Python or R: Which to Learn First

If you are interested in Big Data as a career choice and are aware of the skills required to be an expert in this field, then chances are high that you are aware of the role of R and python programming languages for analyzing data. In case you are confused about which language to learn first, this blog will guide you towards making the right decision.

R and Python are free solutions which are user friendly and can be used for data analysis. It is normal for beginners to wonder which language to learn first; but the good thing is both are excellent choices.

Recent studies on the use of programming languages has revealed the following:

Why You Should Choose R?

R has an active, dedicated and thriving community which helps beginners by resolving their queries and offering assistance on various aspects of R. Also, R has an abundance of packages that makes it both accessible and functioning. It is also compatible with Java, C, C++.

R programming does heavy tasks in statistical analysis and also creates high definition graphics. R can perform complicated mathematical operations and the array centered syntax of this language provides great help to persons with little or no knowledge of coding.

Why You Should Choose Python?

Unlike the specialized nature of R, Python is a language that performs a wide range of tasks like engineering and wrangling data, building web applications and scraping websites among others. If have knowledge in OOP, then it is much easier to learn than R programming. Also the code in python is robust, unlike R.

Though its data packages are limited, Python when used in conjunction with tools like Numpy, Pandas, Scikit, it comes pretty close to the comprehensive functionality of R. It is also being adopted for tasks like statistical work of intermediate and basic complexity as well as machine learning.

For more details please visit our blog at http://www.dexlabanalytics.com/blog/python-vs-r-which-you-want-to-learn-first

BIG DATA AND PREDICTIVE ANALYSIS

While psychology still remains to be a study that deals with a cluster of symptoms rather than a specific cause-related diseases; but researchers have long tried to pinpoint a standard causative agent that pushes patients to fall prey to the biggest causes of premature deaths other than cancer. Now it may be possible thanks to the technology related to predictive modeling.

Are you in the age of Aristotle presently?

In Aristotle’s age everybody inquires about the cause behind every happening and that is exactly what the data driven world does today. To identify the cause behind low sales figures of your company or the low revenue generation, get with the Aristotles of today’s world in the realm of data. These books may help you through.

Ways to Overcome Barriers Associated With IoT Data

Things look bright for data scientists and big data analysts before the dawn of the IoT or the Internet of Things, which is expected to yield unforeseen amounts of data from networked devices and sensors that create real-time data that is unstructured. Data architecture is also set to undergo a sea change in order to deal with these developments and properly trained data scientists with the ability to properly leverage this data will be in great demand.

In order to overcome the obstacle of barriers related to data as in relation to the adoption of IoT we may contemplate the following measures:

•    Rethinking Data and IT Infrastructures

The data analytics of IoT will rest ultimately on superior IT infrastructures like clusters of servers, computing based on the cloud, data centers amongst other things. Existing networks are already near the point of being exhausted and the vast amounts of increased data will only serve to put additional pressure on these networks and much more power will be needed in order to process the same properly.

Organization and aggregation of data is a pre-requisite for applying analytics.

Upgrades are an essential part of this new data architecture and technologies like Hadoop with its impressive combination of processing parallel and clusters of servers that are distributed will assume greater importance along with the people who have the technical expertise to set things up and negotiate with some of the more tricky aspects of the architecture.

Chances are that data centers will take the distributed approach path with mini-centers of tiered clusters that pull the data and subsequently send it to other clusters that process the data. This will undoubtedly affect storage of data in addition to backup and bandwidth.

•    Data that is of Good Quality and Can Be Acted Upon

The key issue will be to find the information that is capable of being acted upon and can make a meaningful and real change and the situation will likely arise where a company will find itself capable of collecting data that they do not have any use for as not all of the new data will be useful. Management of such actionable data will be in the purview of business analysts and data scientists.

•    NoSQL databases will in all likelihood replace RDBMSs

Much if not exactly most of the unstructured data available from the Internet of Things may not easily be sorted into a number of tables, a main feature of traditional relational database management systems and they likely to be replaced by NoSQL databases like MongoDV, Couchbase and the like as they provide the flexibility needed by IoT data scientists in order to organize data in a manner that makes it usable.

•    A Software Stack is needed to be chosen in order to preprocess and analyze IoT Data

After the organization and collection of massive amounts of such data, businesses are in need to have the proper plan and the software stack necessary to analyze the data, in place. There is a dire need of choosing the correct software stack and the accompanying database which is able to deal with the type and scale of the data at hand.

A data professional needs to undergo suitable Big data training that will get him up and ready for all of the challenging and exciting tasks that await him in his Big Data adventure.

How is big data analytics a strategic part of business decisions today?

(Decode big data analytics and its relevance in today's business scenario)

Businesses are getting increasingly competitive and thanks to the connected community through multiple devices, the way people are shopping or looking for products or solutions has undergone a paradigm shift. We are living in a changed world in terms of the way technology intersects almost all aspects of business, notwithstanding the size and scale. Big data is driving businesses today and as per Gartner’s bid data predications 2015¹, “By 2020, information will be used to reinvent, digitalize or eliminate 80% of business processes and products from a decade earlier.” Sifting through this big data requires special skills, training and niche area of expertise. The following are some key ways in which big data analytics and professionals engaged in this area will play a pivotal role in innovation, identifying new patterns, product strategy, and customer engagement and remaining competitive by being agile and predictive.

1 Improve internal processes- Companies of all sizes have data being generated through multiple sources by their employees through the BYOD culture, internal chat groups, intranet, emails, customer service calls etc. All this data is critical as it indicates the seamless functioning of a company and its internal health. Companies can use all this analytics to gauge and predict the behavior of employees; the onsite teams etc. and make changes if needed to fill gaps or to raise awareness amongst its internal audience when needed.

2-Increase efficiency- There are sophisticated ways using predictive data analysis and other models wherein huge cost inefficiencies and operational flaws can be looked into. By looking into work flows and other indicators, as well as the production information, analysts can derive at the problem areas helping companies to bring about changes that improve overall productivity by eliminating challenges.

 3-Customer strategy-Social media chatter, emails, voice calls etc. are potential gold mines when it comes to understanding what customers are perceiving about a company or its brand, the adoption trends, overall sentiment and of course when you wish to enter a new geography. Even when customers have a bad or good experience-everything comes down to how companies are planning on using this crucial data. Analysts sift through such information and find triggers, patterns, potential and of course crucial actionable insights that helps companies making a new customer communication or engagement campaign.

The above are some of the ways big data can help companies turn around their operations, remain relevant and profitable in the midst of sweeping changes across the global economy. This is where a certified big data analyst plays a leading role.

 http://www.forbes.com/sites/gartnergroup/2015/02/12/gartner-predicts-three-big-data-trends-for-business-intelligence/2/