Data Analytics Tools

Analytic professionals have used a range of tools over the years, which enabled them to?prepare data for analysis, execute analytic algorithms, and assess the results.?These tools have evolved over time which has added to their functionality. Apart from the robust user interfaces, tools can now be used for?automating and streamlining mundane tasks.? As a result, analytic professionals end up with more time to focus on analysis. These combinations of new tools are bolstered by efficient and scalable processes that allow the organisations to tame?Big Data

Evolution of Data Analytic Approaches

In this section, we will discuss the evolution of the data analytic approaches.

Over the years, many data analytical and statistical techniques have been in use. Some of these techniques and approaches such as?regression, classification, clustering?have been effectively used to solve the data problems. Previously, there were constraints on tool availability and even scalability. And, it required much more simpler models and data.

The growth in technology has seen an emerge in Big Data. This data is present in large volumes and requires advanced statistical as well as data manipulation techniques. Furthermore, there is a need for development for scalable models that can not only handle such a large volume of data but do it efficiently and without any fault.

The traditional statistical techniques have evolved over the years to accommodate large volumes of data. Today, we have advanced?machine learning algorithms?that are able to draw accurate predictions with a large amount of data. Deep Learning is one of those tools that perform accurate predictions with an increase in data. Therefore, it is apt for dealing with such a surplus volume of data.

Some of the analytical methods are as follows:

1. Ensemble Methods

The key principle behind Ensemble Methods is the combination of multiple base models to strengthen the overall performance of the combined model. There are several methods in Ensemble Learning – BAGGING and Random Forest Models.?The power of ensemble models stems from different techniques that pose varying strengths and weaknesses.

2. Commodity Modeling

The aim of commodity modeling is not the development of an accurate model but a model that will help us to obtain better results. A commodity model provides us with a lower bar that all the other models have already cleared. This model halts when it obtains better results. While quantifying our commodity model, the primary concern is to lead yourself to better results.

3. Text Data Analysis

Text data is an unstructured data. This form of data is everywhere on social media, telephonic logs, voice messages, etc. Companies and organisations analyse the text data to?unearth hidden information, customer sentiment, dissatisfaction,?etc. Semantic Mining is one of the most used techniques in Text Analysis. With this, companies are able to assess the meaning of user posts and review it without manually going through them. This allows them to obtain the overall customer report, allowing them to make the necessary decisions.

Categories of Data Analytics Tools

There are two types of tools in data analytics:

1. Statistical Data Analysis Tool

The modern commercial data tools consist of GUIs that enable the user to implement their code with minimal lines of code. As a result, the utility has become a major area of focus for the organisations. With the help of various pre-defined and pre-processed packages and functions, we can achieve the various tasks very easily without any hassle of writing long pages of code.

With the assistance of robust GUIs, users are able to?perform rapid prototyping and obtain analytical results?at a fast pace. As a result, analytics professionals are able to perform jobs quickly with accurate results. GUI tools are apt at optimising the time of these professionals as they are able to focus on the statistical and analysis methodologies and spend less time on writing the code.

2. Data Visualisation Tool

The results obtained from the analysis of the data need to be represented in the forms that are useful for the user. Data Analytics professionals are able to?create interactive, appealing and aesthetic visual analytics?using visualisation tools. The complex analytical results need to be explained in a lucid manner by the analytic professional routinely. Anything that can help this to be done more effectively is a good thing. Data visualisation falls into this category. Considering the complication of data analytics results, the clients often understand the clear data depictions through charts and graphs. This is where visualisation helps.

What is R?

R was developed in 1995 by Ross Ihaka and Robert Gentleman. It is a programming language that is most widely used by statisticians and data miners for performing statistical modeling and computations. R’s popularity is mainly due to its specific functionalities in the field of?statistical data analysis?and?graphical techniques.

The most impressive feature about R is its massive collection of packages that exceed 10,000 in the CRAN repository. Various statistical applications and fields like?medicine, astronomy, sales and finance?make use of R because of its diverse packages. R tends to have a steep learning curve despite having an easy to understand syntax. R is mostly considered to be an expressive tool through which statistical learning can be implemented. It is not meant for beginners who have little or no knowledge of statistics.

Advantages of R:

1. R is entirely open-source. Therefore, you can utilise this tool without any requirement for a licence. You can also work towards the development of the R language by?developing packages, customising its code and through the resolution of its existing problems.?Furthermore, you can contribute towards the development of R by customising its packages, developing new ones and resolving issues.

2. R is the most popular language because of its?data-wrangling facilities. With the help of packages like?dplyr, readr,?R is capable of performing data wrangling.?

3. R has a colossal repository of packages. There are over 10,000 packages in the CRAN repository and this number is growing at a constant rate. Furthermore, these packages are of utilisation by all the areas of industry.??

4. With the help of R, you can?delineate visually appealing plotting and graphing.?There are various popular libraries like?ggplot2?and?plotly?that are used heavily for the aesthetic creation of graphs.

5. R is platform independent and holds cross-platform compatibility on Windows, Linux and Mac.

Limitations of R


1. R was developed from the much older programming language called S. The architecture of R therefore, is much older that does not advocate for dynamic and 3D graphics.?

2. R stores its objects in a physical memory. This is a problem when the data is much larger and the memory is less. R also utilises a lot of memory for its execution of statistical models. It loads all of its data into one single place and hence it is not ideal when dealing with large data sets.?

