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98-381 - Introduction to Programming Using Python - Dump Information

Vendor : Microsoft
Exam Code : 98-381
Exam Name : Introduction to Programming Using Python
Questions and Answers : 40 Q & A
Updated On : October 18, 2018
PDF Download Mirror : [98-381 Download Mirror]
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98-381 Questions and Answers

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98-381 Introduction to Programming Using Python

Study Guide Prepared by Killexams.com Microsoft Dumps Experts


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98-381 exam Dumps Source : Introduction to Programming Using Python

Test Code : 98-381
Test Name : Introduction to Programming Using Python
Vendor Name : Microsoft
Q&A : 40 Real Questions

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Cricket pro groups with Microsoft for a bat that can track analytics in precise-time, and send them to lovers | killexams.com Real Questions and Pass4sure dumps

Microsoft is teaming up with renowned Indian athlete Anil Kumble to debut a cricket bat that can song batting analytics in true-time, and ship them straight to enthusiasts.

Kumble on Thursday introduced the vigour Bat, a bat fitted with a web-linked sensor that communicates with a receiver buried in the cricket pitch and analyzes information immediately.

The light-weight sticky label affixed on a bat is designed to be unobtrusive for gamers but nevertheless powerful sufficient to capture records.

ultimately, Microsoft and Kumble’s enterprise, Spektacom, hope to expand the technology to different sports in India and the U.S., referred to Peggy Johnson, Microsoft’s company development govt vice chairman.

It may be used in baseball, soccer or soccer and may sooner or later encompass helmet analytics that could assist become aware of and stop concussions, she pointed out.

The vigor Bat’s sensor can notice velocity on influence, twist on have an impact on and different analytics. When a batsman hits the ball, the small sticker caught on the bat sends tips to the receiver, or “stump box,” observed behind the participant and then on to a broadcaster.

quickly, Spektacom’s vigour Bat will also ship analytics to a cell app so enthusiasts in the stands can get assistance on their phones.

“It brings about a whole new dimension of how the fan appears at the video game of cricket itself,” pointed out Kumble, who retired from taking part in professionally in 2012.

Kumble, who's an engineer by using practicing and one of the vital country’s all-time properly cricketers, observed the know-how will also be used to support avid gamers and coaches bear in mind and tweak how batsmen are taking part in.

Spektacom has places of work in Bangalore, India, at Microsoft’s ScaleUp software, which helps launch startups. The Indian company’s know-how is powered by way of Microsoft’s Azure cloud computing provider. Spektacom and Microsoft have partnered with Indian broadcaster celebrity India to begin sharing video game analytics.

The vigour Bat isn't yet accessible for sale publicly, and Microsoft didn’t free up particulars about can charge.


Has the home windows Insider software turn into too large for its own first rate? | killexams.com Real Questions and Pass4sure dumps

When Microsoft first introduced the Insider software, it changed into met with an exuberant reception. the brand new initiative from the application gigantic would open up windows building to the loads, and enable lovers to observe alongside as the enterprise's engineers labored on improving and expanding the capabilities of what turned into deemed 'the last edition of windows'.

regardless of that previous enthusiasm, although, many among the Microsoft trustworthy have when you consider that turn into a little bit disenchanted with it. as the myriad tweets lampooning the ninja cat will inform you, something's long past awry. it's also now not just a problem that has to do with perception; with the unencumber of each major characteristic updates to windows 10 this 12 months, Microsoft confronted massive challenges with reference to the high-quality of their unlock.

in the case of the October 2018 replace, in certain, Microsoft has even had to take the embarrassing step of pulling the replace whereas it works to fix a controversy it really is deleting some clients' files. far worse is the indisputable fact that these considerations had been said by using Insiders as a good deal as three months in the past, and yet were someway still unresolved. All of this raises the question: has the Insider application lost its center of attention?

returned when the primary Technical Preview of home windows 10 became flighted to Insiders, the focus of the endeavour looked as if it would were inviting home windows fans to chime in and aid the business navigate the treacherous waters of OS development devoid of risking an additional disaster à la windows eight. inside two weeks, the software had 1 million users, and had accrued 200,000 pieces of feedback. A bafflingly colossal number by way of any measure.

but this turned into just the beginning. by the point home windows 10 formally launched, there have been 5 million registered Insiders; that number grew to 10 million back in March of 2017, and is likely even greater these days. With all those testers and all that comments, Microsoft gained an tremendously giant pool of beta testers for its OS, and possibly a up to now unthinkable quantity of telemetry and other statistics to study.

