Category: Analytics

People who wish to embark or make a career shift in coding, or have a passion for coding always find themselves standing at crossroads with the dilemma of choosing the most appropriate programming language. Coding is an essential skill set for any data scientist or a professional working in the data analysis field. SAS often comes as the most preferred option in programming languages. Specially, if you are beginning your career, then this is the most obvious and logical thought to have.
Is SAS the right language to learn? Is knowledge in SAS enough to start your career in data science and coding? SAS v/s R or Python which is best? few questions you should have answers to.
As a general answer, there is no language in programming that can be termed as ‘Best’ purely because it is knowledge that you are acquiring, the same knowledge can be transferred.
All programming languages are good and developmental. Learning a programing language can be compared to driving, if you take the analogy further, you learn driving a particular car, but later you can apply the same skill while driving a truck, or a tractor, or an automatic vehicle, left hand or a right-hand drive. In the same way, all programming languages implement, Input, Output, Variables, Loops, Conditions, and Functions.
Learning one language will make learning the other one simpler, it is majorly only a different Syntax.
SAS is popular and can be considered as the undisputed market leader in the enterprise analytics scenario, it has a good GUI hence learning becomes fairly structured and easier. It has a good array of statistical functions and also offers great technical support.
SAS is majorly popular in established organisations because they are synonymous with great customer support and service. SAS is an expensive tool, therefore in the financial sector, where a budget is not a concern, it is usually the preferred option.
For these reasons SAS is still considered a leader in the coding sector by dominating 80% of market and R and Python together at 20%, they are open source languages.
However, on conducting a survey with over 1000 quantitative professionals, on mapping their preference in programing language, only 39% of them voted for SAS, while 42% for R and the rest 20% for Python.
Retail, Marketing and majorly Healthcare, Pharma and Financial services are loyal to SAS, while Telecom and the Corporate Start-up sector, swings towards R and Python. These sectors have large volumes of unstructured data, machine learning techniques need to be applied, for which R and Python are more suitable.
Cost of learning is also one of the factors while making an informed decision. SAS is very expensive when compared to other languages, so unless your company is assisting you in training, on an individual level it’s a costly affair. Although expensive SAS is fairly easy to learn, plus you do not need any prior knowledge in programming, basic SQL knowledge is good enough.
With SAS it will be easy for you to get a job, it is a fourth generation language, it relies on user-written programs, that when requested know what to do. Based on your needs and interests, it can be said that SAS is versatile and flexible with a variety of input and output formats. SAS has an electronic network where resources are available and one can get connected to share knowledge.
One needs to base their decision on personal factors, if your dream is to join a start-up or the telecom sector, then perhaps R or Python should be your choice. If you want to join the financial sector or venture in healthcare, then maybe SAS should be your first preference before learning any other languages.
 

Everyone wants to become a data scientist, after all, it is considered and labelled as the ‘sexiest job of the 21^st century!’ There is a lot of mystic in the tile ‘Data Scientist’. The data scientist is considered as a wizard, with mystical skills, which when applied can get great insights from the existing data, that holds the key to the future success.

But as much as we would like to believe otherwise, it is the way the data is interpreted, analyzed and methods of data science applied by the data scientist that gets us great results. In this blog let’s understand “What Does a Data Scientist Do?”

The power of data comes from great knowledge of Business Domain, Statistics, Algorithms, Computer Science or Programming skills and exceptional Communication Skills. These become the four pillars of data science. When these skills are applied in a harmonious and synchronized manner, the true essence of data science is discovered.

With the arrival of Big Data, comes the age of large datasets which cannot be managed by the traditional data processing applications. This has opened a way for skilled professionals who can do the job. A data scientist takes the raw data and analysis it in a way that makes it accessible and valuable for the organization to make strategic decisions.

So, in short, they analyze data to get actionable insights from it. A data scientist’s level of experience and knowledge would range from nascent to expert levels, and their degree of work will change with time and business requirements.

Precise tasks performed by the data scientist would be……

• Applying knowledge in identifying the best data analytics program suitable for the organization to optimise opportunities.
• Collecting and analysing large sets of data, in structured, unstructured and semi structured format.
• A major role of the data scientist would include cleaning, and mining and validating the sets of data to ensure that it is accurate, complete, correct and that it is uniform in nature.
• A data scientist has to ensure that the correct dataset and variables are defined.
• A data scientist would need to dedicate a great deal of time in applying models and algorithms to mine the stores of big data for insights.
• One of the key tasks of a data scientist is to identify the patterns and trends in the data set and communicate the same so that strategic decisions can be based on it.
• As much as they are required to identify trends, a data scientist jobs also require finding hidden solutions and opportunities from the data set.
• Lastly, use their communication and visualisations skills to translate the finding or while making suggestions to the stakeholders.

