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Tag: Analytics

Statistics For Data science

Posted on by Imarticus Learning

Data Science is the effective extraction of insights and data information. It is the science of going beyond numbers to find real-world applications and meanings in the data. To extract the information embedded in complex datasets, Data Scientists use myriad techniques and tools in modelling, data exploration, and visualization.

The most important mathematical tool of statistics brings in a variety of validated tools for such data exploration. Statistics is an application of mathematics that provides for mathematical concrete data summarization. Rather than use one or all data points, it renders a data point that can be effectively used to describe the properties of the point regarding its make-up, structure and so on.

Here are the most basic techniques of statistics most popularly used and very effective in Data Science and its practical applications.

(1) Central Tendency

This feature is the typical variable value of the dataset. When a normal distribution is x-y centered at (110, 110) it means the distribution contains the typical central tendency (110, 110) and that this value is chosen as the typical summarizing value of the data set. This also provides us with the biasing information of the set.

There are 2 methods commonly used to select central tendency.

Mean:

The average value is the mid-point around which data is distributed. Given 5 numbers here is how you calculate the Mean. Ex: There are five numbers

Mean= (188 2 63 13 52) / 5 = 65.6 aka mathematical average value used in Numpy and other Python libraries.

Median:

Median is the true middle value of the dataset when it is sorted and may not be equal to the mean value. The Median for the sample set requires sorting and is:

[2, 13, 52, 63, 188] → 52

The median and mean can be calculated using simple numpy Python one-liners:

numpy.median(array)

numpy.mean(array)

(2) Spread

The spread of data shows whether the data is around a single value or spread out across a range. If we treat the distributions as a Gaussian probability figure of a real-world dataset, the blue curve has a small spread with data points close to a narrow range. The red line curve has the largest spread. The figure also shows the curves SD-standard deviation values.

Standard Deviation:

This quantifies the spread of data and involves these 5 steps:

1. Calculate mean.

2. For each value calculate the square of its distance from the mean value.

3. Add all the values from Step 2.

4. Divide by the number of data points.

5. Calculate the square root.

Made with https://www.mathcha.io/editor

Bigger values indicate greater spread. Smaller values mean the data is concentrated around mean value.

In Numpy SD is calculated as

numpy.std(array)

(3) Percentiles

The percentile shows the exact data point position in the range of values and if it is low or high.

By saying the pth percentile one means there is p% of data in the lower part and the remaining in the upper part of the range.

Take the set of 11 numbers below and arrange them in ascending values.

3, 1, 5, 9, 7, 11, 15,13, 19, 17, 21. Here 15 is at the 70th percentile dividing the set at this number. 70% lies below 15 and the rest above it.

The 50th percentile in Numpy is calculated as

numpy.percentile(array, 50)

(4) Skewness

The Skewness or data asymmetry with a positive value means the values are to the left and concentrated while negative means a right concentration of the data points.

Skewness is calculated as

Skewness informs us about data distribution is Gaussian. The higher the skewness, the further away from being a Gaussian distribution the dataset is.

Here’s how we can compute the Skewness in Scipy code:

scipy.stats.skew(array)

(5) Covariance and Correlation

Covariance

The covariance indicates if the two variables are “related” or not. The positive covariance means if one value increases so do the other and a negative covariance means when one increases the other decreases.

Correlation

Correlation values lie between -1 and 1 and are calculated as the covariance divided by the product of SD of the two variables. When 1 it has perfect values and one increase leads to the other moving in the same direction. When less than one and negative the increase in one leads to a decline in the other.

Conclusion: 

When doing PCA-Principal Component Analysis knowing the above 5 concepts is useful and can explain data effectively and helps summarize the dataset in terms like correlation in techniques like Dimensionality Reduction. Thus when more data can be defined by a median or mean values the remaining data can be ignored. If you want to learn data science, try the Imarticus Learning Academy where careers in data science are made.