Yet, the program's success looks to have additionally been its Achilles heel. As Microsoft started to depend much less on expert unbiased testers and more on Insiders, it bought a lot more records, sure, but that statistics was now not at all times essentially the most advantageous, particularly when it involves trying out the OS.

among the tens of millions of windows Insiders, most are conveniently enthusiasts of the enterprise and use it as a means of telling Microsoft what they would want to see in windows in the future. it really is now not a foul thing, but when Microsoft is relying so closely on Insiders for testing, there should be a more concerted effort to weed out complications before they turn into real issues. Open feedback Hub now, for instance, and among the true 10 most upvoted gadgets, best two pertain to genuine problems clients encountered in their use of windows 10.

both of these are about part crashing from time to time, and as a result confer with the same challenge. Then, there may be the fact that while both provide a problem to repair, they provide little assistance about what exactly may well be inflicting the difficulty, whatever thing a team of expert testers could be way more adept at.

sure, Microsoft can, and has, used synthetic intelligence and the telemetry it necessarily gets from Insiders to complement its bug-bashing efforts however as is fitting clearer now, those efforts aren't understanding nearly as smartly as the business would like.

in the last 12 months, each of the predominant feature updates released with the aid of Microsoft have suffered from principal, often deal-breaking considerations. Following the free up of the April 2018 update, many clients noticed their systems freeze after the use of Chrome. Google's browser is among the many most time-honored courses on home windows, and the whole OS freezing with its use is not any small thing.

last week, the business released the October 2018 replace for home windows 10, and this release became similarly beset by using numerous concerns from day one. Some users have been unable to set up the update on their PCs due to concerns with Intel drivers, whereas others said their files being deleted. All of this culminated in Microsoft taking the unparalleled step of pulling the update unless it had fixed the complications, exceptionally these bearing on data loss.

To make concerns worse, we soon learned that windows Insiders had pronounced information loss considerations within the feedback Hub nearly three months in the past. however, on account that the number of clients affected became no longer especially large, and the comments studies most effective received just a few upvotes, they have been buried beneath the barrage of requests for a prettier UI or different characteristic requests.

The Insider application allows for Microsoft to profit a big amount of feedback, but that additionally capacity that the substance of that comments can also no longer at all times be up to par, specially when it comes to first-rate handle. needless to say, whatever thing AI hints the business is counting on for sorting remarks had been possible unsuccessful in flagging these concerns. furthermore, the incontrovertible fact that it also selected to effectively skip the release Preview ring this time around didn't aid matters plenty, both.

those feedback about too many ninja cats I talked about earlier could be disturbing, however they do underline a brand new side to the Insider software: it be not basically beta checking out, it's now considered one of Microsoft's most vital advertising and branding equipment. Take here interview from Insider program head Dona Sarkar, the place she tries to describe it to americans who have no idea what being an Insider ability:

word that whereas she emphasises the function of the Insider program in assisting Microsoft figure out where to take home windows next, she doesn't mention, at all, the other vital function of the Insider program: testing windows.

What can Microsoft do now? That the Insider application is such a hit or that it's now one of Microsoft's most in demand brands for interfacing with its neighborhood aren't at all dangerous things, but all of this does imply that Microsoft needs to recognize that it can not simply treat the Insider program like a traditional beta.

at the start, it be clear that the enterprise should cease relying on the Insider software so lots for trying out, and desires to supplement the information it receives from Insiders with some historic faculty dedicated in-condo testers. lower back in 2014, the resolution to lay-off a big variety of the home windows group's QA group changed into regarded unthinkable, but it truly is precisely what took place. Microsoft should rehire one of the most many committed testers that it has laid off considering the fact that 2014. The Insider application is decent for studying what users desire, and informing the windows team of what aspects to add, but it surely is not doing enough to help stave off foremost concerns from making their option to public releases.