To perform the above tasks, it is vital to have knowledge in maths or statistics, an inquisitive mindset with critical thinking capabilities is an added advantage. A knack for connecting the dots, correlating findings from the data to business advantage is a must.

If you have a background in computer programing, it will be easier for you to devise the algorithms for excavating the stores of big data. R, Python, SAS, Hadoop are a few preferences. A business acumen, that like an entrepreneur will be a big advantage, as you need to be a leader in devising your own methods and building your own infrastructure so that you can treat the data in your own methods to get new discoveries from the same data sets.

Lastly, you need to be confident, understand the business domain of the institution you are working with. Possess exceptional communications skills to that you can explain technical findings to the non-technical stakeholders’.

Harvard correctly states Data scientist as the trendiest job, it is an extremely important role, high in demand and has a significant impact on business’s ability to achieve its goals irrespective of its nature.

The database technology world essentially has two types of databases, SQL and NoSQL. SQL is also referred to as a Relational Database Management System (RDMS), and NoSQL is a Non-Relational database management system. Their differences augment on how they are built, how do they store information and the mainly the type of information they store.

So let’s take an analogy of a phone diary, it stores the phone numbers and addresses in a categorised format, we can say relational databases are like a phone diary.

And non-relational databases are document-oriented, more like a personal file folder, that holds information from the personal address, online presence, holiday preferences, educational qualifications etc…, in the technical world an RDMS system is called SQL and non-relational system is called NoSQL, depending on whether they are written in Structured Query Language (SQL) or not.

For the most part of the century, the RDMS was considered as the most frequently used and dominant model for database management. Since the last 10 years or so the non-relational models are gaining popularity, like ‘Cloud’.

Some advantages of using a Non-Relational model are……

The capability of Elastic Scaling – the ‘Scale Up’ method was generally used for management, where additional servers were purchased as the load increased, rather than the ‘Scale Out’ approach where the distribution of databases across multiple hosts is considered as the load increases. The scaling out method is more economical. While RDMS might not scale out easily on commodity clusters, to take advantage of new nodes, a new breed of NoSQL database is designed to increase transparency.

With the onset of ‘ and internet applications, huge volumes of data is generated and need economic management. A company can thus adopt different NoSQL database systems as they are ‘Open Source’, which have added benefits like, reliability, security faster deployment, and one can have different NoSQL database for different projects like MongoDB, Hadoop, Couch DB etc…,

NoSQL has great ease in implementation, hence you will see it being adopted by most companies like Amazon, BBC, Facebook, Google, all rely on NO SQL Database.

Is there a future for Schema-less data?

Change Management becomes easier with NoSQL database, to an extent of it being non-existent in certain scenarios as there are no model restrictions, as NoSQL stores information in a document database, allowing applications to store information in almost any structure it prefers in a data element.

As a result of this, application changes or Schema changes have a non-complicated approach. A schema is a relationship between tables and fields and in an RBDM the schema needs to be clearly defined before adding any information.

So NoSQL databases are technically Schema-Less, to interpret, it can store documents in any shape, the notion of schema does not disappear, it only shifts the responsibility to maintain the schema to the developer. So we cannot think of Hadoop data source as having no schema.

What essentially Hadoop and NoSQL database allow, is for users to come to the raw data with different schema, which is tolerant to changes.

The rise in NoSQL does not mean an end of SQL databases, there is no competition between the two. Both are suitable for specific cases and will continue to grow in the future.

Is it Useful to Learn Python Language for Big Data?

If you are a big data enthusiast and want to enter the field of big data, or if you are employing a development team to handle your big data requirements, you would find yourself pondering over this question many times. Is Python the best choice over the many other programming languages available? Which language should you train yourself or your staff in? Python or R or Hadoop? well, an article cannot solve your quandary, but read on if you need to find out what Python has to offer.
Python is an open-source programing language, which is most popularly used in big data. Python Language is synonymous with flexibility, and powerful yet easy-to-use features. Python has its USP in the rich set of utilities and the libraries it offers for analytics and data processing tasks. So, all in all, it is a given fact that among other options available Python maintains its popularity essentially because of it’s easy to use features, which supports big data processing.
Python was developed with the philosophy to bring coding to an open platform, where coding becomes easy, more readable, where one can write less number of lines and yet get the desired results. Keeping the objective in mind, a standard library was introduced, which contained ready to use tools for performing various tasks.
These features make Python the most preferred choice for software development, and mostly so for Artificial intelligence and Machine Learning.
To put it shortly you need to learn Python because……

1. It offers a speedy learning curve and reduced development time, the syntax in Python is much cleaner and neater in comparison to other languages. It is easy to debug due to shorter codes. The modular architecture makes it easy to merely import and use a module rather than writing a large block of code. A great choice for beginners. Shorter and quicker codes reduce the development time drastically.