Retail Analytics – How Does It Help Boost The Sales?

Posted on by Imarticus Learning

 

SMB retailers benefit in three main ways from retail data analytics. 

1. Knowing Customers:

Singapore’s Dish-the-Fish fish-stall uses inventory and sales analytics on Vend’s retail management platform and cloud-based POS. Owner Jeffrey Tan prior to switching to the platform, bought what he thought to be the fastest selling fish the ikan kuning. On tracking data by the hour on different fish sales frequencies on Vend’s POS system, he found the leatherjacket fish was fast-selling though pricier. Monitoring in real-time also gave Tan the data-analytics ability to track and cater to the preferences and tastes of his clients. According to data from Accenture, 65% of clients buy from brands that know their brand preferences and buying history.

2. Analyzing Trends:

To use data analytics effectively one must know when and what the customers want even before they produce it. Just look at Dash a fashion store! The store’s Retail Director, Dakota DiSanto, admitted that before switching to LightSpeed’s POS system, her staff spent as many as 8 man-hours per week on studying and tracking manually the sales, inventory, re-order items and so on. According to her the real-time inventory view, sales trends and stock levels across their operational stores in Miami and Los Angeles provided them crucial information on the best sellers, re-ordered units, inventory scheduling, etc well ahead of the demand.

3. The True Costs:

Marquis Gardens’ Ostap Bosak, the General manager, used ACCEO’s POS system. Being Toronto based pond-supply retailers he made use of the transport insights from data analysis on their retail operations. Here’s his story.

On evaluating his data he dropped several suppliers as he found they were earning too little and working too much on them. Though they formed a major portion of the revenue generations the sidelined products were more profitable. He then focused on the main two generators of revenue namely the small pond kit and the pond-less waterfall kit. Bosak stated that he was able to better monitor the ROI from his data analysis as it enabled him to watch over the metric of profit with respect to time spent and efforts spent on it. Bosak reasserts that most businesses do not account for the actual man-hours taken while calculating profitability. In his opinion retail data-analytics helps drill into data in greater minute details to help sustain your operations in a fiercely competitive market.  

Which metrics should one analyze?

Analysis of KPIs like foot-fall traffic, margins, sales growth, and walk-in rates speak the numbers-story of any enterprise with accuracy and transparency to enable your making profitable decisions with those data analytics insights. Here are the metrics in retail business analytics every store must and should monitor.

  1. The square foot rate of sales 
  2. Rate of Retail Conversion 
  3. Net Margins on profit

1. Sales/SqFt:

This index helps. Because, when you know exactly how many sales you earn per sqft of space you can assess and gauge the store’s performance to

  • Refurbish your retail layout: Express rearranged the layout of its store bringing its merchandise selling at full-price to the front and taking the other discounted apparel to the rear-end, based on its analytics and trends in sales. The results showed in a spurt of sales in the more profitable full-price range.
  • Pile up effectively: The fashion boutique Covet’s owner Adrienne Wiley cautions retailers to carefully monitor sales data when they decide on inventories and range of products to sell. She benefited by stocking up the necklaces and tweaking the sales/hour figures in her data analytics analysis.

2. Rate of Retail Conversion:

Browsers are common and this metric gauges how many of them you convert into sales or buyers of your merchandise. So, why study and analyze data when you cannot use it. Right? No, wrong! Here’s what to do with it. 

  • Figure out why customers buy and what keeps them from buying: Your low sales could result from poor displays, long billing times, lack of sales reps or customers not finding what they want. If you take the time to speak to and observe customers respond you can find ways to make their journey more pleasurable and this would result in repeat sales and loyal customers.
  • Set goals and train your employees: Employees are an organizational asset. Train employees to make the customer experience good using goal-setting, loyalty-rewards and incentives. The Friedman Group Founder Harry Friedman claims training helps retail organizations push sales 15-25%.