The company additionally needs to drastically reassess how remarks Hub works. there's evidently room for development when it comes to how its AI algorithms sort and fee feedback, in particular those concerns that may well be crucial in nature but don't seem to be skilled with the aid of too many individuals. user-generated content will all the time be missing in terms of knowledge and satisfactory, but Microsoft's fresh resolution to permit users to also point out the severity of a malicious program might also go a long method in helping it stay away from an identical mishaps in the future.

What further measures the company eventually decides to soak up order to sort things, simplest time will inform, however hopefully, the embarrassment of getting to tug a major characteristic update simply days after its unencumber may still make it clear to Microsoft that issues aren't working out as they stand. The contemporary changes are a step within the appropriate path, and hopefully Microsoft will proceed to construct on them.


Microsoft invites LlamaZOO to be a part of combined truth companion program | killexams.com Real Questions and Pass4sure dumps

Microsoft invites LlamaZOO to be part of mixed truth partner application

“We’re excited to have LlamaZOO be a part of the Microsoft blended fact software, as they deliver years of journey and talents in constructing real-time mixed truth options,” says Paul Corriveau, Director of companion advertising and marketing.

VICTORIA, British Columbia (PRWEB) October 08, 2018

LlamaZOO Interactive Inc. (LlamaZOO), award-winning builders of interactive spatial facts visualization, digital twinning, and combined fact options, has been invited to join Microsoft’s blended fact companion application (MRPP). LlamaZOO now joins an international group of fellow leaders in blended reality innovation, selected via Microsoft to support business valued clientele effectively pilot and install disruptive mixed reality options.

“We’re excited to have LlamaZOO join the Microsoft mixed reality application, as they deliver years of event and knowledge in developing true-time blended reality solutions,” says Paul Corriveau, Director of associate advertising and marketing. “As one in every of our chosen partners they're going to continue to create spectacular options for purchasers in training, natural components, and industrial sectors.”

whereas most businesses accepted into the MRPP participate in an extensive multi-week Readiness practicing curriculum, LlamaZOO’s invitation into the application was quickly-tracked due to the enterprise’s validated in-condominium knowledge, a hit mixed truth/augmented fact tasks together with MineLife and JetsonVR, and proven dedication to building combined truth potential.

The purpose of the mixed fact companion application is to allow Microsoft companions comparable to LlamaZOO to collaborate and construct commercial mixed fact options that enrich communications, streamline operations, and drive worker productiveness, while developing unbelievable client experiences. besides the superb reputational have an impact on that accompanies this kind of vote of self belief from Microsoft, the merits to LlamaZOO in joining Microsoft’s MRPP consist of joint enterprise planning, technical information, income and advertising and marketing enablement, and more.

“LlamaZOO has lengthy had a good relationship with Microsoft,” pointed out LlamaZOO’s CEO & Co-Founder Charles Lavigne. “From being first wave Hololens developers, to advertising blended truth know-how at essential business events, to collaborating on Canada’s Digital technology Supercluster projects – it’s entertaining to start this next chapter together, and we’re wanting to support push MR know-how to new limits.”

“over the past year we’ve developed blended fact options that have helped herbal components business leaders keep thousands and thousands via virtual presence, and accelerating mission timelines,” says LlamaZOO’s VP enterprise development & Co-Founder Kevin Oke. “Now having Microsoft’s support, we seem ahead to reaching further reach with our existing use-circumstances leading to a improved client ROI.”

For more tips about how combined fact options can improve enterprise operations and tactics please talk over with https://www.llamazoo.com

About LlamaZOO

At LlamaZOO every little thing we do is set assisting clarify the complicated, enabling stakeholders to make meaningful and impactful choices through visually astounding communication equipment.

Our mission is to exchange the way the realm sees facts. We simply turn up to improve main AR/VR/MR applied sciences to obtain this.

