The market for Business Analytics stood at $42.55 billion in 2014 and is all set to touch $70.11 billion by the year 2022, globally. The industry will witness a CAGR of a considerable 6.44% during the period of 2014 to 2022 for which the forecast is made. The factors that are fuelling the growth of the market include rising demand for analytics by organizations as more and more organizations embrace Big Data, changing the environment in which businesses operate and the choices made by customers with unprecedented swiftness. The things that stand as an obstacle to its growth are factors like the relatively high costs of execution and a general unwillingness to adopt Business Analytics. Other hindrances are severe shortages of skilled workers who have the technical ability to run applications related to Business Analytics.
The lion’s share of the market has been captured by financial services, insurance and banking sector. The ten top vendors of business analytics software together constituted for 70% of the market share all over the world as of 2013. In the year 2013, SAP, Oracle, IBM and Microsoft together sold more than 50% of all sales of software related to business analytics. Tableau earned the distinction as being the fastest growing software company in the category of business analytics in the same year, witnessing a growth of 80% in a single year.
The global market for business analytics is segmented based on application, deployment, end users, software as well as geography. If deployment is considered the market is further segregated to cloud and on-site deployment. If the market is viewed from the perspective of the end user it may be categorized medium and small businesses and large enterprises. From the point of view of application the market for business analytics may be segmented into IT and telecom, media and entertainment, retail, healthcare, manufacturing, energy and power, government, banking, education, insurance and financial services.
According to software the business analytics market globally may be segregated into search and alter, performance and management of big data, predictive analytics, discovery of data, software for visualization, business intelligence and analytics of content. According to geography the markets are North America, Asia Pacific, Rest of the world besides Europe.
The key players in this market are INFOR, IBM, Microsoft, Oracle, Microstrategy Incorporated, Inc., SAS Institute, Tableau, QLIK Technologies and Tibco Software.
Business Analyst Certification Training
If you are interested in this field and are contemplating a career here it is highly advisable that you sign up for a Business Analysis Training in Delhi.
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This post is targeted towards established R Programmers or those who are learning the basics of R Programming through proper R programming certification.
The top ten tips that R enthusiasts should follow while writing their code are as follows:
- You don’t have to tidy up things manually
Though it is indeed a best practice to keep your code neat and clean, you can prevent unnecessary waste of time by letting linters like formatR do the trick for you. After its job has been done you can just lay back and relax with a few minor tweaks here and there.
- Make Use of an IDE
Though you most certainly can write code on a text editor or even the R graphical user interface but Rstudio like Interactive Development Environments make the process of writing code hassle free and so much easier. The code completion hints is sure to save you much time.
- Get To Know the Hotkeys
IDEs quite akin to the OS comes with its share of hotkeys. This saves loads of time as you accomplish more without ever taking your hands off the keyboard.
- Plan Before Coding
If you are sure of the direction the practice of your coding may take you will find the task of coding far easier. And doing things like commenting makes things even more easier.
- If unsure about something make sure to just Google it
If you start from scratch you are sure to learn a lot of things but there is a better more sensible way. Just Google the problem in search of canonical solutions, some of the more common pitfalls or perhaps simply some things that you should take into consideration.
- Avoid repetition
In all probability this tip is one of those that you are pretty much sure to have heard befory but nonetheless is worthwhile to mention. R has the potential to create functions, split the codes that need to be repeated into a set of functions.
- Select the appropriate tool in your context
Avoid relying on R as the primary hammer tool of your choice. Make evaluations of project needs and make use of appropriate languages. If you learn a bit at the outset you are spared of whole lot of pain later.
- Write code that facilitates tests
Make it a second nature to test your code and make the whole procedure quick and one that may be conducted with ease. While writing code incorporate validation and avoid using functions that have negative effects.
- Make proper documentation
Make this a regular part of your code writing practice that is write documentation as you go along the process of writing your code. If you leave the task till the end not only will the task be harder to complete but the final documentation will rarely be completed.
- Make Proper Source Control
Make use of source control like Git or SVN that lets you regularly maintain code versions and lets you make your development more conducive to collaboration.
The value of a code lies in a great part on whether it is documented, tracked and may be tested easily. If you are undergoing a course in a proper R programming training institute these tips are sure to separate you from your peers.
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If you still are unaware of the power of data analytics even after the success of the movie Moneyball, then you might as well be living under a rock. Yes the book by Michael Lewis transformed the way sports administrators can identify low-performing players by using data analysis rather than the conventional techniques. This was the first time something as analytical and core-academic was used in the field of sports and that too for something as substantial as selecting players.
But today data analysis is used in almost every aspect of our lives than just the way we like our favorite teams in popular sports. The basic idea behind statistical analysis is to determine meaningful patterns within a given set of data. This discipline is slowly edging its way into every conceivable business avenue these days whether big or small. The main elements in this field are data (that are available easily these days) and highly powerful computer programs that are used to analyze it and draw valuable insights from it. This field has completely changed the way we perceive information and make decisions. Historically these analysis tools have helped companies take decisions on how they should position themselves in new market ventures as well as place their new products.