Wrapping up, there can be no doubt that data analytics enables boosting sales. So, do a course in data analysis with Imarticus Learning. They get you career-ready from day one in a variety of interesting subjects.

Analytics and Agriculture

Posted on by Imarticus Learning

Agriculture drives the Indian economy with a whopping population of nearly 70% in rural areas and 40% being part of the agricultural workforce. However, it has many issues and hurdles in realizing its full potential and leveraging analytics and technology for it. The sector lacks banking, financial, disaster management, and water inadequacy facilities and infrastructure. Also due to lack of education migration to cities is a major issue. Though in the early stages the policymakers were quick to realize the potential of analytics and technology in mitigating the hardships of farmers and slowly but steadily the combination is appearing to slow down and address the agriculture segment pressing issues.
Use of Big Data Analytics:
Data is the life breath of all activities in modern times and in agriculture too. Leveraging the potential of analytics and Big Data can bring about immense changes in agriculture and its productivity. The frequent news-releases on droughts, crop failures, farmer suicides and such acute results of backward farming and agriculture stresses the need for the involvement of technology and big data in improving the lot of the farmers and agriculture segment. Be it crop patterns, wind directions, crop loss mitigation, soil adequacy, and fertility, it is Big Data analytics that has offered solutions using technologies like

  • Cloud and Nanocomputing
  • Big data, digitalization and visualization use.
  • AI, IoT and ML use.
  • Saas Platforms, cloud services, and web-based apps.

Role of data and the data analyst:

Agriculture is interdisciplinary and combines concepts of business management, chemistry, mathematics, statistics, physics, economics, and biology. Like all interdisciplinary sectors, the need for data and its use is crucial for growth, change, and development. This means that like in other segments the data analyst role is both well-paying, has an unending scope and relies on a variety of latest futuristic technologies and smart apps.
Knowledge of sciences, agriculture methods, biotechnology, animal and soil sciences, etc will definitely aid the analyst. The analyst will also need proficiency in analysis techniques, data prepping and predictive analysis.
Analytical technologies in the agriculture sector can be used effectively in 

  • Capturing data: using the IoT, biometrics, sensors, genotyping, open and other kinds of data, etc.
  • Storage of Data: using data lakes, Hadoop systems, Clouds, Hybrid files and storage, etc.
  • Transfer of Data: via wireless and wifi, linked free and open source data, cloud-based solutions, etc.
  • Analytics and Transformation of data: through ML algorithms, normalization, computing cognitively, yield models, planting solutions, benchmarks, etc.
  • Marketing of data and its visualization.

What is Smart Farming?

Smart Farming uses analytics, IoT, Big Data and ML to combine technology and agriculture applications. Farming solutions also offer

  • ML and data visualization techniques.
  • App-based integration for data extraction and education.
  • Monitoring through drones and satellites.
  • Cloud storage for securing large volumes of data.

Smart Farming technologies and analytics can thus be efficiently used for forecasts, predictions for better crop harvests, risk mitigation, and management, harvest predictions, maximizing crop quality, liaising and interconnectivity with seed manufacturers, banks, insurers, and government bodies.

What is Precision Agriculture?

This methodology is about Crop Management which is site-specific and also called ‘Farming using Satellites’. The information from satellites helps distill data regarding topography, resources, water availability, the fertility of the soil, nitrogen, moisture and organic matter levels, etc which are accurately measured and observed for a specific location or field. Thus an increase in ROI and optimization of resources is possible through satellite aided analytics. Other devices like drones, image files from satellites, sensors, GPS devices, and many more can prove to be helpful aids and are fast becoming popular.

Concluding with the challenges:

Though the technologies are the best the implementation and applications to the agriculture sector are lacking. Education and training of the farmers is the best solution but involves a lot of man-hours, uninterrupted power, use of data efficiently, internet connectivity, and finance to help these measures succeed and develop to their full potential. Right now it is in the nascent stage and the need for data analysts is very high.  To get the best skill development training courses in data analytics do try Imarticus Learning which is a highly recommended player with efficient, practical skill-oriented training and assured placements as a bonus. Where there is a will the way will show up on its own. Hurry and enroll.