LlamaZOO are award-profitable builders of interactive 3D visualization, digital twin, and mixed fact solutions, with offices in Victoria and Vancouver, BC.

learn extra at http://www.llamazoo.com/careers

Media Contact:Charles LavigneLlamaZOO Interactive1-888-291-4445 ext 100charles(at)llamazoo.com

Contact writer

Kevin Oke LlamaZOO Interactive Inc+1 888-291-4445 Ext: 101Email > discuss with web page

Media

LlamaZOO’s “MineLife”: An interactive and immersive virtual duplicate of mine planning facts developed for Microsoft Hololens, VR headsets (together with Microsoft combined reality Headsets), and computer 3D.

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98-381 Introduction to Programming Using Python

Study Guide Prepared by Killexams.com Microsoft Dumps Experts


Killexams.com 98-381 Dumps and Real Questions

100% Real Questions - Exam Pass Guarantee with High Marks - Just Memorize the Answers



98-381 exam Dumps Source : Introduction to Programming Using Python

Test Code : 98-381
Test Name : Introduction to Programming Using Python
Vendor Name : Microsoft
Q&A : 40 Real Questions

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I passed 98-381 paper inside weeks,way to your wonderful QA look at materials.Rating ninety six percentage. I amvery assured now that im able to do better in my closing 3 exams and certainly use your workout material and advocate it to my buddies. Thank you very a whole lot for your extremely good on line attempting out engine product.


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6.149: Introduction to Python | killexams.com real questions and Pass4sure dumps

(October 25, 2016) 6.149 will not be offered in IAP 2017.

This 6-unit P/D/F course will provide a fast-paced introduction to programming using Python for highly motivated students with little or no prior experience in programming computers over IAP. The course will focus on planning and organizing programs, as well as the grammar of the Python programming language. Lectures will involve plenty of examples, and office hours will be staffed with numerous LAs to help students master Python.

This class is designed to help prepare students for 6.01: Introduction to EECS. 6.01 assumes some knowledge of Python upon entering; the course material for 6.149 has been specially designed to make sure that concepts important to 6.01 are covered.

Participants are encouraged to bring their own laptop to class daily, starting on the first day of class. If you do not have a laptop, please contact the instructors prior to the first day so other arrangements may be made.

This course is fast-paced, historically over-subscribed, and entirely student-run. Therefore:

  • - we will be unable to grant extensions (except for medical emergencies with a dean's note)
  • - if you will be missing any lecture, lab, or exam sessions, you cannot take 6.149
  • - cross-registered and graduate students will not be able to take 6.149
  • Please direct questions to 6.149-instructors AT mit DOT edu.


    Python for Kids: A Playful Introduction to Programming | killexams.com real questions and Pass4sure dumps

    My 9-year-old daughter Jane likes playing with Scratch, a kids' programming language developed at MIT. (I recently reviewed a great book called Super Scratch Programming Adventure.)

    Python for Kids: A Playful Introduction to Programming is another programming book for kids. I've been going through it myself, and enjoying it. Unlike Scratch, which lets you write programs by dragging and dropping colored command blocks, Python is a traditional programming language that uses lines of code. I've played around with other languages a bit, but Python is the only language that seems to be as easy and intuitive as BASIC. I'm not sure if Jane is ready for Python -- she wouldn't have trouble learning it, but it's not as fun as Scratch (at least at first), but I think in a year or two she might be. And this is the book I'll give her when she's ready.

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    Chapters end with programming puzzles designed to stretch your brain and strengthen your understanding. By the end of the book you'll have programmed

    two complete games: a clone of the famous Pong and "Mr. Stick Man Races for the Exit" -- a platform game with jumps, animation, and much more.

    As you strike out on your programming adventure, you'll learn how to:

    --Use fundamental data structures like lists, tuples, and maps

    --Organize and reuse your code with functions and modules

    --Use control structures like loops and conditional statements

    --Draw shapes and patterns with Python's turtle module

    --Create games, animations, and other graphical wonders with tkinter

    Python for Kids: A Playful Introduction to Programming

    French illustrator Linda Bouderbala did a fun exercise where she gathered some of her favorite characters from geek and pop culture and organized them by color.

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    Raising baby corals is a labor-intensive process that requires gathering the babies at the moment the corals spawn in the wild. Scientists compete with fish that feast on the babies, netting the gametes and planulae, then caring for them in a lab until they can be planted on the ocean floor.

