Introduction

“This hot new field promises to revolutionize industries from business to government, health care to academia,” says the New York Times. People have woken up to the fact that without analyzing the massive amounts of data that’s at their disposal and extracting valuable insights, there really is no way to successfully sustain in the coming years.

Touted as the most promising profession of the century, data science needs business savvy people who have listed data literacy and strategic thinking as their key skills. Anjul Bhambri, VP of Architecture at Adobe, says, “A Data Scientist is somebody who is inquisitive, who can stare at data and spot trends. It’s almost like a Renaissance individual who really wants to learn and bring change to an organization.” (She was previously IBM’s VP of Big Data Products.)

How do we get value from this avalanche of data in every sector in the economy? Well, we get persistent and data-mad personnel skilled in math, stats, and programming to weave magic using reams of letters and numbers.

Over the last few years, people have moved away from the umbrella term, data scientist. Companies now advertise for a diverse set of job roles such as data engineers, data architects, business analysts, MIS reporting executives, statisticians, machine learning engineers, and big data engineers.

In this post, you’ll get a quick overview about these exciting positions in the field of analytics. But do remember that companies often tend to define job roles in different ways based on the inner workings rather than market descriptions.

List of Job Roles in Data Science / Big Data

1. MIS Reporting Executive

Business managers rely on Management Information System reports to automatically track progress, make decisions, and identify problems. Most systems give you on-demand reports that collate business information, such as sales revenue, customer service calls, or product inventory, which can be shared with key stakeholders in an organization.

Skills Required:

MIS reporting executives typically have degrees in computer science or engineering, information systems, and business management or financial analysis. Some universities also offer degrees in MIS. Look at this image from the University of Arizona which clearly distinguishes MIS from CS and Engineering.

Roles & Responsibilities:

MIS reporting executives meet with top clients and co-workers in public relations, finance, operations, and marketing teams in the company to discuss how far the systems are helping the business achieve its goals, discern areas of concern, and troubleshoot system-related problems including security.

They are proficient in handling data management tools and different types of operating systems, implementing enterprise hardware and software systems, and in coming up with best practices, quality standards, and service level agreements. Like they say, an MIS executive is a “communication bridge between business needs and technology.”

2. Business Analyst

Although many of their job tasks are similar to that of data analysts, business analysts are experts in the domain they work in. They try to narrow the gap between business and IT. Business analysts provide solutions that are often technology-based to enhance business processes, such as distribution or productivity.

Organizations need these “information conduits” for a plethora of things such as gap analysis, requirements gathering, knowledge transfer to developers, defining scope using optimal solutions, test preparation, and software documentation.

Skills Required:

Apart from a degree in business administration in the field of your choice, say, healthcare or finance, aspiring business analysts need to have knowledge of data visualization tools such as Tableau and requisite IT know-how, including database management and programming.

You could also major in computer science with additional courses that include statistics, organizational behavior, and quality management. Or you could get professional certifications such as the Certified Business Analysis Professional (CBAP®) or PMI Professional in Business Analysis (PBA). Many universities offer degrees in business intelligence, business analytics, and analytics. Check out the courses in the U.S/India.

Roles & Responsibilities:

Business analysts identify business needs, crystallizing the data for easy understanding, manipulation, and analysis via clear and precise requirements documentation, process models, and wireframes. They identify key gaps, challenges, and potential impacts of a solution or strategy.

In a day, a business analyst could be doing anything from defining a business case or eliciting information from top management to validating solutions or conducting quality testing. Business analysts need to be effective communicators and active listeners, resilient and incisive, to translate tech speak or statistical analysis into business intelligence.

They use predictive, prescriptive, and descriptive analysis to transform complex data into easily understood actionable insights for the users. A change manager, a process analyst, and a data analyst could well be doing business analysis tasks in their everyday work.