Sandeep’s Review of Imarticus’ Data Science Course

Posted on by Imarticus Learning

We caught up with Sandeep, a recent graduate of the Post Graduate program in Analytics, for a quick chat to get his perspective on the program, the curriculum, Imarticus Learning’s placement process and more.
Tell us a little bit about yourself.

Sandeep: My name is Sandeep Singh. I recently completed my B.Sc. in Computer Science and was looking for an avenue to enhance my analytics skills and start my career.

Data Science Course in MumbaiI came across Imarticus’ data science course and, after thorough research, decided to enroll for it. I completed the course and have been placed at M Technologies through Imarticus.

How has your experience been with Imarticus Learning?
Sandeep: My experience with Imarticus Learning was super! The course focused on practical training with hands-on learning of various analytical tools and thorough practice with numerous datasets.

Looking back, I see the importance of actually applying Analytical tools and techniques to the projects I worked on because it gave me a running start when I began working.

What has changed since you joined Imarticus Learning?
Sandeep: Since the day I joined Imarticus my confidence has been boosted to a very high level. Through the practice of various analytical tools such as R, Python, SAS, Tableau, etc. I’ve come to believe in myself. My soft skills have also been elevated with the help of business communication workshops, mock interviews, and soft skill sessions throughout the course.

Would you recommend the program to someone else?
Sandeep: While researching various institutes, I came across some reviews that say Imarticus Learning is fake. Well, I wanted to see for myself and now that I have, I would definitely recommend Imarticus. If you’re looking for an institute, the first thing that comes to mind is the faculty and the learning material.

The faculty and staff are very cooperative and help you both inside and outside the classroom. The learning material is extensive and covers every aspect of data analytics. The best part is all of the lectures, notes, datasets, and quizzes are stored in an online Learning Management system and is available to students anytime, anywhere.

What do you like most about Imarticus?
The best thing about Imarticus Learning was the course content, the cooperative staff and the informative notes that are easily accessible. The resume building workshops and mock interviews definitely prepared me for the placement drives and I was able to crack the interview and land a job at M Technologies.

Looking to get started on your data science career, Speak with a counselor and get matched with the best course for you.

How Business Analysts Can Identify and Reduce Sales Gaps?

Posted on by Imarticus Learning

 

Sales are worked for and don’t happen by chance. The marketing and sales departments need the sales funnel, the pipelines and effective strategy to translate them into currency. Your Business Analyst can help you grow your sales as these great marketers show you the way.

Why sales persons need a BA?

CEO Warren Kurzrock, of Porter Henry a sales-training consultancy says that salespersons don’t have enough time to track their activities, leads, and efforts. The solution lies in the statement of Aventri’s market strategy director John Kearney that you will be able to spot patterns and figure out why your leads turn into a loss, win or no go when you look through the data analysis lens and analyze performance. It helps you measure performance in terms of a lose-win ratio during the whole cycle of sales.

Motivation through effective reviews:

In the words of CEO Matthew Cook of SalesHub, it is the sales team’s motivation that drives your bottom-line, productivity, and company culture. If this is true then BA can drive the levels of sales-adrenaline up through effective reviews to help conversions. Who needs better motivation?

The gap-analysis puzzle:

Consultant Catherine Yochum at ClearPoint stresses the need to extrapolate the present state to the future goal to understand the process and resources to scale them to the future. The first step to improve your sales process through analysis is to conduct a gap analysis. This is the process of reviewing your resources and processes and predicting how they will scale into the future. 

Chief Editor Aaron Orendorff of Shopify Plus advises a gap analysis of potential Vs performance could outstrip expectations. They turn businesses to finding the right factors of success rather than dealing with solutions to problems. That for you is a solution that can be scaled and extrapolated.

