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Decoding the Equation of AI, Machine Learning and Python

Decoding the Equation of AI, Machine Learning and Python

AI is an absolute delight. Not only is it considered one of the most advanced fields in the present computer science realm but also AI is a profit-spinning tool leveraged across diverse industry verticals.

In the past few years, Python also seems to be garnering enough fame and popularity. Ideal for web application development, process automation, web scripting, this wonder tool is a very potent programming language in the world. But, what makes it so special?

Owing to ease of scalability, learning and adaptability of Python, this advanced interpreted programming language is the fastest growing global language. Plus, its ever-evolving libraries aid it in becoming a popular choice for projects, like mobile app, data science, web app, IoT, AI and many others.

Python, Machine Learning, AI: Their Equation

Be it startups, MNCs or government organizations, Python seem to be winning every sector. It provides a wide array of benefits without limiting itself to just one activity – its popularity lies in its ability to combine some of the most complex processes, including machine learning, artificial intelligence, data science and natural language processing.

Deep learning can be explained as a subset of a wider arena of machine learning. From the name itself you can fathom that deep learning is an advanced version of machine learning where intelligence is being harnessed by a machine generating an optimal or sub-optimal solution.

Combining Python and AI

Lesser Coding

AI is mostly about algorithms, while Python is perfect for developers who are into testing. In fact, it supports writing and execution of codes. Hence, when you fuse Python and AI, you drastically reduce the amount of coding, which is great in all respects.

Encompassing Libraries

Python is full of libraries, subject to the on-going project. For an instance, you can use Numpy if you are into scientific computation – for advanced computing, you have put your bet on SciPy – whereas, for machine learning, PyBrain is the best answer.

A Host of Resources

Entirely open source powered by a versatile community, Python provides incredible support to developers who want to learn fast and work faster. The huge community of web developers are active worldwide and willing to offer help at any stage of the development cycle.

Better Flexibility

Python is versatile. It can be used for a variety of purposes, right from OOPs approach to scripting. Also, it performs as a quintessential back-end and successfully links different data structures with one another.

Perfect for Today’s Millennial

Thanks to its flexibility and versatility, Python is widely popular amongst the millennials. You might be surprised to hear that it is fairly easier to find out Python developers than finding out Prolog or LISP programmers, especially in some countries. Encompassing libraries and great community support helps Python become the hottest programming language of the 21st century.

Data Science Machine Learning Certification

Some of the most popular Python libraries for AI are:

  • AIMA
  • pyDatalog
  • SimpleAI
  • EasyAI

Want to ace problem-solving skills and accomplish project goals, Machine Learning Using Python is a sure bet. With DexLab Analytics, a recognized Python Training Center in Gurgaon, you can easily learn the fundamentals and advance sections of Python programming language and score goals of success.

 

The blog has been sourced from ― www.information-age.com/ai-machine-learning-python-123477066

 


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Top Python Interview Questions You Should Start Preparing RN

Top Python Interview Questions You Should Start Preparing RN

Welcome! May be, you are new to programming altogether or seasoned in the field; either way, you have come to the right place, and have chosen the right language. Python is a very powerful, advanced kind of object-oriented programming language. It is simple and follows an easy to use syntax. Therefore, it has stood out to be the most popular programming language to learn and master, even by those who has set foot recently into the world of computer programming.

So, if you are planning to kickstart a career in Python, pore over these frequently asked questions; they are often asked in job interviews. If you have any doubts regarding Python, feel free to ask us, or comment below.

Or you can also take up our Python Course in Delhi NCR. It’s industry-related and student-friendly.

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What is Python?

Highly readable, interactive and interpreted, Python is an object-oriented scripting language. It focuses on English language, while its competitors use punctuations more.

What are the key features of Python?

  • Dynamically typed – In Python, you don’t have to state the variable kinds. You can easily run x=111  and then x="I'm a string" without error.
  • Interpreted language – This means Python doesn’t have to be compiled before running. PHP and Ruby are other interpreted languages.
  • Object oriented programming – Python is highly suitable for such kind of programming. It lacks access specifiers (like C++’s public, private>).

What role does PYTHONPATH environment variable plays?

PYTHONPATH works similar to a PATH. It helps Python interpreter in locating the module files imported into a program. It includes Python source library directory as well as the directories comprising the Python source code.

Name the supported data types in Python.