2. You can automate the repetitive tasks, for lesser cognitively demanding tasks, tasks that need little decision-making can be automatically programmed by writing a script in Python.

3. It is the most common choice for data scientists and analytics because of the convenience of feature-rich modules in Python which makes it easy to conduct data analytics in an efficient manner.

4. Python is an object-oriented language, so if you learn Python it will make it easy for you to switch to any other object-oriented language. You will only need to learn the syntax of the other language.

5. It is the future for Artificial Intelligence and Machine Learning, which will be integrated in most functions in the very near future. Python becomes the premium choice for Machine learning algorithms mainly because of the portable extendable and scalable features of the language.

The field of data science and analytics, more specifically artificial intelligence and machine learning will only continue to flourish in the coming years. If you are looking to take a plunge in this field, then fluency in Python can be considered a prerequisite. Learning Python has minimal investment and maximum benefits, it then surely becomes an advantage to learn.

Blockchain is considered as one of the most noteworthy developments in the field of technology over the recent years. Blockchain definitely has the ability to change the way we view big data. Blockchain promises to offer security and data quality, as a primary advantage over many others that are yet to be explored, while taking stalk about the impact Blockchain has on big data. Currently, Blockchain is synonymous to Bitcoin and other similar cryptocurrencies, however, to put it simply, Blockchain as a technology, has the competence to handle and store any kind of information that can be digitized.
Blockchain is essentially a distributed database system, which acts as an ‘open ledger’ that stores and manages any transactions. Each recorded transaction is called a ‘Block’ which gives information like the time of the transaction, along with the link to the previous block, hence information once fed into the Blockchain cannot be changed. The technology also becomes secure simply by design, as each transaction is recorded over multiple distributed database systems. So the information becomes immortal in time till the system exists, where any alterations are not possible. For that reason, it is considered that Blockchain is immutable.

Big Data Challenges

Due to the rise in storage options like cloud computing, it has become easier for companies to store colossal amounts of data which is collected and received from many sources like the internet, corporate systems, online unstructured sources etc…, however storing data does not assure you that the data is correct. Due to genuine human errors, or intentionally data can be tampered. It is no surprise then that executives have trust issues with the quality of data.

Blockchain and Big Data

Blockchain is always associated with bitcoin in conversations, hence its connection with big data might seem vague. Technically Blockchain is simply a database but with features like, Decentralisation as it is not owned by one entity but has shared control, it is Immutable and has a trail of previous link in the block, lastly it has Native Assets and open exchanges in the network.
Blockchain thus takes care of the three most common challenges of big data, Authenticity, Restricted Access which maintain control and quality of data, Monetisation.
If Blockchain is viewed as a simple database, then it can find application in almost any line of business and not only in finance, as it is currently being applied. A good example would be that of Walmart, which is using Blockchain technology to increase food safety, by offering the traceability from its origin to the consumer. Blockchains have great utility when we talk about private data, in the UK talks are already in process with NHS, where they intend to use Blockchain to safely store patient data. This data could be used in the future for research purposes with the knowledge that it is verifiable and authentic.
With Blockchain there is a possibility of Real Time Analytics. Real time fraud detection is a wishful reality for most banking institutions. Blockchain offers a record of every single transaction in their database, hence it serves the opportunity for companies to look for real time patterns if need be.
So far it looks like the benefits and outcomes of Blockchain technology, can be integrated positively across industries. A more evolved database in Blockchain will be the required, perhaps a one that has query knowledge. Therefore, companies that wish to apply big data and Blockchain are finding new tools such as BigChainDB which has added features required for enterprise development.