Solve the cold-leads issue:

B2B marketer Amanda Nielsen of New Breed Marketing specifies the solution lies in attaching weights to qualify leads potential into levels like 

  • Marketing-Qualified ones worth the investment
  • Sales-Qualified ones ready-to-reach
  • Worth-talking-to opportunities

Opportunities mean clients. And clients mean business and brand-ambassadors.

Copy-what-works strategy:

The whole purpose of BA is to find the winning strategy which reduces leads loss, generates better strategies and provides you with insights and training on why errors occur and how not to replicate them. A satisfied customer is akin to asset acquisition.

Empower your staff:

Motivation runs high when you can forecast efficiently and juggle the leads to improve the sales funnel through an effective strategy. BA provides your sales and marketing team with the tools to succeed. It shows them how to achieve and equips them with the required timely knowledge and strategy.

In conclusion, BA sets the pace, helps you with timely strategy, reviews, causes for failures and helps literally set the timetable for work. That’s as easy as it can make your job. One must, however, being in sales convert those leads with people-skills that only the human touch can add value to.

Future of Big Data Hadoop Developer in India

Posted on by Imarticus Learning

In this era of electronic and digital devices, most people are using Big Data, ML, AI and such without really understanding what goes on to provide those services. Data is at the very center of any application and the sheer volumes of data generated, the variety of sources and formats, the need to manage, clean, prepare and draw inferences for business purposes and making decisions is being used extremely widely. And this spawning of data, means the projects involve Big Data and that technology has to evolve and changes to manage it. This also indirectly implies the need for Hadoop developers. The relationships are symbiotic and spur growth in each other’s needs.

Why Choose Big Data Hadoop As a Career

• Since data is an asset people trained on handling the large amounts of data performing analytics on it and providing the right gainful assets for business decisions are also fast being considered invaluable assets.
• Those employees who do not re-skill to include managing Big Data face the risks of getting laid off. For example, TCS, Infosys, and many other data giants laid off nearly 56,000 people in just one year.
• 77% of the companies and verticals across industries are adapting to use Big Data. Thus many are recruiting data analysts and scientists. Even the non-IT sector!
• The payouts are second to none in the category and a large number of aspirants are taking up formal Hadoop careers, both newbies and those changing careers mid-way.
• Data is growing and will continue to be used even in the smallest of devices and applications creating a demand of personnel to handle Big Data.

The Hadoop Career Choice

Pros:
• Big data applications and demand for trained personnel shows tremendous growth.
• Job scope is unending since data continues to grow exponentially and is used by most devices today.
• Among the best technology for managing Big Data sets Hadoop scores as the most popular suite.
• The salaries and payouts globally are better than for other jobs.
• Most verticals and industries, a whopping 77%, are switching tracks to use Big Data.
• Hadoop is excellent at handling petabytes of Big Data.
Cons:
• Your skills need to be of practical nature and constantly updated to keep pace with evolving technology.
• You need a combination of skills that may require formal training and is hard to assimilate on your own before you land the job.