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    Multinational companies have used the no-nonsense methodologies of Six Sigma and Lean Six Sigma to oil a smooth-running operation for years. What is it? Six Sigma (and its offshoot, Lean Six Sigma) apply the principles of science to business, teaching managers to methodically target waste, maximize output and streamline the flow from producer to consumer. […]

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    It’s the age of the gig economy for good reason. With an unlimited market out there on the internet, side hustles are as easy as finding your voice and finding an audience. Whether it’s podcasting, writing or design, these training courses are an easy mentor for your transition from hobbyist to hustler.   Start a […]

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    Data science sexiness: Your guide to Python and R, and which one is best | killexams.com real questions and Pass4sure dumps

    At Springboard, we pair mentors with learners in data science. We often get questions about whether to use Python or R – and we’ve come to a conclusion thanks to insight from our community of mentors and learners.

    Data science is the sexiest job of the 21st century. Data scientists around the world are presented with exciting problems to solve. Within the complex questions they have to ask, a growing mountain of data rests a set of insights that can change entire industries. In order to get there, data scientists often rely on programming languages and tools.

    This is an excerpt of our free, comprehensive guide to getting a job in data science that deals with two of the most common tools in data science, Python and R.

    Python

    Python is a versatile programming language that can do everything from data mining to plotting graphs. Its design philosophy is based on the importance of readability and simplicity. From the The Zen of Python:

  • Beautiful is better than ugly.
  • Explicit is better than implicit.
  • Simple is better than complex.
  • Complex is better than complicated.
  • Flat is better than nested.
  • Sparse is better than dense.
  • Readability counts.
  • As you can imagine, algorithms in Python are designed to be easy to read and write. Blocks of Python code are separated by indentations. Within each block, you’ll discover a syntax that wouldn’t be out of place in a technical handbook.

    Photo Creditimage00

    Many data scientists use Python to solve their problems: 40 percent of respondents to a definitive data science survey conducted by O’Reilly used Python, which was more than the 36 percent who used Excel.

    This has spawned one of the largest programming communities in the world, filled with brilliant and versatile problem-solvers who collaborate together to push Python forward. You’ll be sure to find any answers to your Python questions on StackOverflow or on Quora. The official Python website is filled with a community building out its future.

    The community is so large, you’ll find all kinds of open source code libraries suited to solving very specific problems. One example is NLTK, a library of code that makes it easier for computers to process language. With one import statement, you can parse through large texts and get intelligent text analysis from a machine. You’d be able to tell if your Twitter followers are sending you positive or negative messages in a matter of seconds.

    The Swiss-Army knife that Python represents fits quite well into data science work:

    An introduction to Python and data science

    The following introduction to Python for data science will get you set up on the basics. You’ll get set up on a powerful environment manager known as Anaconda. Once you’re done with the tutorial, you’ll have installed all of the essential code libraries you need to start in data science with Python.

    Python is a language you can use for nearly every step in the data science process thanks to its versatility.

    Collect raw data

    Python supports all kinds of different data formats. You can play with comma-separated value documents (known as CSVs) or you can play with JSON sourced from the web. You can import SQL tables directly into your code.

    You can also create datasets. The Python requests library is a beautiful piece of work that allows you to take data from different websites with a line of code. It simplifies HTTP requests into a line of code. You’ll be able to take data from Wikipedia tables, and once you’ve organized the data you get with beautifulsoup, you’ll be able to analyze them in-depth.

    You can get any kind of data with Python. If you’re ever stuck, google Python and the dataset you’re looking for to get a solution.

    Process the data

    To unearth insights from the data, you’ll have to use Pandas, the data analysis library for Python. It can hold large amounts of data without any of the lag that comes from Excel. You’ll be able to filter, sort and display data in a matter of seconds.

    Pandas is organized into dataframes, which can be defined and redefined several times throughout a project.

    data structures

    You can clean data by filling in non-valid values such as NaN (not a number) with a value that makes sense for numerical analysis such as 0.

