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R Cheat Sheet: The One You Should Keep it Handy

Updated on 03 July, 2023

6.57K+ views
13 min read

Introduction

R programming language’s status has grown from being a mere programming language made for statistical analysis to a more potent all-round tool. The user base of R has also grown over the past few years. It is now being employed by a host of programmers, scholars, and practitioners. In order to make the most out of any programming language, learning how to get help is quintessential because errors are bound to happen.

So, with the knowledge of syntax, the knowledge on how to access the R help files and find help from other sources is critical for success as an R programmer. Now, here is where the R cheat sheet will come in handy. The R cheat sheet contains all the vital functions along with its calls for an easy reference of the programmers.

Learn More: R Tutorial for Beginners: Become an Expert in R Programming

Getting help with the programming language R

Even the best books to introduce people and ease their way into the world of programming in R are not enough on their own. Sometimes one needs to learn and access the R help files. This help file that we keep talking about presents the user with a piece of detailed information on how to use various dependencies in R. How to make use of a particular function, for every built-in function is baked into these help files. The code examples on how to use the specific function are also there on each of these different help pages. 

If you want to access the R help files, to get help on how to use a particular feature, you will have to use any one of the functions that are listed below:

1. ?: The use of a single question mark displays the help files pertaining to any function that the user desires to get help. For example, “?data.frame” would view the page on the R help files that contain the documentation on how to use the function data.frame(). 

2. ??: If you want to search for a particular substring in the R help files, “??” will do the job for you. So, if you want to know the names of a function which contains the word “list” in them, all you have to do is run “??list” and your problem would be solved

3. RSiteSearch(): This function RSiteSearch() essentially does what it is named after. It essentially does an online search about the query that is passed as the parameter for this function. So, RSiteSearch(“linear models”) will compile the search at the website “RSiteSearch” for the string “linear models.”

If you are struggling to get help for R and the baked-in documentations are not sitting well with you, there are many add-on packages that you can install to get all the help that you need with R. Packages like “sos” is available for download which is offered by CRAN. This R package contains some clear and concise function which would make the search for all kinds of queries through all the help files available on the website “RSiteSearch.”

The installation of the package is also reasonably straightforward. All that you need to do is run the code install.packages(“sos”) in the R console, then all that is left is to load the package. The package loading can be done through the use of the library(“sos”).

With the installation of the “sos” package, you will now have access to the function called findFn(). This findFn() function takes in the search parameter as the argument and then returns the list of hundreds of the web pages, which contain the argument that has been passed. So, for example, if you run the function findFn (“regression”) into your R console, you will be faced with a web page containing a lot of information.

The information includes links to many functions that have the word regression in the name, or even if they have the phrase regression in their help text, you will also find a reference to it if you use the function findFn().

Read: 6 Interesting R Project Ideas For Beginners

Data Transformation Cheat Sheet

The Data Transformation in R cheat sheet covers how to use the dplyr package to manipulate tidy data. Tidy data is a data format where each variable is a column, each observation is a row, and each value is a cell. The dplyr package provides a set of functions that make it easy to perform common data manipulation tasks, such as filtering, arranging, selecting, mutating, summarizing, and joining data frames. The following are the various functions –

Single-Table Verbs

This section shows how to use the six core functions of dplyr to manipulate a single data frame. These functions are filter(), arrange(), select(), mutate(), summarise(), and group_by(). Each function takes a data frame as the first argument and returns a modified data frame as the output. For example, the filter() function can be used to subset rows based on a condition, the arrange() function can be used to sort rows by one or more variables, and the select() function can be used to choose or rename variables.

Two-Table Verbs

This section shows how to use the four join functions of dplyr to combine two data frames based on a common variable. These functions are inner_join(), left_join(), right_join(), and full_join(). Each function takes two data frames as the first two arguments and returns a joined data frame as the output. For example, the inner_join() function can be used to keep only the rows that match in both data frames. The left_join() function can be used to keep all the rows from the first data frame and add columns from the second data frame, and the full_join() function can be used to keep all the rows from both data frames and fill in missing values with NA.

Grouped Mutates & Filters

This section shows how to use the group_by() function in combination with other dplyr functions to perform operations on grouped data. The group_by() function can be used to group a data frame by one or more variables and create a grouped data frame. A grouped data frame behaves like a regular one, but any operation applied to it will be performed on each group separately. For example, the summarise() function can be used to calculate summary statistics for each group, the mutate() function can be used to create new variables based on group values, and the filter() function can be used to subset groups based on a condition.