Python has 5 supported data types:

  • Numbers
  • List
  • String
  • Dictionary
  • Tuple

Do you know how to display contents of text file in a reverse order?

  • Change the given file into a list,
  • Then reverse the list by using reversed()
  • Eg: for line in reversed(list(open(“file-name”,”r”))):
  • Print(line)

Pick out the invalid statement from below.

  1. a) abc = 1,000,000
  2. b) a b c = 1000 2000 3000
  3. c) a,b,c = 1000, 2000, 3000
  4. d) a_b_c = 1,000,000

 Answer: b

How do you manage memory in Python?

  • Python private heap space manages its memory. All Python items and data structures are stored in a private heap. The programmer is not given access to this, but an interpreter manages this heap.
  • Pythom memory manager allocates Python heap space, while giving some access to the programmer for coding.
  • Python comprises of an inbuilt garbage collector – it recycles all the redundant memory, frees the extra memory and feeds it into the heap space.

What do you mean by Python dictionary?

Python dictionary is nothing but built-in datatypes. It entails a one-to-one relationship between keys and values. Dictionaries are indexed by keys; they contain pairs of keys and their corresponding values.

Hope this set of Python interview questions have prepared you well for upcoming job interviews. All the best!

Want to learn Python and its whole set of applications? Feel free to check out our interactive Python certification course training, offered by industry experts. They help transform your career.

 

The blog has been sourced from:

www.edureka.co/blog/interview-questions/python-interview-questions

intellipaat.com/interview-question/python-interview-questions

 

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Python Language for Embedded Applications

Python Language for Embedded Applications

Python is the need of the hour – not only for fueling websites but also for embedded applications.

Though C and C++ are still dominant programming languages for embedded applications, new age competitors, such as Python and Javascript are fast picking up pace. Especially Python: it’s gaining accolades for driving websites and Stack Overflow’s recent research has showcased the steady increase of popularity.

In terms of machine learning, Python is nowadays used with recommended platforms, such as TensorFlow and Caffe. It’s even used for fortifying neural networks.

The reason for such spiking popularity is it’s easy to download attribute – open source Python programming language can be downloaded for diverse platforms, including Windows and Linux. Moreover, several integrated development environments (IDEs) already exists for Python; some of the popular multiplatform tools are Microsoft’s Visual Studio and Eclipse (PyDev).

On the other hand, Python is reckoned as an embedded scripting language by wide motley of technical experts and consultants. Autodesk’s 3D animation program, Maya is programmed using Python. Similarly, Blender is also run on Python.

VDC Research highlighted spiked up interest in Python in IoT devices, “The embedded engineering community is embracing the use of scripting languages,” shares Chris Rommel, EVP of IoT & Embedded Technology research at VDC. “What began primarily isolated as a tool in the QA domain has quickly expanded within the software development ranks, with Python, in particular, showing incredible growth in the past few years,” he further adds.

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Python Graphics and User Interfaces

Python is loaded with a multitude of user-interface and graphics options. Developers, newbie and seasoned take advantage of Matplotlib: it’s a 2-D plotting library that offers a MATLAB-inspired interface. An open source KIVY framework is also used extensively. It can be run on a versatile range of platforms, such as Android,Linux, iOS, Windows, OS X, and the Raspberry Pi.

Qt is another very effective user-interface framework that’s high on popularity drive for over 25 years. Javascript, C++ and Python, all of them have relied on Qt for good. It specializes in handling graphics and different other multimedia formats as well as cameras and radios.

The Rise of Pythons for Embedded Systems

Python opens a world of opportunity, including providing support to numerous programming platforms and readable and manageable code. It eradicates the need to use brackets common to languages, such as C++, C and Java. Along with that, it enables an independent, interactive test-driven development approach.

All this sounds too alluring, isn’t it?

But wait, like all programming languages, Python too is bogged down by a few technical glitches. Running the application can sometimes become a bit tricky. Also, at times, Python may not be the perfect language for all embedded applications. Nevertheless, we cannot ignore the perks it ensures us: the benefits we derive from its dynamic nature, simplistic functions, flexibility and widespread support is incredible.

Data Science Machine Learning Certification

Of late, Python Data Science course in India is becoming increasingly popular. Join the bandwagon and get Python certified today!