For instance, ecommerce start-ups that house some of the smartest minds in the business are using expert statisticians to analyze Big Data with state-of-the-art data analysis tools to generate useful deductions from otherwise dull numbers and decimal points. Whether it is the heavy shifters in the logistics market or simply consumer durables and other fast selling products, companies are using data analysis to predict trends, deduce pricing patterns and are leveraging themselves with a revolutionary use of data analysis tools. Also in the sports world data analysis is behind big decisions like who to draft in a team to what should be the in-game strategy to marketing budget. Movie studios also employ analysis tools to calculate box-office generations and to even predict which movies will be a hit and which ones will be a flop.
Therefore, it is highly unlikely that data analytics would not make its way into the field that generates the highest number of data i.e. finances. The worlds of finances are all about numbers and so data analysis is an integral tool that has been used as an operational instrument in this field for a long-time now. While data analysis has been used in the financial world for several years globally, it is still fairly new to the Indian markets. Until now Indian markets only used data analysis tools in brokerage firms for slicing orders and for minimizing the impact cost of sales and purchases. But data analysis can also be used for mitigating frauds in money laundering, risk analysis and management and for rogue trading.
But currently the stock market is also on the verge of boarding the Big Data analysis train which is known as ‘Quants’ amongst the industry insiders. The basis of decision making in the stock trading market holding the hands of Data Analysis is to choose on the basis of history repeats itself theory. So the analysis tools are used more for technical analysis. The trend of analyzing data on the verge of the result season is slowly transforming as realizing valuable insights and patterns in data has somewhat a knee-jerk effect for the analysts and experts associated with the field. Hence, it is understandable that data analysis is making its way into the core practices of how we trade finances. So, it is understandable that the new key player in the market is data analysis or in other words also known as power computing. As the market experts have realized that using data analysis tools with precision will only increase their trading capabilities while enriching their funds in the process, so it is highly recommended for market participants to join the coveted team of data analysts to harness the true potentials of data.
In India the leading data analyst training institute in Delhi, NCR is DexLab Analytics which offers industry-oriented training courses in data analysis, Big Data Hadoop and other statistical analysis tools used in the market currently.
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As per a recent survey, the training wheels of data science mostly turn to Python. Most industry experts revealed that Python remains to be the no. 1 tool in data science. Many even suggest that R programming skill is turning out to be the top dog in the data industry today after Python.
While many argue that there is no reason to believe that Python’s reign on the data world will not last. But also these are the same people who only thought so, when data science was a place for PhD-holding propeller-heads. Now data science is a mainstream industry, with fresh recruits from a varied range of fields. Initially Python was thought to have the widest range of utilities, but newer and more advanced technologies are cropping up every day.
With data science and management slowly emerging to be elemental in all industries, so is R programming.
Why Python is being swallowed?
It is a common notion that comparing two different programming languages is whimsical as each has their separate “use scenarios”. For instance, it might still seem plausible comparing C++and Swift, but it may not be very informative on the font of revealing new news.
Similar is the case for comparing Python with R programming; both are used by data scientists for data analysis. But R software was initially developed keeping the needs of statisticians. While on the other hand, Python has a more generalized purpose. Earlier in the industry Python had the most number of job opportunities, with its usability in web applications and other such similar uses.
But there has been a sudden change in preferences within the industry which is interesting to note, that led to the sudden rise in the popularity of R programming.
As per the multifaceted ranking from IEEE spectrum, the topmost 5 programming languages currently are – Java, C, C++, R and Python. Another interesting fact to note about R programming language is that it rose from the 9th to the 6th position within a single year. So, it is understandable that it is slowly emerging into the top ranking programming languages now.
Are experts snacking on Python first and then feasting on R?
Many experts expect that there will be an impending fusion in the realm of big data with the melding of R and Python. While Python is the generalist language for developers, but R is a data experts’ language, for those who know their way in data. Many had the question if both R and Python would be in use in the long-run, are both such languages useful in the future.
Today we have the answer to this question as the biggest experts in the field have claimed that more often than not R and Python are used together. But still there is ample reason to believe that R will soon take over the data science world over Python. Because with time, as data planning, management and analysis becomes invaluable in businesses regardless of the department of operation, R will also gain greater popularity rather than Python. And that is not only the forecast for data science industry but overall in the corporate world.
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We at DexLab Analytics conducted a survey, where we asked some of the tech savviest professionals associated with the ITES/Telecom/Marketing/BFSI fields, if they look to change their line of work in the near future and if they do, what will it be? Most of their response was to get into – Business Analytics.
But we have also noticed that although many are ready to take up Business Analytics for their career, when the time comes to take the big leap they are frazzled with a million questions that leave them confused. So, to help give such professionals some direction while taking such important decisions, here we have answered some of the most commonly arising question in the minds of students.
Firstly let us start with the very basics of the subject. What essentially is Business Analytics? Although this question has been answered by us numerous times; to put it simply it is the logical way we make decisions or the way we use logic.
For better understanding imagine a scenario, you are at the mall for shopping for a new dress. Some stores at the mal are offering a sale while some aren’t. And like all malls this one also has diverse range of retailers from budget-grade to superior high-quality ones. Now how do you decide on a dress?