Other Useful Functions

This section shows how to use other useful functions from dplyr or related packages to help with data transformation. These functions include rename(), relocate(), slice(), pull(), across(), if_else(), case_when(), and more. Each function has a different purpose and syntax, but they all work with tidy data and follow the same logic as dplyr functions. For example, the rename() function can be used to rename variables in a data frame, the relocate() function can be used to move variables to different positions in a data frame, and the slice() function can be used to select rows by their position.

How to import Data into R

The following table is handy because it contains some functions which will come in very handy when you want to import data into R:

Function What It Does Example
read.table() This function is responsible for reading the data whose columns are not joined together. Usually, this function is employed when the data that you want to read has its columns separated with a comma or a tab. One thing to note is that you can specify the separator yourself alongside some other different arguments which accurately describe the data you want R to read. read.table(file=“myfile”, sep=“t”,
header=FALSE)
read.csv() This function in crude terms is a very toned down or watered-down version of the read.table() method. This function has been hard-coded to read the data from any CSV file that is being passed into this function as an argument. CSV files are typically spreadsheets and MS Excel documents. read.csv(file=“myfile”)
read.csv2() This function is essentially a read.csv() function with minor tweaks. Read.csv2() function has a preset where the separator of the data is a semicolon and the comma serves as the floating or decimal point.  read.csv2(file=“myfile”,
header=FALSE)
read.delim() This function is used when the main motive is to read the files which have been delimited. The default separator that is being used here is tab. read.delim(file=“myfile”,
header=TRUE)
scan() This function gives you a finer and much more precise control over the data that you want to be read by R if the data in question is not tabular. scan(“myfile”,skip=1,
nmax=10)
readLines() This function is used when reading one line at a time from a text file is the required job we want the program to perform. readLines(“myfile”)
read.fwf If the data you have has dates in fixed-width-format then you should use this function because it reads the dates in the fixed-width-format. In simpler words, if the data that you have has a fixed number of characters in each column then this function should be used. read.fwf(“myfile”,
widths=c(1,2,3)

The host of function that you will gain access to after running that line of code and the purpose that they serve are listed below:

Function What it does Example
read.spss This function takes in the name of an SPSS file as the argument and reads it into the R program. read.spss(“myfile”)
read.dta This function takes in the input of the file name of Stata binary format and it reads it into the R program. read.dta(“myfile”)
read.xport This function takes the argument of the name of a SAS export file and it reads the file into the R program. read.export(“myfile”)

Source

Also check out: Why Learn R? Top 8 Reasons To Learn R

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Transformation & Opportunities in Analytics & Insights

Different data types and the basic manipulation of the tables

1. There are basically three data types that are of major importance when you are programming in R. These three types are namely: numeric, character, and a factor. You can quickly do a search for which kind of data type is this, or you can also typecast by using the following two commands, respectively, is.factor() and as.factor().

2. If you happen to import a table whose variables contain one or more than one entries, which are characters, then R will automatically cast the table as the datatype of the factor. However, that being said you can still cast the data into numeric by forcing R, using the command= as.numeric(as.character(dat1$VAR1)).

3. The command names (dat1)=c(“ID”, “X”, “Y”, “Z”) actually renames the variable in your dataset. You will have to keep in mind and the vector length should match the number of variables that you have; otherwise, you will run into an error.

4. The command fix (dat2) opens the entire data you have in a spreadsheet document where you can edit the cells with a simple double-clicking in the cells.

5. If the data you have only contains numeric values in the table, you can take the transposition of the table. Use, dat2 = t(dat1), and the table named as dat2 will contain the transpose (making all the rows into columns) of the table of data contained in dat1.

Our learners also read: Top Python Free Courses

Tips on how to create random data and how to do random sampling

1. The function rnorm(10) takes in the argument of 10 and creates ten random samples. These random samples are generated from a normal distribution, which has a zero mean, and the standard deviation of the dataset happens to be 1.

2. The function runif(10) takes ten different random samples to create a distribution that is uniform and whose value is between zero and one.

3. The function round(rnorm(10)*3+15) takes ten samples, which are random from a normal distribution whose mean is 15, and the standard deviation that it has is of 3 and the floating points which are there in the data are removed with the help of the rounding function.

4. The function round(runif(10)*5+15) gives the user back with random integers, which has the value between the values of 15 and 20. The distribution of these values will be uniform.

5. The function sample(c(“A”, “B”, “C”), 10, replace=TRUE) samples and creates a random sample from any vector that has been passed as the argument to this function. 

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Tips on how to transform data that is inside the data table

1. The function call of the transform function done like this dat2=transform(dat1, VAR1=VAR1*0.4), multiplies the values stored in VAR1 with 0,4 and then re-assign the multiplied value to VAR1 again.