Introduction Of Tableau

Many of you frequenting the scientifically enhanced circles must have been subjected to hearing this term called ‘Tableau’. According to the Fortune magazine, “Tableau as a concept of visual analytics has been pioneered by a Seattle based software firm.” This concept saw its beginning in the initial stages at the Stanford University in the year 2003 and later on its development resulted from the launching of a public sector offering a decade later in the year 2013.
The basic function and mission of Tableau are to be the creator of various spreadsheet databases and various other information sources which will prove to be simpler for the use for an average person and their usages. The co-founders of this technological advancement are Christian Chabot, Pat Hanrahan, Chris Stolte all of whom worked as a trifecta force and began their journey by bringing together and devising a structured query language for all of those databases which require a descriptive language for the purpose of rendering graphics.
The result of all of those well-timed, meticulous efforts was a database visualization language which is called as Visual Query Language or as it is popularly known by its acronym VizQL. This language essentially underlines the Tableau’s software application which basically then goes on to set up queries in the relational databases, cubes, cloud databases and spreadsheets and then goes on to generate a variety of graphs and different types of representations. These graphs can either be combined into dashboards or also shared over a computer network and even over the internet.
Today this software application actually boasts close to 30,000 client accounts. These client accounts are not just any, but actually include high profile accounts of the World Bank, Coca-Cola, Exxon Mobil, Homeland Security, Pfizer, Fannie Mae, and Gallup, Nike and Adobe and so many more big names. There are many other customers of Tableau where the executives are working in order to develop a 360 degree perspective for their consumer base. Even at the Cornell University there happen to be 600 employees who perform various kinds of analysis by using Tableau.
Those who work under this environment usually perform various functions like contributor relations, in order to visualize faculty salary statistics as well as track all the information about the students and their respective classes that they hope to attend. The Gartner Annual Report that was released in the year 2015, which essentially happens to be a report on the business intelligence and analytical concepts around the globe on an annual basis has proclaimed that Tableau is a ‘leader’ in the business analytics space and if future predictions are to be believed that it is most definitely here to stay.
The various products that this software offers include Tableau Desktop, which helps everyone attain fast analytics, Tableau Server which functions are the collaboration for any organization, Tableau Online which functions as a business intelligence in the cloud and Tableau Public which is generally for public data.

We are in the digital era, with so many gadgets that we use on a daily basis to keep track of our fitness, appointments, expressing ourselves on social networks, smartphones, apps, etc.,
Before the onset of the digital age, marketing professionals were looked upon for their creativity in coming up with catchy jingles and interesting adds to print media and television.
Digital marketing has forced a change in perspective and compelled the marketing department to take aid from technology, to be precise big data, to pass on their marketing message across the right channels.
The digital age has brought with it huge volumes of data. Big data offers great insight which can be used by the marketers to make better marketing strategies. A direct benefit of this is that the companies can develop better content with the help of these insights and thus target the core need of the customer.
Campaigns that use the nuances of Big data have proven to be more beneficial than the previously used blanket mass advertising. This method of using big data for creating advertising campaigns takes away the disparity, the guesswork out of thinking what the customer wants to know what the customer wants.
Now it’s not all as easy as it sounds, applying big data to digital marketing is definitely great, but one also needs to factor that big data can be overwhelming. Getting insights from big data requires the use of analytical tools so that the data can speak and give valuable insights.
So that the insights are received in time, and the companies can understand the data in a speedy manner, data visualisation tools are used to derive actionable insights.
Decisions are based on studying the data points collectively, where the marketing team can look at facts and then base their decisions.

Real-Time Customer Insights

For most businesses, one of the most important tasks is to enhance customer experience. Especially nowadays, due to heavy competition, it is very important that companies know on a whole, what their customers are thinking so that they are able to retain them. Immediate tweaks and turns can be applied and changes can be implemented to leverage the customer.
Big data tools like Sentimental Analysis offers such insight, on what the customer or a section of customers are thinking.

Data Analytics as a Service

Appling big data to digital marketing is not expensive. Many service providers offer database management solutions, mostly to small businesses who collected huge volumes of data. However, with the changing needs, many providers have started offering data analytics as a service as well, this is a perfect solution for the same small businesses that can now use ready dashboards, more so if you are from the non-tech background. It works like this, the data from your company will be hosted and also reviewed remotely, while you can access it on a need basis.

Predictive Analysis

The data-driven approach allows companies to look in the past, pick up what has worked, and see the impact of it in the future in a simulated environment and based on the desired results apply the strategy. This is what predictive analysis offers you.
Paid Search, Search Engine Optimization, Content Marketing are all other forms of digital marketing aided by Big data. Marketing teams should if already not, understand the online marketing channel and use these non-conventional data sources, like Search Information, Customer Transaction and other big data sources available.
Online data is the energy that drives any successful digital marketing campaign.