You and almost everyone else take a decision based on the available information or data. How do you find data? You gather them by perusing around the mall visiting different shops and looking at the available options. But what if all the data is available, are there any risks associated with available data then? Is decision making completely risk-free? In such a scenario, even if information or data is available to us, they are usually not very conclusive or not present in their entirety. So, it might help to analyze the data to reduce the risk of taking wrong decisions.
While this might seem like an overly complex process and unnecessary burden to take care of when just shopping for a plain old dress, but imagine making high-stake decisions in similar or even more complex settings at business. Sounds daunting don’t it? That is the main use of Business Analytics. And as long as the mountainous heap of data keeps growing in the industry, the work of a Business Analyst will not dry-out.
The important tools in business analytics today are:
- R programming
- Advanced MS Excel (using macros and VBA)
- Tableau/Spotfire/Qlikview etc.
Where can you learn Business Analytics?
The analytics training institutes in Delhi are the leading organizations in this sector much like B-schools. And with the real estate boom in the NCR regions of Gurgaon many reputed organizations are expanding their chains to such locations. DexLab Analytics is a premium analytics training institute that caters industry-specific training to data driven minds with the best in class faculties.
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Many programmers suggest that while they have developed software professionally in a plethora of programming languages, but the hardest language they have come across was R. While this statement may be debatable dividing the software developers’ community in the middle, as many others say that language is fairly easy to cope with. While the language may seem somewhat unconventional to learn initially, it is due to these factors that one with experience in languages like Java, Perl and C++ etc find it easier to handle. It has been developed keeping their abilities in mind.
What truly makes R programming stand-out from every other language is the fact that it is not just a programming language but also an environment for carrying out statistical analysis. Many experts suggest that they like to think that R is more of an environment consisting of a programming language component within that it being a programming language.
Most job sites these days are teeming with vacancies for R programmers, so it is highly recommendable to aspiring professionals to board the R train with a well-recognized R programming certification course.
When speaking about R programming it is safe to say, that is more like a scripting language for the R environment on similar lines as VBA is for MS Excel. This way some of the unconventional aspects of R can be explained when viewed in this perspective.
Understanding ‘sequences’ in R programming:
The reason behind using the expression seq(a, b, n) is used is to create a closed interval that starts from ‘a’ ends at ‘b’ and runs with step sizes of ‘n’. Taking a more realistic example, if we implement seq(1, 10, 3) returns with the following vectors – 1, 4, 7 and 10.
This command is somewhat similar to the range(a, b, n) in Python, except in Python only half-open intervals are used so, the vector 10 would not be included which was returned in case of the R example. The default step size augments in case of both R and Python is 1.
Boolean operators used in R:
The Boolean operators used in R are T or True for true values and F or False for false values.
As for the operators & and |, they are applied on the vectors element-wise. Conditional elements use && and || and they use lazy evaluations like in C. in such cases the operators do not use the second augment if the first augment works to determine the return value.
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The best way to begin speaking about how interesting the past year was for Big Data is to say that – there will be more.
By more we mean that there will be more data, thus, there will be more data scientists, data analysts, more cloud analytics, more mobile analytics and data discovery. So, it is evident that Big Data will only get Bigger this New Year on 2016 with more data to visualize.
But if you are new in the world of Big Data Hadoop courses and are only beginning to acquaint yourself with Big Data, here are a few special trends of 2015 that you must keep a close eye on, this year.
You can make magic:
The technologies we use today are nothing short of sheer magic that, we could only imagine in our wildest dreams even a few years ago. But in the field of Big Data Hadoop and other data analytics tools, nothing such amazing has been noticed yet. We only know of tools and features that get the job done with some difficulty, but nothing that gives us data magic on our very fingertips. Thus, the world of data science still requires shepherds who would oversee and micromanage each and every step of data analysis. We still need people to manage aspects like – where to find data, how it should be stored, how it should be stored and most important how to analyze it and what conclusions to draw from the analysis.
Experts suggest that all this would soon change with advanced technologies being incorporated into the world of analysis to further automate the process. Things like machine learning with other advanced analytics tools are being applied to data science to further upgrade the process.
Insiders now want advanced tools that help them take care of the trivialities fast and get to analysis sooner with facilities like – simply pointing them at the data and allowing the algorithms to figure out things like how to join the data, propose complementary data, and cleanse it and to optimize it to determine it should be stored. Thus, 2016 will see more developments in this aspect of advanced automation.
Make way for multi-polar analytics:
Nowadays the layer cake approach of model analytics is slowly becoming obsolete and a practice that was thought of as the best – with external data feeding data marts and warehousing data with Bi features, as the final touch is fast being replaced with a multilayered or multi-polar approach. So, this New Year we will gain a better idea of how we can get better results from realistic yet complex data analysis centres.
Data privacy laws:
Currently there is a huge room for development in the world of data science contrary to the rate of development in the technologies. Already there have been reports of serious abuses with important data and we expect that 2k16 will see some strict actions in encryption practices in data.
In conclusion, 2016 will be a wonderful year for data analytics with increased adaptability.
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