2. The call of the function transform can also be used to create variables with specific dependencies on existing variables. If you call the function like this dat2=transform(dat1, VAR2=VAR1*2), it will create a new variable with the name of VAR2, which will contain the value of VAR1 multiplied with a factor of two.

3. You can also call the transform function to modify the values at any specific site that you require. For performing that task, you will have to call the function like dat2=transform(dat1, VAR1=ifelse(VAR3== “Site 1”, VAR1*0.4, VAR1)). The call, as mentioned earlier of the transform function, multiplies the data stored in VAR1 for the data entries, which are the place known as site 1. The value of the variable VAR1 remains the same everywhere else.

Read : 8 Astonishing Data Science Projects in R For Beginners

Conclusion

The world of programming has seen a boom of languages over the past few years. These programming languages are aimed to eradicate and focus its attention on one aspect of computing. The languages like R have a robust statistical and data science-centric approach mainly because of the baked-in features that this language possesses.

While working in any programming language, having every command on your fingertips is not an easy task. Now, this is where the R cheat sheet comes to the rescue. One thing to remember always is that the best R cheat sheet is the one that you create. 

Frequently Asked Questions (FAQs)

1. What is the meaning of C in the R programming language?

The C function stands for ‘Combine’ in the R programming language. This function is utilized for getting the output by passing parameters in the function. You can extract data in three different ways with the use of C in R: using the c(row) command for extracting rows, the c(column) command for extracting columns, and the c(row, column) command for extracting both columns and rows.
Here, you have to provide the value of rows and columns in the function from the dataset that you are utilizing. The function will return a vector in return to this command. Other than that, you can use the c() function for combining two different vectors.

2. What are R functions?

Functions are self-contained modules of code that are used for performing a specific task. Usually, functions take in a particular data structure like value, dataframe, vector, or anything and process it for returning a result. Arguments are passed in these functions in parenthesis for specifying the requirements.
There are two types of functions being used in R: basic and user-defined. The basic functions are the ones that are already available in the R programming language. You can access these functions from various packages or libraries that are available in R. Every function is used for a different purpose and to complete a specific task. Some of the basic functions in R are sqrt(), round(), getwd(), etc. Since it is not possible to complete every action with the help of basic functions, you need to take the help of the user-defined functions by writing your own code to perform certain customized tasks. These functions are developed when you have to perform certain actions multiple times. A function can make this easier for you.

3. What are some of the key features of the R programming language?

There are plenty of ways R can help out data analysts and data scientists. Some of its key features help it to stand out from the general crowd of statistical languages. The key features are strong graphical capabilities, the ability to perform complex statistical calculations, running code without the need of any compiler, data wrangling, data handling and storage capacities, and the ability to generate reports in the desired formats.

