now that you have got the idea about the nature of your data and decided the algorithms to be used, in the next phase you will apply the algorithms and build up the model.
4. Model Building: In this phase, you will develop the datasets for training and testing purposes. You will analyze which modelling technique is best fit to prepare the model like classification, association and clustering to build the model.
5. Operationalize: In this phase, you will deliver the final reports, briefings, code and technical documents.
6. Communicate Results: It is important to evaluate if you have achieved the goal that you have planned in phase 1. So, in this last phase, you will identify all the key findings and communicate to the stakeholders to determine if the results of the project are a success or failure.

I hope you enjoyed reading my post and got an idea what Data Science really is. This was my first medium story, if you find any technical or grammatical mistake then you can comment the same and don’t forget to clap.