 

The blog has been sourced from:

www.electronicdesign.com/embedded-revolution/python-s-big-push-embedded-space

 

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Python Is Gaining Popularity against SAS, R – Says Burtch Works

Python Is Gaining Popularity against SAS, R – Says Burtch Works

Python is on the rise – though R and SAS are languages of choice amongst the data scientists but R is soon ascending the steps of analytics ladder. Already a lot of practitioners and data scientists have armed themselves up with this incredible R Programming tool for future career aspirations. To add volume to the statement, we’ve a new survey from a high-end recruitment agency, Burtch Works – let’s see what their comprehensive report says about our preferred language.

The survey began with R, an open source tool and SAS, another commercial tool. Later in 2016, Burtch Works added another open source tool, Python.

This year, however we witnessed something that never happened before. There’s no clear winner, this time – Python stood at 33%, R at 33% and SAS at 34%. “This is the first year that we’ve seen SAS, R, and Python all at the same level of preference,” said Linda Burtch, a quantitative recruiting specialist and Managing Director at Burtch Works.

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According to the results, R declined slightly as compared to last year figure, whereas SAS remained fairly flat. On a positive note, Python continued reflecting an increasing trend over the last two years, since its inclusion.

“The most noticeable trend from the 2018 data was Python’s ascension, and how Python’s growing popularity has been eroding support for R,” Burtch shared with InformationWeek. “Data scientists have typically strongly preferred Python, but predictive analytics professionals working primarily with structured data are shifting that way as well.”

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But what makes Python so fetching? It is considered to be a very strong language for machine learning, perfect for data visualizations and other statistical applications, better than SAS and R. Budding professionals enjoy working with Python(48%) as compared to R(38%) and SAS(14%). Survey reveals that open source tools, such as R and Python are in-favor of professionals who are young and new in technology. 

Going by the survey results, the use of R has fallen drastically from 50% in 2016 to below 40% this year. At the same time, the growth of python has been phenomenal – in 2016, it was standing at 20% and this year, it is hovering around 50%.

“Python gained support in almost every category we examined this year and has especially taken hold at the early career level, with professionals who have five or less years of work experience,” Burtch concluded to InformationWeek.

As parting thoughts, Python is considered to be a very versatile programming language. Its popularity soared in recent years – its usage and employability knows no bounds. For beginners and newcomers, it’s like a treasure trove waiting to be discovered. So, if you are one of them, it’s high time to consider a Machine Learning Using Python certification program – easy to learn and highly accessible, Python programming is ideal to get started. Most importantly, its simplified syntax with an undue focus on natural language is an added bonus.

 

The blog has been sourced from – 

informationweek.com/big-data/ai-machine-learning/python-gains-on-sas-r/d/d-id/1332331

kdnuggets.com/2017/07/6-reasons-python-suddenly-super-popular.html

 

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The Nitty-Gritty When It Comes to SAS 101

The Nitty-Gritty When It Comes to SAS 101

SAS is a state-of-the-art business intelligence tool that is primarily designed to facilitate reporting, data analysis, mining and predictive modeling using convincing visualization and interactive dashboards. Being a powerful programming language, SAS performs complex statistical data analysis; unlike other built-in tools, like Microsoft Excel, SAS lets users to salvage and run data from a plethora of sources, along with ensuring enough control and freedom during data manipulation and compilation.

Statistical Analysis System (SAS) was introduced for organizations to explore their vast datasets in a highly interactive format. Today, SAS is largely used in machine learning, data science and business intelligence applications. Not only does it arms the organizations with the necessary tools and techniques to monitor key BI metrics, but also develops incredible insights and comprehensive reports, facilitating informed decision-making procedures.

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SAS Fuelling Career Growth

Business analytics and incredible BI tools have become central for running medium and large-scale enterprises across the globe, efficiently. With data becoming increasingly instrumental in pushing businesses to horizons of success, a majority of organizations is betting on SAS BI analytics.

As a result, the demand for SAS consultants is surging at an accelerating rate. Since more and more companies are adopting SAS analytics and altering the ways they used to work, SAS-related jobs are flooding the market. Handsome pay-packages are being offered to the right candidates, skilled and professional.

According to a recent study, the average salary of a diligent SAS programmer is around 10.8 Lacs – organizations are looking for professionals who would not only know how to slice and dice but also know how to draw the right projections and effectively communicate the insights. This is where SAS training Delhi comes in – Head-start a data journey with DexLab Analytics, as it offers the best SAS analytics training Delhi.