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Note: The article was originally written for LinkedIn Pulse by Sameer Dhanrajani, Business Leader at Cognizant Technology Solutions. Data Scientist is one of the fastest-growing and highest paid jobs in technology industry. Dr. Tara Sinclair, Indeed.com’s chief economist, said the number of job postings for “data scientist” grew 57% year-over-year in Q1:2015. Yet, in spite of the incredibly high demand, it’s not entirely clear what education someone needs to land one of these coveted roles. Do you get a degree in data science? Attend a bootcamp? Take a few Udemy courses and jump in? Learn data science to gain edge over your competitors It depends on what practice you end up it. Data Sciences has become a widely implemented phenomenon and multiple companies are grappling to build a decent DS practice in-house. Usually online courses, MOOCs and free courseware usually provides the necessary direction for starters to get a clear understanding, quickly for execution. But Data Science practice, which involves advanced analytics implementation, with a more deep-level exploratory approach to implementing Data Analytics, Machine Learning, NLP, Artificial Intelligence, Deep Learning, Prescriptive Analytics areas would require a more establishment-centric, dedicated and extensive curriculum approach. A data scientist differs from a business analyst ;data scientist requires dwelling deep into data and gathering insights, intelligence and recommendations that could very well provide the necessary impetus and direction that a company would have to take, on a foundational level. And the best place to train such deep-seeded skill would be a university-led degree course on Data Sciences. It’s a well-known fact that there is a huge gap between the demand and supply of data scientist talent across the world. Though it has taken some time, but educationalists all across have recognized this fact and have created unique blends of analytics courses. Every month, we hear a new course starting at a globally recognized university. Data growth is headed in one direction, so it’s clear that the skills gap is a long-term problem. But many businesses just can’t wait the three to five years it might take today’s undergrads to become business-savvy professionals. Hence this aptly briefs an alarming need of analytics education and why universities around the world are scrambling to get started on the route towards being analytics education leaders. Obviously, the first mover advantage would define the best courses in years to come i.e. institutes that take up the data science journey sooner would have a much mature footing in next few years and they would find it easier to attract and place students. Strategic Benefits to implementing Data Science Degrees Data science involves multiple disciplines The reason why data scientists are so highly sought after, is because the job is really a mashup of different skill sets and competencies rarely found together. Data scientists have tended to come from two different disciplines, computer science and statistics, but the best data science involves both disciplines. One of the dangers is statisticians not picking up on some of the new ideas that are coming out of machine learning, or computer scientists just not knowing enough classical statistics to know the pitfalls. Even though not everything can be taught in a Degree course, universities should clearly understand the fact that training a data science graduate would involve including multiple, heterogeneous skills as curriculum and not one consistent courseware. They might involve computer science, mathematics, statistics, business understanding, insight interpretation, even soft skills on data story telling articulation. Beware of programs that are only repackaging material from other courses Because data science involves a mixture of skills — skills that many universities already teach individually — there’s a tendency toward just repackaging existing courses into a coveted “data science” degree. There are mixed feelings about such university programs. It seems to me that they’re more designed to capitalize on the fact that the demand is out there than they are in producing good data scientists. Often, they’re doing it by creating programs that emulate what they think people need to learn. And if you think about the early people who were doing this, they had a weird combination of math and programming and business problems. They all came from different areas. They grew themselves. The universities didn’t grow them. Much of a program’s value comes from who is creating and choosing its courses. There have been some decent course guides in the past from some universities, it’s all about who designs the program and whether they put deep and dense content and coverage into it, or whether they just think of data science as exactly the same as the old sort of data mining. The Theories on Theory A recurring theme throughout my conversations was the role of theory and its extension to practical approaches, case studies and live projects. A good recommendation to aspiring data scientists would be to find a university that offers a bachelor’s degree in data science. Learn it at the bachelor’s level and avoid getting mired in only deep theory at the PostGrad level. You’d think the master’s degree dealing with mostly theory would be better, but I don’t think so. By the time you get to the MS you’re working with the professors and they want to teach you a lot of theory. You’re going to learn things from a very academic point of view, which will help you, but only if you want to publish theoretical papers. Hence, universities, especially those framing a PostGrad degree in Data Science should make sure not to fall into orchestrating a curriculum with a long drawn theory-centric approach. Also, like many of the MOOCs out there, a minimum of a capstone project would be a must to give the students a more pragmatic view of data and working on it. It’s important to learn theory of course. I know too many ‘data scientists’ even at places like Google who wouldn’t be able to tell you what Bayes’ Theorem or conditional independence is, and I think data science unfortunately suffers from a lack of rigor at many companies. But the target implementation of the students, which would mostly be in corporate houses, dealing with real consumer or organizational data, should be finessed using either simulated practical approach or with collaboration with Data Science companies to give an opportunity to students to deal with real life projects dealing with data analysis and drawing out actual business insights. Our learners also read: Free Python Course with Certification upGrad’s Exclusive Data Science Webinar for you – ODE Thought Leadership Presentation document.createElement('video'); https://cdn.upgrad.com/blog/ppt-by-ode-infinity.mp4 Explore our Popular Data Science Online Certifications Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Online Certifications Don’t Forget About the Soft Skills In an article titled The Hard and Soft Skills of a Data Scientist, Todd Nevins provides a list of soft skills becoming more common in data scientist job requirements, including: Manage teams and projects across multiple departments on and offshore. Consult with clients and assist in business development. Take abstract business issues and derive an analytical solution. Top Data Science Skills You Should Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Online Certification Inferential Statistics Online Certification 2 Hypothesis Testing Online Certification Logistic Regression Online Certification 3 Linear Regression Certification Linear Algebra for Analysis Online Certification The article also emphasizes the importance of these skills, and criticizes university programs for often leaving these skills out altogether: “There’s no real training about how to talk to clients, how to organize teams, or how to lead an analytics group.” Data science is still a rapidly evolving field and until the norms are more established, it’s unlikely every data scientist will be following the same path. A degree in data science will definitely act as the clay to make your career. But the part that really separates people who are successful from that are not is just a core curiosity and desire to answer questions that people have — to solve problems. Don’t do it because you think you can make a lot of money, chances are by the time you’re trained, you either don’t know the right stuff or there’s a hundred other people competing for the same position, so the only thing that’s going to stand out is whether you really like what you’re doing. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences?
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by Ashish Korukonda