3. Data Analyst

Unlike data scientists, data analysts are more of generalists. Udacity calls them junior data scientists. They play a gamut of roles, from acquiring massive amounts of data to processing and summarizing it.

Skills Required:

Data analysts are expected to know R, Python, HTML, SQL, C++, and Javascript. They need to be more than a little familiar with data retrieval and storing systems, data visualization and data warehousing using ETL tools, Hadoop-based analytics, and business intelligence concepts. These persistent and passionate data miners usually have a strong background in math, statistics, machine learning, and programming.

Roles & Responsibilities:

Data analysts are involved in data munging and data visualization. If there are requests from stakeholders, data analysts have to query databases. They are in charge of data that is scraped, assuring the quality and managing it. They have to interpret data and effectively communicate the findings.

Optimization is must-know skill for a data analyst. Designing and deploying algorithms, culling information and recognizing risk, extrapolating data using advanced computer modeling, triaging code problems, and pruning data are all in a day’s work for a data analyst. For more information about how a data analyst is different from a data scientist.

4. Statistician

Statisticians collect, organize, present, analyze, and interpret data to reach valid conclusions and make correct decisions. They are key players in ensuring the success of companies involved in market research, transportation, product development, finance, forensics, sport, quality control, environment, education, and also in governmental agencies. A lot of statisticians continue to enjoy their place in academia and research.

Skills Required:

Typically, statisticians need higher degrees in statistics, mathematics, or any quantitative subject. They need to be mini-experts of the industries they choose to work in. They need to be well-versed in R programming, MATLAB, SAS, Python, Stata, Pig, Hive, SQL, and Perl.

They need to have strong background in statistical theories, machine learning and data mining and munging, cloud tools, distributed tools, and DBMS. Data visualization is a hugely useful skill for a statistician. Aside from industry knowledge and problem-solving and analytical skills, excellent communication is a must-have skill to report results to non-statisticians in a clear and concise manner.

Roles & Responsibilities:

Using statistical analysis software tools, statisticians analyze collected or extracted data, trying to identify patterns, relationships, or trends to answer data-related questions posed by administrators or managers. They interpret the results, along with strategic recommendations or incisive predictions, using data visualization tools or reports.

Maintaining databases and statistical programs, ensuring data quality, and devising new programs, models, or tools if required also come under the purview of statisticians. Translating boring numbers into exciting stories is no easy task!

5. Data Scientist

One of the most in-demand professionals today, data scientists rule the roost of number crunchers. Glassdoor says this is the best job role for someone focusing on work-life balance. Data scientists are no longer just scripting success stories for global giants such as Google, LinkedIn, and Facebook.

Almost every company has some sort of a data role on its careers page.Job Descriptions for data scientists and data analysts show a significant overlap.

Skills Required:

They are expected to be experts in R, SAS, Python, SQL, MatLab, Hive, Pig, and Spark. They typically hold higher degrees in quantitative subjects such as statistics and mathematics and are proficient in Big Data technologies and analytical tools. Using Burning Glass’s tool Labor Insight, Rutgers students came up with some key insights after running a fine-toothed comb through job postings data in 2015.

Roles & Responsibilities:

Like Jean-Paul Isson, Monster Worldwide, Inc., says, “Being a data scientist is not only about data crunching. It’s about understanding the business challenge, creating some valuable actionable insights to the data, and communicating their findings to the business.” Data scientists come up with queries.

Along with predictive analytics, they also use coding to sift through large amounts of unstructured data to derive insights and help design future strategies. Data scientists clean, manage, and structure big data from disparate sources. These “curious data wizards” are versatile to say the least—they enable data-driven decision making often by creating models or prototypes from trends or patterns they discern and by underscoring implications.

6. Data Engineer/Data Architect

“Data engineers are the designers, builders and managers of the information or “big data” infrastructure.” Data engineers ensure that an organization’s big data ecosystem is running without glitches for data scientists to carry out the analysis.