Books: For Enhancing the Level of SAS Knowledge

Besides encompassing SAS certification course modules, books tend to take us all a step closer to the bubbling pool of knowledge – SAS books are carefully written, specifically keeping in mind the requirements and focused areas of programmers and analysts.

Without any further ado, let’s dive into a well-curated list of SAS books that’ll help you ace the language like a pro:

 

  • SAS Essentials: Mastering SAS for Data Analytics by Elliott and Woodward – With an advanced approach, this book is perfect for master’s students of data analysis and programming and higher-level undergraduates.
  • SAS for Dummies by McDaniel and Hemedinger – An absolute beginner’s approach to SAS, this book is widely popular for its simple language, easier representation of facts and easy-to-follow guidelines.
  • The Little SAS Book by Delwiche and Slaughter – Ideal for beginners and experienced SAS consultants, as well, this book includes self-contained lessons, plenty of examples and interesting visuals.
  • SAS Certification Prep Guide – Released by the SAS institute, this is the final and official test-prep guide to be SAS certified.
  • Learning SAS by Examples: A Programmer’s Guide by Ron Cody – If you are a fast learner, this is the one for you. Each chapter in this book ends with test problems so that you are trained SAS-ready.

 

As final thoughts, SAS analytics is the most powerful tool for performing complex data analysis. Grasping the fundamentals of SAS language will surely present you a big leg up in the analytical domain. For SAS certification courses, drop by DexLab Analytics.

 

The blog has been sourced from –

https://www.whoishostingthis.com/resources/sas-programming

https://intellipaat.com/blog/what-is-sas-analytics

https://analyticsindiamag.com/analytics-india-salary-study-2017-by-analytixlabs-aim
 

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R Programming, Python or Scala: Which is the Best Big Data Programming Language?

R Programming, Python or Scala: Which is the Best Big Data Programming Language?

For data science and big data, R, Python and Scala are the 3 most important languages to master. It’s a widely-known notion, organizations of varying sizes relies on massive structured and unstructured data to predict trends, patterns and correlations. They are of expectation that such a robust analysis will lead to better business decisions and individual behavior predictions.

In 2017, the adoption of Big Data analytics has spiked up to 53% in companies – says Forbes.

The story of evolution

To start with, big data is just data, after all. The entire game-play depends on its analysis – how well the data is analyzed so as to churn out valuable business intelligence. With years, data burgeoned, and it’s still expanding. The evolution of big data mostly happened because traditional database structures couldn’t cope with such multiplying data – scaling data became an important issue.

For that, here we have some popular big data programming languages. Dive down:

R Programming

R Programming is mainly used for statistical analysis. A set of packages are available for R named Programming with Big Data in R (pbdR), which encourages big data analysis, across multiple systems via R code.

R is robust and flexible; it can be run on almost every OS. To top that, it boasts of excellent graphical capabilities, which comes handy when trying to visualize models, patterns and associations within big data structures.

According to industry standards, the average pay of R Programmers is $115,531 per year.

For R language training, drop by DexLab Analytics.

Python

Compared to R, Python is more of a general-purpose programming language. Developers adore it, because it’s easy to learn, a huge number of tutorials are available online and is perfect for data analysis, which requires integration with web applications.

Python gives excellent performance and high scalability for a series of complicated data science tasks. It is used with high-in-function big data engines, like Apache Spark through available Python APIs.

Their Machine Learning Using Python courses are of highest quality and extremely student-friendly.

Let’s Take Your Data Dreams to the Next Level

Scala

Last but not the least, Scala is a general-purpose programming language developed mainly to address some of the challenges of Java language. It is used to write Apache Spark cluster computing solution. Hence, Scala has been a popular programming language in the field of data science and big data analysis, in particular.

There was a time when Scala was mandatory to work on Spark, but with the proliferation of many API endpoints approachable with other languages, this problem has been addressed. Nevertheless, it’s still the most significant and popular language for several big data tools, including Finagle. Also Scala houses amazing concurrency support, which parallelizes a whole many processes for huge data sets.

The average annual salary for a data scientist with Scala skills is $102,980.

In the end, you can never go wrong with selecting any one of the big data programming languages. All of them are equally good, productive and easy to excel on. However, Python is probably the best one to start off with.