03 May'16
Computer Center turns Data Center; Computer Science turns Data Science

5.13K+

Computer Center turns Data Center; Computer Science turns Data Science

(This article, written by Prof. S. Sadagopan, was originally published in Analytics India Magazine) There is an old “theory” that talks of “power shift” from “carrier” to “content” and to “control” as industry matures. Here are some examples In the early days of Railways, “action” was in “building railroads”; the “tycoons” who made billions were those “railroad builders”. Once enough railroads were built, there was more action in building “engines and coaches” – General Electric and Bombardier emerged; “power” shifted from “carrier” to “content”; still later, action shifted to “passenger trains” and “freight trains” – AmTrak and Delhi Metro, for example, that used the rail infrastructure and available engines and coaches / wagons to offer a viable passenger / goods transportation service; power shifted from “content” to “control”. The story is no different in the case of automobiles; “carrier” road-building industry had the limelight for some years, then the car and truck manufacturers – “content” – GM, Daimler Chrysler, Tata, Ashok Leyland and Maruti emerged – and finally, the “control”, transport operators – KSRTC in Bangalore in the Bus segment to Uber and Ola in the Car segment. In fact, even in the airline industry, airports become the “carrier”, airplanes are the “content” and airlines represent the “control” Learn data science courses from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. It is a continuum; all three continue to be active – carrier, content and control – it is just the emphasis in terms of market and brand value of leading companies in that segment, profitability, employment generation and societal importance that shifts. We are witnessing a similar “power shift” in the computer industry. For nearly six decades the “action” has been on the “carrier”, namely, computers; processors, once proprietary from the likes of IBM and Control Data, then to microprocessors, then to full blown systems built around such processors – mainframes, mini computers, micro computers, personal computers and in recent times smartphones and Tablet computers. Intel and AMD in processors and IBM, DEC, HP and Sun dominated the scene in these decades. A quiet shift happened with the arrival of “independent” software companies – Microsoft and Adobe, for example and software services companies like TCS and Infosys. Along with such software products and software services companies came the Internet / e-Commerce companies – Yahoo, Google, Amazon and Flipkart; shifting the power from “carrier” to “content”. Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses This shift was once again captured by the use of “data center” starting with the arrival of Internet companies and the dot-com bubble in late nineties. In recent times, the term “cloud data center” is gaining currency after the arrival of “cloud computing”. Though interest in computers started in early fifties, Computer Science took shape only in seventies; IITs in India created the first undergraduate program in Computer Science and a formal academic entity in seventies. In the next four decades Computer Science has become a dominant academic discipline attracting the best of the talent, more so in countries like India. With its success in software services (with $ 160 Billion annual revenue, about 5 million direct jobs created in the past 20 years and nearly 7% of India’s GDP), Computer Science has become an aspiration for hundreds of millions of Indians. With the shift in “power” from “computers” to “data” – “carrier” to “content” – it is but natural, that emphasis shifts from “computer science” to “data science” – a term that is in wide circulation only in the past couple of years, more in corporate circles than in academic institutions. In many places including IIIT Bangalore, the erstwhile Database and Information Systems groups are getting re-christened as “Data Science” groups; of course, for many acdemics, “Data Science” is just a buzzword, that will go “out of fashion” soon. Only time will tell! As far as we are concerned, the arrival of data science represents the natural progression of “analytics”, that will use the “data” to create value, the same way Metro is creating value out of railroad and train coaches or Uber is creating value out of investments in road and cars or Singapore Airlines creating value out of airport infrastructure and Boeing / Airbus planes. More important, the shift from “carrier” to “content” to “control” also presents economic opportunities that are much larger in size. We do expect the same from Analytics as the emphasis shifts from Computer Science to Data Science to Analytics. Computers originally created to “compute” mathematical tables could be applied to a wide range of problems across every industry – mining and machinery, transportation, hospitality, manufacturing, retail, banking & financial services, education, healthcare and Government; in the same vein, Analytics that is currently used to summarize, visualize and predict would be used in many ways that we cannot even dream of today, the same way the designers of computer systems in 60’s and 70’s could not have predicted the varied applications of computers in the subsequent decades. We are indeed in exciting times and you the budding Analytics professional could not have been more lucky. Announcing PG Diploma in Data Analytics with IIT Bangalore – To Know more about the Program Visit – PG Diploma in Data Analytics. Top Data Science Skills to Learn to upskill SL. No Top Data Science Skills to Learn 1 Data Analysis Online Courses Inferential Statistics Online Courses 2 Hypothesis Testing Online Courses Logistic Regression Online Courses 3 Linear Regression Courses Linear Algebra for Analysis Online Courses upGrad’s Exclusive Data Science Webinar for you – ODE Thought Leadership Presentation document.createElement('video'); https://cdn.upgrad.com/blog/ppt-by-ode-infinity.mp4 Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Our learners also read: Free Online Python Course for Beginners About Prof. S. Sadagopan Professor Sadagopan, currently the Director (President) of IIIT-Bangalore (a PhD granting University), has over 25 years of experience in Operations Research, Decision Theory, Multi-criteria optimization, Simulation, Enterprise computing etc. His research work has appeared in several international journals including IEEE Transactions, European J of Operational Research, J of Optimization Theory & Applications, Naval Research Logistics, Simulation and Decision Support Systems. He is a referee for several journals and serves on the editorial boards of many journals.
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by Prof. S. Sadagopan