    You’ll be able to easily scan through the data you have with Pandas and clean up data that makes no empirical sense. If you only have 1,000 users, and you have data that indicated that 2,000 users have successfully been invited in, you might have an encoding error where somebody added an extra zero. Now is the time to look through your data with Pandas and make sure they correspond to the context of your problem.

    Explore the data

    Once you dive deep into Pandas, you’ll realize how powerful it is. You’ll be able to take complex rules and apply them to the data. Do you want to find how many actors have played Hamlet in the 1950s, in a dataset of every movie that has ever been released? Use the filtering functions and the sorting method to get that information with a few lines of code.

    You’ll be able to do deep time series analysis and match data points with their temporal context to the exact second. You can track variations in stock prices to their finest detail.

    You’ll be able to group data points by their context using the groupby() function. This will allow you to track changes in stock prices based on the day of the week, or the industry they’re in.

    If you need any help getting started, check out the Pandas Cookbook, written by Julia Evans. Get started with a few projects and you’ll quickly get the hang of it.

    Perform in-depth analysis

    Now that you have your data processed, you can start doing numeric analysis with Numpy. You can do scientific computing and calculation with SciPy. You can access a lot of powerful machine learning algorithms with the scikit-learn code library. scikit-learn offers an intuitive interface that allows you to tap all of the power of machine learning without its many complexities.

    Visualizing data

    The IPython Notebook that comes with Anaconda has a lot of powerful options to visualize data. You can use the Matplotlib library to generate basic graphs and charts from the data embedded in your Python. If you want more advanced graphs or better design, you could try Plot.ly. This handy data visualization solution takes your data through its intuitive Python API and spits out beautiful graphs and dashboards that can help you express your point with force and beauty.

    You can also use the nbconvert function to turn your Python notebooks into HTML documents. This can help you embed snippets of nicely-formatted code into interactive websites or your online portfolio. Many people have used this function to create online tutorials on how to learn Python and interactive books.

    The benefits of Python

    Indeed.com notes that the average salary of those that have Python in their job description ranges around $102,000. Python is the most popular programming language taught in universities: the community of Python programmers is only going to be larger in the years to come. Using Python, you can solve a data problem end-to-end, and you’ll be able to do so much more than data science if you want to explore other computer science problems.

    Takeaway: Python is a powerful, versatile language that programmers can use for a variety of tasks in computer science. Learning Python will help you develop a versatile data science toolkit, and it is a versatile programming language you can pick up pretty easily even as a non-programmer. This introduction to data science with Python will take you where you need to go.

    R

    Photo Creditimage02

    R is slightly more popular than Python in data science, with 43 percent of data scientists using it in their tool stack compared to the 40 percent who use Python. It’s a programming environment and language dedicated to solving statistical problems. It is much more focused on the domain than the versatile Swiss-Army knife that Python is.  R specializes in solving data science problems.

    R can be difficult to get into if you have experience with a previous programming language: it isn’t constructed by computer scientists for computer scientists. One programmer has documented his struggles and built a tutorial to help others out with some of the unconventional quirks of R, something you should be aware of if you have familiarity with other programming languages. Unlike Python which is built to have a simple syntax, R has a tricky syntax with a bit of a steep learning curve.

    UCLA has a great resource to get you started in R and our learning path in data analysis offers several more resources. This introduction to R is comprehensive in going from A to Z for R in data science, and you’ll be able to see what a sample session looks like. You’ll find a healthy StackOverflow community for R in case you bump into any problems.

    An introduction to R and data science

    R really shines when it comes to building statistical models and displaying the results. It is an environment where a wide variety of statistical and graphing techniques, from time series analysis to classification, can be applied. Every scientist has had to build regressions and test for statistical significance of results, and R is perfectly suited for visualizing those models, from bell curves to Bayesian analysis.

    R is a staple in the data science community because it is designed explicitly for data science needs. The community contributes packages that, similar to Python, can extend the core functions of the R codebase so that it can be applied to very specific problems such as measuring financial metrics or analyzing climate data.

    Collect raw data

    You can import data from Excel, CSV, and from text files into R. Files built in Minitab or in SPSS format can be turned into R dataframes as well. While R might not be as versatile at grabbing information from the web like Python is, it can handle data from your most common sources.