3. R is not secure. This is in contrast with other tools like SAS and SPSS where security is the most quintessential feature.?

4. R has a steep learning curve. It is not an ideal programming language for people who are beginners in programming.

What is SAS?

SAS stands for Statistical Analysis System.?It?was?developed?by the SAS Institute with a sole purpose of efficient?statistical modeling.?SAS has a variety of applications in the field of statistical modeling. It is popular for?predictive analytics, business intelligence, data management, multivariate analysis,?etc. At the North Carolina State University, SAS developed as a rival to IBM’s SPSS. It has now evolved into a primary and a major tool for statistical modeling.

SAS has been a power player in the world of analytics and enterprise market. It facilitates various functionalities like?data mining, updation, data extraction and data management. We apply these methods for statistical analysis after data extraction and processing is carried out. You can perform these actions using the SAS programming environment – SAS Studio.

i. Advantages of SAS:

1. SAS offers high security to its users. Due to this, it has become a trusted name in the enterprise industry.

2. It comprises of a wide range of statistical libraries that allow the organisations to implement these techniques on all types of data.

3. It provides a scalable and stable software that allows the companies to load large volumes of data and also facilitates ease of extension with various Big Data platforms.

4. SAS facilitates interaction with the data files that other statistical tools like?Excel, SPSS, Stata,?etc generate. All the external data files can be easily converted into the SAS format.

5.? SAS has an active and dedicated support centre. It is helpful when you are dealing with any form of error, either in regards to the installation or any bug that you encountered during the execution.

ii. Limitations of SAS

1. SAS is a closed source software. It means that you have to buy a licence for using it. The cost of this licence is very expensive that individuals or small-scale enterprises cannot afford.

2. SAS lacks most features in graphical visualisations. It falls behind in these areas when compared to an open-source tool like R.

3. Most of the features in SAS are very limited. In order to use statistical techniques or machine learning models, you will have to purchase other versions of R that can add up to the overall costs.

What is SPSS?

SPSS stands for Statistical Process for Social Sciences.?While the name suggests its original usage in the field of Social Sciences, it is now being used in every field that makes use of data ever since its acquisition by IBM in 2009. The IBM SPSS Software?is?for?advanced analytics, text analytics, trend analysis, validation of assumptions and translation of business problems into data science solutions.

Industries and organisations use the SPSS software for performing hypothesis testing, ad-hoc analysis and forecasting. Minimal lines of code are capable of carrying this out through the usage of functionalities. SPSS?is?closed?source and requires a licence for use.

1. Advantages of SPSS:

1. SPSS is easy to use due to its GUI features that facilitate minimal coding to undertake complex tasks.

2. It comprises of efficient data management tools with which the user can have a lot of control.

3. It is popular because of its in-depth data analysis, faster as well as accurate data results.

4. SPSS keeps track and the location of data objects and variables. This allows the user to efficiently manage the model and perform faster data analysis.

5. A separate file stores the SPSS data. This also aids in better management as the users need to no longer worry about file overwriting or mixing of the data.

2. Limitations of SPSS:

1. As compared with SAS, SPSS has a limited data storage facility. Therefore, it is not so apt at handling and processing large datasets.

2. SPSS is also closed source and expensive to purchase. Only large scale enterprises and organisations can afford to purchase this software for their data requirements.

3. It provides a limited syntax and features that are otherwise prevalent in other programming tools like R and SAS.

R vs SAS vs SPSS

Let us see a comparison between the three Data analytics tools seen above:

1. User Interface

When it comes to interactive GUI, SAS takes the lead followed by SPSS. SAS offers an interactive and user-friendly interface. On the other hand, R is a programming tool that requires the user to code statistical model. Working in R requires knowledge of the programming fundamentals. SAS and SPSS were developed to implement statistical models with minimal code through an extensive interface.

2. Decision Trees

IBM SPSS holds the edge when it comes to the implementation of decision tree algorithms. In the case of the SAS tool, you cannot implement decision trees without purchasing the expensive data mining suite. This limits the capabilities of the base SAS package which is already highly expensive. Furthermore, the decision trees that IBM SPSS supports, are much more diverse than the ones that are distributed by R.

3. Data Management

Data Management is the strongest suite of SPSS. SAS follows this. In data management, SAS has an edge over IBM SPSS and is somewhat better than R. A major drawback of R is that most of its functions load all the data into memory before execution, which sets a limit on the volumes that it can handle. However, some packages are beginning to break free of this constraint. One example is the?biglmpackage?for linear models.

4. Documentation

R provides extensive documentation through various manuals, books, journals as well as the contributed documentation of the CRAN website. SPSS lags behind R, in this feature. On the contrary, SAS has comprehensive technical documentation that covers the depth of SAS programming. One of the strongest suits of R is its community support. The R community organises various seminars, bootcamps to promote its support for programming.

5. Learning Curve

Areas that require utility have a preference for SPSS. It provides various functions that can?be?pasted?into the interface to obtain fast and accurate results. As a result, SPSS has the?easiest learning curve.?And, SAS also follows this. R has the steepest learning curve among all. In R, we perform statistical modeling through programming. Therefore, it is essential to have knowledge of software fundamentals and programming paradigms in R.

6. Data Handling Capability

SPSS’s limitations are mostly its inability to handle a large amount of data. SAS proves to be a powerful tool when it comes to working on a large dataset. It can efficiently slice and splice the data. R, on the other hand, is relatively slow when it comes to data loading and data processing..