11 May'16
Enlarge the analytics & data science talent pool

5.19K+

Enlarge the analytics & data science talent pool

Note: The articlewas originally written by Sameer Dhanrajani, Business Leader at Cognizant Technology Solutions. A Better Talent acquisition Framework Although many articles have been written lamenting the current talent shortage in analytics and data science, I still find that the majority of companies could improve their success by simply revamping their current talent acquisition processes. Learn data science courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. We’re all well aware that strong quantitative professionals are few and far between, so it’s in a company’s best interest to be doing everything in their power to land qualified candidates as soon as they find them. It’s a candidate’s market, with strong candidates going on and off the market lightning fast, yet many organizational processes are still slow and outdated. These sluggish procedures are not equipped to handle many candidates who are fielding multiple offers from other companies who are just as hungry (if not more so) for quantitative talent. Here are the key areas I would change to make hiring processes more competitive: Fix your salary bands – It (almost) goes without saying that if your salary offerings are outdated or aren’t competitive to the field, it will be difficult for you to get the attention of qualified candidates; stay topical with relevant compensation grids. Consider one-time bonuses – Want to make your offer compelling but can’t change the salary? Sign-on bonuses and relocation packages are also frequently used, especially near the end of the year, when a candidate is potentially walking away from an earned bonus; a sign-on bonus can help seal the deal. Be open to other forms of compensation – There are plenty of non-monetary ways to entice Quants to your company, like having the latest tools, solving challenging problems, organization-wide buy-in for analytics and more. Other things to consider could be flexible work arrangements, remote options or other unique perks. Pick up the pace – Talented analytics professionals are rare, and the chances that qualified candidates will be interviewing with multiple companies are very high. Don’t hesitate to make an offer if you find what you’re looking for at a swift pace – your competitors won’t. Court the candidate – Just as you want a candidate who stands out from the pack, a candidate wants a company that makes an effort to stand apart also. I read somewhere, a client from Chicago sent an interviewing candidate and his family pizzas from a particularly tasty restaurant in the city. I can’t say for sure that the pizza was what persuaded him to take the company’s offer, but a little old-fashioned wooing never hurts. Button up the process – Just as it helps to have an expedited process, it also works to your benefit is the process is as smooth and trouble-free as you can make it. This means hassle-free travel arrangements, on-time interviews, and quick feedback. Network – make sure that you know the best of the talent available in the market at all levels and keep in touch with them thru porfessional social sites on subtle basis as this will come handy in picking the right candidate on selective basis Redesigned Interview Process In the old days one would screen resumes and then schedule lots of 1:1’s. Typically people would ask questions aimed at assessing a candidate’s proficiency with stats, technicality, and ability to solve problems. But there were three problems with this – the interviews weren’t coordinated well enough to get a holistic view of the candidate, we were never really sure if their answers would translate to effective performance on the job, and from the perspective of the candidate it was a pretty lengthy interrogation. So, a new interview process need to be designed that is much more effective and transparent – we want to give the candidate a sense for what a day in the life of a member on the team is like, and get a read on what it would be like to work with a company. In total it takes about two days to make a decision, and there be no false positives (possibly some false negatives though), and the feedback from both the candidates and the team members has been positive. There are four steps to the process: Resume/phone screens – look for people who have experience using data to drive decisions, and some knowledge of what your company is all about. On both counts you’ll get a much deeper read later in the process; you just want to make sure that moving forward is a good use of either of both of your time. Basic data challenge – The goal here is to validate the candidate’s ability to work with data, as described in their resume. So send a few data sets to them and ask a basic question; the exercise should be easy for anyone who has experience. In-house data challenge – This is should be the meat of the interview process. Try to be as transparent about it as possible – they’ll get to see what it’s like working with you and vice versa. So have the candidate sit with the team, give them access to your data, and a broad question. They then have the day to attack the problem however they’re inclined, with the support of the people around them. Do encourage questions, have lunch with them to ease the tension, and check-in periodically to make sure they aren’t stuck on something trivial. At the end of the day, we gather a small team together and have them present their methodology and findings to you. Here, look for things like an eye for detail (did they investigate the data they’re relying upon for analysis), rigor (did they build a model and if so, are the results sound), action-oriented (what would we do with what you found), and communication skills. Read between the resume lines Intellectual curiosity is what you should discover from the project plans. It’s what gives the candidate the ability to find loopholes or outliers in data that helps crack the code to find the answers to issues like how a fraudster taps into your system or what consumer shopping behaviors should be considered when creating a new product marketing strategy. Data scientists find the opportunities that you didn’t even know were in the realm of existence for your company. They also find the needle in the haystack that is causing a kink in your business – but on an entirely monumental scale. In many instances, these are very complex algorithms and very technical findings. However, a data scientist is only as good as the person he must relay his findings to. Others within the business need to be able to understand this information and apply these insights appropriately. Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Good data scientists can make analogies and metaphors to explain the data but not every concept can be boiled down in layman’s terms. A space rocket is not an automobile and, in the brave new world, everyone must make this paradigm shift. Top Data Science Skills You Should Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Online Certification Inferential Statistics Online Certification 2 Hypothesis Testing Online Certification Logistic Regression Online Certification 3 Linear Regression Certification Linear Algebra for Analysis Online Certification upGrad’s Exclusive Data Science Webinar for you – Watch our Webinar on The Future of Consumer Data in an Open Data Economy document.createElement('video'); https://cdn.upgrad.com/blog/sashi-edupuganti.mp4 Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Our learners also read: Free Python Course with Certification And lastly, the data scientist you’re looking for needs to have strong business acumen. Do they know your business? Do they know what problems you’re trying to solve? And do they find opportunities that you never would have guessed or spotted?
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by upGrad