    Many modern packages for R data collection have been built recently to address this problem. Rvest will allow you to perform basic web scraping, while magrittr will clean it up and parse the information for you. These packages are analogous to the requests and beautifulsoup libraries in Python.

    Process the data

    library

    R is a flexible environment for adding new columns of information, reshaping information, and transforming the data itself. This tutorial goes into the exact steps you’ll need to perform to get clean data. Many of the newer R libraries such as reshape2 allow you to play with different data frames and make them fit the criterion you’ve set.

    Explore the data

    R was built to do statistical and numerical analysis of large data sets, so it’s no surprise that you’ll have many options while exploring data with R. You’ll be able to build probability distributions, apply a variety of statistical tests to your data, and use standard machine learning and data mining techniques.

    Basic R functionality encompasses the basics of analytics, optimization, statistical processing, optimization, random number generation, signal processing, and machine learning. For some of the heavier work, you’ll have to rely on third-party libraries.

    Perform in-depth analysis

    In order to do specific analyses, you’ll sometimes have to rely on packages outside of R’s core functionality. There are plenty of packages out there for specific analyses such as the Poisson distribution and mixtures of probability laws.

    Visualizing data

    R was built to do statistical analysis and demonstrate the results. It’s a powerful environment suited to scientific visualization with many packages that specialize in graphical display of results. The base graphics module allows you to make all of the basic charts and plots you’d like from data matrices.  You can then save these files into image formats such as jpg., or you can save them as separate PDFs. You can use ggplot2 for more advanced plots such as complex scatter plots with regression lines.

    The benefits of R

    Takeaway: R is a programming environment specifically designed for data analysis that is very popular in the data science community. You’ll need to understand R if you want to make it far in your data science career. This interactive tutorial will help.

    Python vs R

    Photo Creditimage01

    You’ll see a lot of content that compares and contrasts R with Python. This analysis by Datacamp on the differences is often regarded as the definitive take on the topic.

    Usage

    Python, as we noted above, is often used by computer programmers since it is the Swiss knife of programming languages, versatile enough so that you can build websites and do data analysis at the same time.

    R is primarily used by researchers and academics who don’t necessarily have a background or knowledge of computer science.

    Syntax

    Python has a nice clear “English-like” syntax that makes debugging and understanding code easier, while R has unconventional syntax that can be tricky to understand, especially if you have learned another programming language.

    Learning curve

    R is slightly harder to pick up, especially since it doesn’t follow the normal conventions other common programming languages have. Python is simple enough that it makes for a really good first programming language to learn.

    Popularity

    Python has always been among the top 5 most popular programming languages on Github, a common repository of code that often tracks usage habits across all programmers quite accurately, while R typically hovers below the top 10.

    Focus on data science

    Python is a general-purpose language, and there is less focus on data analysis packages then in R. Nevertheless, there are very cool options for Python such as Pandas, a data analysis library built just for it.

    Salary

    The average data scientist who uses R will receive a salary of $115k compared to the $102k they would earn with Python.

    Conclusion

    Python is versatile, simple, easier to learn, and powerful because of its usefulness in a variety of contexts, some of which have nothing to do with data science. R is a specialized environment that looks to optimize for data analysis, but which is harder to learn. You’ll get paid more if you stick it out with R rather than working with Python.

    Yet, that’s only one way to frame the debate: seeing it as a zero-sum game. The reality is that learning both tools and using them for their respective strengths can only improve you as a data scientist. Versatility and flexibility are traits any data scientist at the top of their field. 23 percent of data scientists surveyed by DataCamp used both R and Python.

    O’Reilly found in their survey of data scientists that using many programming tools is correlated with increased salary. While those who work in R may be paid more than those that work in Python, those that used 15 or more tools made 30k more than those that just used 10 to 14.

    Takeaway: The Python vs R debate confines you to one programming language. You should look beyond it and embrace both tools for their respective strengths. Using more tools will only make you better as a data scientist.

    Read next: What customer success managers actually do all day

    This post is part of our contributor series. The views expressed are the author's own and not necessarily shared by TNW.

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    Microsoft 98-381 Exam (Introduction to Programming Using Python) Detailed Information



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