For more updates or information on big data courses, visit DexLab Analytics.

The original article is here at – http://www.i-programmer.info/news/197-data-mining/11622-top-3-languages-for-big-data-programming.html

 

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How VC Firms Are Using Machine Learning to Make Robust Investment Decisions

How VC Firms Are Using Machine Learning to Make Robust Investment Decisions

Venture capital companies find it hard to pool in interesting investment options – the task is laborious and travel-intensive. But, thanks to machine learning and predictive analytics – they have now started to transform the entire procedure of how an investor builds up a portfolio altogether.

Considering the power of AI’s utility in determining the most fabulous startup investments, InReach Ventures co-founder Roberto Bonanzinga has decided to invest $7 million on respective software that deploys machine learning to identify significant European startups to invest capital into. Following its footsteps, several other VC firms have started doing this, already just to thrive in.

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Rightfully so, AI is an incredible tool that is capable enough to filter out all the unnecessary noise and pull up VCs with potential candidates for sound investment. This makes it easier for entrepreneurs to hit the optimal level of funding and appeal to strong VCs.

AI: An Investment Ally

According to a Social Science Research Network Study, there lies an inherent risk with investing on newbie entrepreneurs, and just only 18% tastes success on their feats. Brand new business owners are ambiguous, they need some scrutiny before investment – for that, AI framework is armed with the required tools and information – it can internalize data to easily derive at conclusions and fasten a success rate to a company on the basis of past industry performance, revenue growth, profit ratios and market size.

As a result, entrepreneurs can tweak their pitches and alter company profiles to better tally with AI, and this how they can start:

Get Deeper

Who doesn’t dream of owning a company that’s a market leader?! However, raising such adequate amount of capital becomes the real challenge. The challenge intensifies when budding entrepreneurs need to attract funds.

For such minority-fronted startups, Alice, a formidable AI platform uses data to decide which businesses are worth funding. Entrepreneurs should implement AI platforms, like Alice to take a deeper look into the key metrics to get a larger picture how their startups are staking up to their tailing rivals who received funding and how well they are functioning.

Tracking Investor Trends Helps

Age-old methods of tracking investment trends are things from the past, because AI and machine learning is changing the entire ball-game. A Berlin-based VC firm Fly Venture plans to target European startups in the seed stage and pre-Series A startups and finally closed its first fund at $41 million. It aims to use machine learning to generate deal flow. This type of technology helps entrepreneurs meet the right investors at right time. After keeping a close eye on the market, it’s about time to utilize the AI-sought information to make sure your company is line with what investors are seeking in a veritable startup partner. This will bear more fruits and less frustration.

Never stop evolving

The best thing about AI is that it never stops improving. Constantly, machine learning is on the move – it analyzes information 24/7 so that entrepreneurs gain access to non-stop updates to tweak their businesses, while pitching for investors.

In a nutshell, to have better insights and cleaner access to data, entrepreneurs need to harness the relentless power of AI. The technology isn’t eating away our jobs, instead its bringing a new change in the data-inspired environment. And if you are already working with it, you’ll understand how it’s reshaping and guiding venture capital to startups that AI finds worthwhile.

To grasp emerging trends, newer solutions, robust techniques and real-life case studies, take up Machine Learning Using Python courses from DexLab Analytics. Their Machine Learning Training Gurgaon simply gives an out of the world experience, thus need to be tried on.

 

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Getting Started with Machine Learning: Crack the Code

Getting Started with Machine Learning: Crack the Code

Machine Learning has taken us all to the tipping point from where the entire ballgame of technology and the way we interact with the digital world has started changing and the surge is expected to continue over the next decade. Increasingly, the decisions of the future are going to be made by machines, and we can’t seem to be more excited!!

It’s time to adopt Machine Learning

According to McKinsey reports, AI adoption in the tech sector is at its nascent stage, with few firms implementing it on a large scale. The companies that are yet to deploy it are still in two minds whether they should expect return on investments or not.

Nevertheless, skilled data scientists better be start speeding up the process of implementation of these emerging technologies if they want to stay right on edge ahead of their tailing rivals. Machine Learning is the new in-thing that must be embarked on RN.

And for that, here goes the following tips that will help you ride towards AI success:

Inspect the areas where data science fits into

Leverage data science and Machine Learning within an organization to trigger better optimization and smoother implementation. Imbed data science and machine learning into every department, like HR, marketing, sales and finance. Also, try pairing data scientists with software engineers to build agile models on machine learning, that’s the best way to scale across company operations better.