14 May'16
UpGrad partners with Analytics Vidhya

5.69K+

UpGrad partners with Analytics Vidhya

We are happy to announce our partnership with Analytics Vidhya, a pioneer in the Data Science community. Analytics Vidhya is well known for its impressive knowledge base, be it the hackathons they organize or tools and frameworks that they help demystify. In their own words, “Analytics Vidhya is a passionate community for Analytics/Data Science professionals, and aims at bringing together influencers and learners to augment knowledge”. Explore our Popular Data Science Degrees Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Degrees We are joining hands to provide candidates of our PG Diploma in Data Analytics, an added exposure to UpGrad Industry Projects. While the program already covers multiple case studies and projects in the core curriculum, these projects with Analytics Vidhya will be optional for students to help them further hone their skills on data-driven problem-solving techniques. To further facilitate the learning, Analytics Vidhya will also be providing mentoring sessions to help our students with the approach to these projects. Our learners also read: Free Online Python Course for Beginners Top Essential Data Science Skills to Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Certifications Inferential Statistics Certifications 2 Hypothesis Testing Certifications Logistic Regression Certifications 3 Linear Regression Certifications Linear Algebra for Analysis Certifications This collaboration brings great value to the program by allowing our students to add another dimension to their resume which goes beyond the capstone projects and case studies that are already a part of the program. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Through this, we hope our students would be equipped to showcase their ability to dissect any problem statement and interpret what the model results mean for business decision making. This also helps us to differentiate UpGrad-IIITB students in the eyes of the recruiters. upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 Check out our data science training to upskill yourself
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by Omkar Pradhan