How to Land that Dream job

Today it would be exceptional if a company does not use Hadoop and data analytics in one form or the other. Among the ones that you can easily recollect are New York Times, Amazon, Facebook, eBay, Google, IBM, LinkedIn, Spotify, Yahoo!, Twitter and many more. Big Data, Data Analytics, and Deep Learning are widely applied to build neural networks in almost all data-intensive industries. However, not all are blessed with being able to learn, update knowledge and be practically adept with the Hadoop platform which requires a comprehensive ML knowledge, AI deep learning, data handling, statistical modeling and visualization techniques among other skills.
One can do separate modules or certificate Big-Data Hadoop training courses with Imarticus Learning who provide such learning as short-term courses, MOOCs, online classrooms, regular classrooms, and even one-on-one courses. Choices are aplenty with materials, tutorials and options for training being readily available thanks to high-speed data and visualization made possible by the internet.
Doing a formal Hadoop training course with certification from a reputed institute like Imarticus Learning helps because: 
• Their certifications are widely recognized and accepted by employers.
• They provide comprehensive learning experiences including the latest best practices, an updated curriculum, and the latest training platforms.
• Employers use the credential to measure your practical skills attained and assess you are job-prepared.
• It adds to your resume and opens the doors to the new career.
• Knowledge in Big Data is best imbibed through hands-on practice in real-world situations and rote knowledge gained of concepts may not be entirely useful.
The best courses for Big data Hadoop and Advanced Analytics are available at the IIMs at Lucknow, Calcutta, and Bangalore at the IITs of Delhi and Bombay. This is an apt course for people with lower experience levels since their curriculum covers a gamut of relevant topics in-depth with sufficient time to enable you to assimilate the concepts.
The Big data training courses run by software training institutes like Imarticus are also excellent programs which cost more but focus on training you, with the latest software and inculcating practical expertise. Face-to-face lab sessions, mandatory project work, use of role-plays, interactive tutoring and access to the best resources are also very advantageous to you when making the switch.
Job Scope and Salary Offered:
Persons with up to 4 years experience can expect salaries in the range of 10-12 lakhs pa at the MNCs according to the Analytics India Magazine. Yes, the demand for jobs in this sector will never die down and is presently facing an acute shortage.
Hadoop Course Learning:
You can use online resources and do it yourself using top10online courses.com. However, formal training has many advantages and is recommended. Join the Hadoop course at a reputed institute like Imarticus Learning.
Hadoop has a vast array of subsystems which are hard to learn for the beginner without formal training. The course helps you assimilate the ecosystem and apply these systems to solving real-world industry-related problems in real-time through assignments, quizzes, practical classes and of course do some small projects to show off your newly acquired skills. The best part is that you have certified trainers leading convenient modes and batches to help you along even if you are already working.
The steps that follow are the Hadoop progressive tutorial in brief.
• Hadoop for desktop installation using the Ambari UI and HortonWorks.
• Choose a cluster to manage with MapReduce and HDFS.
• Use Spark, Pig etc to write simple data analysis programs.
• Work on querying your database with programs like Hive, Sqoop, Presto, MySQL, Cassandra, HBase, MongoDB, and Phoenix.
• Work the ecosystem of Hadoop for designing applications that are industry-relevant.
• Use Hue, Mesos, Oozie, YARN, Zookeeper, and Zeppelin to manage your cluster.
• Practice data streaming with real-time applications in Storm, Kafka, Spark, Flume, and Flink.
• Start building your project portfolio and get on GitHub.
Conclusion:
In parting, India and the bigger cities like Bangalore, Hyderabad, and Mumbai are seeing massive growth in the need for Hadoop developers. You will also benefit from a Hadoop training course in Data Analytics and it is worth it when your certification helps you land the dream career you want. So don’t wait. Take that leap into Hadoop today!

Top tips on how to apply data analytics in your project

Posted on by Imarticus Learning

Data science and analytics are two extremely useful tools that can give accuracy to your project and help automate repetitive tasks. With the demand and scope of data analytics growing with each passing day, companies are trying to integrate everything and get as much information on it as they can.

Data science techniques and analysis are quite helpful because they can be used to enhance the decision-making capacity of your manager, predict future revenues, understand market segments, and produce better content. In the healthcare sector, this technology can be used to diagnose patients correctly.

But how do you integrate data analytics into your professional projects? For that, a sound knowledge of the same is required. Even if you learn the basics of data analytics, it will give a major boost to your career. The entire world is moving towards digitization, and so data analytics is required to gather, analyse, and make sense of the data in front of you.

In order to become an expert in data analytics, and incorporate it seamlessly into your project, you need to have a data analytics training.There are many data analytics courses that you can take for a better understanding of data science and analysis. Here is a list of some of the best data analytics courses available online.