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Treat data as money

Today, data acts as the fuel for an organization. But it can also be treated as money, and diligent data consultants need to manage, protect and obsess over it. Data is powerful but in order to derive the best out of it, it needs to be played well in the hands of experts. And those hands are of data specialists who values data like money.

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Stop hunting down purple squirrels

No wonder, data scientists are individuals with an exceptionally high aptitude in math and statistics; they are skilled in evaluating insights in data. They don’t necessarily have to be software engineers who only know how to write algorithms and curate tech products. Data scientists are much more than that.

Companies often seek unicorn-like aspirants who are ninja software engineers, ace statisticians and master of industry domain, but the sad part is that they look for all these 3 character traits in a single job candidate, which needs to be changed.

Keep an eye on ‘derived data’

If you are thinking of sharing your algorithms with any other person then the chances are high that they will see your data. But companies that are keen on protecting its data should refrain from such activities. Data for informatics companies is like a new currency – they need to be well-guarded and treasured for life!

Educate about the perks of AI

AI is a blessing, for all you tech nerds and gizmo jerks. And accomplished data professionals should look for ways to promote AI and influence friends and co-workers to embrace this new king-some technology. After all, successful machine learning implementation may become the key to your company’s future growth, provided you treat it in the right manner.

Get amazing Machine Learning course online only at DexLab Analytics. Being an incredible online training platform for data science, they offer the best machine learning training at affordable prices in Delhi NCR.

 

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Periscope Data Adds Python, R and SQL on A Single Platform for Better, Powerful Data Analysis

Periscope Data Adds Python, R and SQL on A Single Platform for Better, Powerful Data Analysis

Recently, a veteran data analytics software provider, Periscope Data announced some brand new developments while updating their Unified Data Platform for Python, R programming and Structured Query Language. This new Unified Data Platform will enable data professionals to work in sync with 3 key skills all on a single platform.  Far more better analysis will be conducted using less time by altering data in SQL, executing complex statistical analyses in Python or R, followed by improved visualization, collaboration and reporting of results – all performed on Periscope’s dynamic analytics platform.

A massive data explosion is taking place around the world around us. More than 90% of the world’s data has been created in the past two years, and the numbers are still on the rise. To this, new levels of sophistication needs to be added to analyze the complexity of data – “The addition of Python and R support to our Unified Data Platform gives our customers a unique combination of tools – from machine learning to natural language processing to predictive analytics, analysts will be able to answer new questions that have yet to be explored,” says Harry Glaser, co-founder and CEO of Periscope Data.

The inclusion of Python and R support in Periscope framework comes with ample benefits, and some of them are highlighted below:

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All data at a single place

Instead of relying on several data sources, Periscope Data prefers to combine data together collected from various databases to bring them to a single platform, where nothing but a single source of truth for data is established. The data collected is updated and in crisp format.

Predictive analytics

It’s time to leverage Python and R libraries and move beyond the conventional historical reporting for the sake of modeling predictions. With lead scoring and churning prediction, businesses are now in a better position to derive significant insights about a future of a company.

No more switching between tools

Seamlessly, users can switch between querying data in SQL and analyzing data in R or Python, all at the same time on a same platform. Data professionals will be able to modify their datasets, enhance the performance of their models and update visualizations from a single location.

Mitigate data security concerns

The integration of R, Python and SQL by Periscope Data ensures the data professionals can run and share all sorts of models securely and in full compliance with all the norms, instead of seeking open source tools. Periscope Data is SOC2 and HIPAA compliant. It performs regular internal audits to check compliance requirements and safety issues.

Efficient collaboration with teams

As all the analysis takes place in a central location, be sure all your insights will be thoroughly consistent, secure and free of any version-control issues. Also, Periscope Data allows you and your team members the right to read and write access when required.

Easy visualization of analysis

To develop powerful visualizations that reach one’s heart and mind, leverage Periscope’s resources to the optimum levels. Data teams allow users to easily visualize through R packages and Python libraries so as to nudge users to explore the better horizons of data.

To learn more about R programming or Python, opt for Python & Spark training by DexLab Analytics. R language certification in Delhi NCR empowers students and professionals to collaborate and derive better insights faster and efficiently.

 

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