09 Oct'16
Data Analytics Student Speak: Story of Thulasiram

5.69K+

Data Analytics Student Speak: Story of Thulasiram

When Thulasiram enrolled in the UpGrad Data Analytics program, in its first cohort, he was not very different for us, from the rest of our students in this. While we still do not and should not treat learners differently, being in the business of education – we definitely see this particular student in a different light. His sheer resilience and passion for learning shaped his success story at UpGrad. Humble beginnings Born in the small town of Chittoor, Andhra Pradesh, Thulasiram does not remember much of his childhood given that he enlisted in the Navy at a very young age of about 15 years. Right out of 10th standard, he trained for four years, acquiring a diploma in mechanical engineering. Thulasiram came from humble means. His father was the manager of a small general store and his mother a housewife. It’s difficult to dream big when leading a sheltered life with not many avenues for exposure to unconventional and exciting opportunities. But you can’t take learning out of the learner. “One thing I remember about school is our Math teacher,” reminisces Thulasiram, “He used to give us lot of puzzles to solve. I still remember one puzzle. If you take a chessboard and assume that all pawns are queens; you have to arrange them in such a way that none of the eight pawns should die. Every queen, should not affect another queen. It was a challenging task, but ultimately we did it, we solved it.” Navy & MBA At 35 years of age, Thulasiram has been in the navy for 19 years. Presently, he is an instructor at the Naval Institute of Aeronautical Technology. “I am from the navy and a lot of people don’t know that there is an aviation wing too. So, it’s like a dream; when you are a small child, you never dream of touching an aircraft, let alone maintaining it. I am very proud of doing this,” says Thulasiram on taking the initiative to upskill himself and becoming a naval-aeronautics instructor. When the system doesn’t push you, you have to take the initiative yourself. Thulasiram imbibed this attitude. He went on to enroll in an MBA program and believes that the program drastically helped improve his communication skills and plan his work better. How Can You Transition to Data Analytics? Data Analytics Like most of us, Thulasiram began hearing about the hugely popular and rapidly growing domain of data analytics all around him. Already equipped with the DNA of an avid learner and keen to pick up yet another skill, Thulasiram began researching the subject. He soon realised that this was going to be a task more rigorous and challenging than any he had faced so far. It seemed you had to be a computer God, equipped with analytical, mathematical, statistical and programming skills as prerequisites – a list that could deter even the most motivated individuals. This is where Thulsiram’s determination set him apart from most others. Despite his friends, colleagues and others that he ran the idea by, expressing apprehension and deterring him from undertaking such a program purely with his interests in mind – time was taken, difficulty level, etc. – Thulasiram, true to the spirit, decided to pursue it anyway. Referring to the crucial moment when he made the decision, he says, If it is easy, everybody will do it. So, there is no fun in doing something which everybody can do. I thought, let’s go for it. Let me push myself — challenge myself. Maybe, it will be a good challenge. Let’s go ahead and see whether I will be able to do it or not. UpGrad Having made up his mind, Thulasiram got straight down to work. After some online research, he decided that UpGrad’s Data Analytics program, offered in collaboration with IIIT-Bangalore that awarded a PG Diploma on successful completion, was the way to go. The experience, he says, has been nothing short of phenomenal. It is thrilling to pick up complex concepts like machine learning, programming, or statistics within a matter of three to four months – a feat he deems nearly impossible had the source or provider been one other than UpGrad. Our learners also read: Top Python Free Courses Favorite Elements Ask him what are the top two attractions for him in this program and, surprising us, he says deadlines! Deadlines and assignments. He feels that deadlines add the right amount of pressure he needs to push himself forward and manage time well. As far as assignments are concerned, Thulasiram’s views resonate with our own – that real-life case studies and application-based learning goes a long way. Working on such cases and seeing results is far superior to only theoretical learning. He adds, “flexibility is required because mostly only working professionals will be opting for this course. You can’t say that today you are free, because tomorrow some project may be landing in your hands. So, if there is no flexibility, it will be very difficult. With flexibility, we can plan things and maybe accordingly adjust work and family and studies,” giving the UpGrad mode of learning, yet another thumbs-up. Amongst many other great things he had to say, Thulasiram was surprised at the number of live sessions conducted with industry professionals/mentors every week. Along with the rest of his class, he particularly liked the one conducted by Mr. Anand from Gramener. Top Data Science Skills to Learn to upskill SL. No Top Data Science Skills to Learn 1 Data Analysis Online Courses Inferential Statistics Online Courses 2 Hypothesis Testing Online Courses Logistic Regression Online Courses 3 Linear Regression Courses Linear Algebra for Analysis Online Courses What Kind of Salaries do Data Scientists and Analysts Demand? Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? upGrad’s Exclusive Data Science Webinar for you – ODE Thought Leadership Presentation document.createElement('video'); https://cdn.upgrad.com/blog/ppt-by-ode-infinity.mp4 Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses “Have learned most here, only want to learn..” Interested only in learning, Thulasiram made this observation about the program – compared to his MBA or any other stage of life. He signs off calling it a game-changer and giving a strong recommendation to UpGrad’s Data Analytics program. We are truly grateful to Thulasiram and our entire student community who give us the zeal to move forward every day, with testimonials like these, and make the learning experience more authentic, engaging, and truly rewarding for each one of them. If you are curious to learn about data analytics, data science, check out IIIT-B & upGrad’s PG Diploma in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.
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by Apoorva Shankar

07 Dec'16