  • Introduction to Data Science

This data analytics training course requires a basic understanding of R programming language and provides an in-depth insight into the necessary tools and concepts used in the data science industry. They also work with powerful techniques for analyzing data and use real-world examples to help you gain clarity over the concepts.

  • Applied Data Science with Python

It is being offered by the University of Michigan. It aims to introduce learners to the specialized version of data science through Python. It is for learners with an understanding of Python, and want to expand their knowledge by incorporating the essentials of statistical,machine learning, information visualization, text analysis, and social network analysis techniques into their projects.

  • The Python Mega Course: Build 10 Real World Applications

This data analytics training is aimed at people with no background of Python, but are interested in learning basic as well as advanced skills of Python and data analysis. It is for people with no previous or little programming experience.

It does not rely on a lot of theoretical teaching but focuses instead on giving problems to the students that they can solve by doing. This course uses video, quizzes, real-world examples to familiarize learners with Python in the beginning and then enhance their skills later.

  • Social Media Data Analytics

This is one of the best data analytics courses available online that especially caters to social media. It is for people who want to use their data analysis skills to get the best out of social media.This course involves giving assignments and mini-projects, which would require you to use your data analytics skills to leverage your social media presence.

Advantages Of R Programming Language

Posted on by Imarticus Learning

R is a programming language, mainly dealing with the statistical computation of data and graphical representations. Many data science experts claim that R can be considered as a very different application, of its licensed contemporary tool, SAS. This data analytics tool was developed at Bell Laboratories, by John Chambers and his colleagues.
The various offerings of this tool include linear and non-linear modeling, classical statistical tests, time-series analysis, clustering, and graphical representation. It can be referred to as a more integrated suite of software facilities, for the purpose of data manipulation, calculation and data visualization. The R environment is more of a well-developed space for an R programming language, inclusive of user-defined recursive functions as well as input and output facilities. Since it is a relatively new data analytics tool in the IT-sphere, it is still considered to be very popular amongst a lot of data enthusiasts.

There are a number of advantages of this data analytics tool, which make it so very popular amongst Data Scientists. Firstly, the fact that it is by far the most comprehensive statistical analysis package available totally works in its favor. This tool strives to incorporate all of the standard statistical tests, models, and analyses as well as provides for an effective language so as to manage and manipulate data.
One of the biggest advantages of this tool is the fact that it is entirely open sourced. This means that it can be downloaded very easily and is free of cost. This is mainly the reason why there are also communities, which strive to develop the various aspects of this tool. Currently, there are about some 19 developers, including practicing professionals from the IT industry, who help in tweaking out this software. This is also the reason why most of the latest technological developments, are first to arrive on this software before they are seen anywhere else.

Why Learn R Programming

When it comes to a graphical representation, the related attributed to R are extremely exemplary. This is the reason why it is able to surpass most of the other statistical and graphical packages with great ease. The fact that it has no license restrictions, makes it literally the go-to software, for all of those who want to practice this in the earlier stages. It has over 4800 packages available, in its environment which belong to various repositories with specialization in various topics like econometrics, data mining, spatial analysis, and bioinformatics.
The best part about R programming is that it is more of a user-run software, which means that anyone is allowed to provide code enhancements and new packages. The quality of great packages on the R community environment is a testament to this very approach to developing certain software by sharing and encouraging inputs. This tool is also compatible across platforms and thereby it runs on many operating systems as well as hardware.
It can function with similar clarity for both the Linux as well as Microsoft Windows Operating Systems. In addition to this, the fact that R can also work well with other data analytics tools like SAS, SPSS and MySQL, have resulted in a number of takers for this data analytics tool. Imarticus Learning The Data Science Prodegree powered by KPMG is one such course which offers both SAS and R along with the opportunity to be a Data Scientist at KPMG.