Skills Required:

Data engineers are computer engineers who must know Pig, Hadoop, MapReduce, Hive, MySQL, Cassandra, MongoDB, NoSQL, SQL, Data streaming, and programming. Data engineers have to be proficient in R, Python, Ruby, C++, Perl, Java, SAS, SPSS, and Matlab.

Other must-have skills include knowledge of ETL tools, data APIs, data modeling, and data warehousing solutions. They are typically not expected to know analytics or machine learning.

Roles & Responsibilities:

Data infrastructure engineers develop, construct, test, and maintain highly scalable data management systems. Unlike data scientists who seek an exploratory and iterative path to arrive at a solution, data engineers look for the linear path. Data engineers will improve existing systems by integrating newer data management technologies.

They will develop custom analytics applications and software components. Data engineers collect and store data, do real-time or batch processing, and serve it for analysis to data scientists via an API. They log and handle errors, identify when to scale up, ensure seamless integration, and “build human-fault-tolerant pipelines.” The career path would be Data Engineer?Senior Data Engineer?BI Architect?Data Architect.

7. Machine Learning Engineer

Machine learning (ML) has become quite a booming field with the mind-boggling amount of data we have to tap into. And, thankfully, the world still needs engineers who use amazing algorithms to make sense of this data.

Skills Required:

Engineers should focus on Python, Java, Scala, C++, and Javascript. To become a machine learning engineer, you need to know to build highly-scalable distributed systems, be sure of the machine learning concepts, play around with big datasets, and work in teams that focus on personalization.

ML engineers are data- and metric-driven and have a strong foundation in mathematics and statistics. They are expected to have experience in Elasticsearch, SQL, Amazon Web Service, and REST APIs. As always, great communication skills are vital to interpret complex ML concepts to non-experts.

Roles & Responsibilities:

Machine learning engineers have to design and implement machine learning applications/algorithms such as clustering, anomaly detection, classification, or prediction to address business challenges. ML engineers build data pipelines, benchmark infrastructure, and do A/B testing.

They work collaboratively with product and development teams to improve data quality via tooling, optimization, and testing. ML engineers have to monitor the performance and ensure the reliability of machine learning systems in the organization.

8. Big Data Engineer

What a big data solutions architect designs, a big data engineer builds, says DataFloq founder Mark van Rijmenam. Big data is a big domain, every kind of role has its own specific responsibilities.

Skills Required:

Big data engineers, who have computer engineering or computer science degrees, need to know basics of algorithms and data structures, distributed computing, Hadoop cluster management, HDFS, MapReduce, stream-processing solutions such as Storm or Spark, big data querying tools such as Pig, Impala and Hive, data integration, NoSQL databases such as MongoDB, Cassandra, and HBase, frameworks such as Flume and ETL tools, messaging systems such as Kafka and RabbitMQ, and big data toolkits such as H2O, SparkML, and Mahout.

They must have experience with Hortonworks, Cloudera, and MapR. Knowledge of different programming and scripting languages is a non-negotiable skill. Usually, people with 1 to 3 years of experience handling databases and software development is preferred for an entry-level position.

Roles & Responsibilities:

Rijmenam says “Big data engineers develop, maintain, test, and evaluate big data solutions within organizations. Most of the time they are also involved in the design of big data solutions, because of the experience they have with Hadoop[-]based technologies such as MapReduce, Hive, MongoDB or Cassandra.”

To support big data analysts and meet business requirements via customization and optimization of features, big data engineers configure, use, and program big data solutions. Using various open source tools, they “architect highly scalable distributed systems.” They have to integrate data processing infrastructure and data management.

It is a highly cross-functional role. With more years of experience, the responsibilities in development and operations; policies, standards and procedures; communication; business continuity and disaster recovery; coaching and mentoring; and research and evaluation increase.

Summary

Companies are running helter-skelter looking for experts to draw meaningful conclusions and make logical predictions from mammoth amounts of data. To meet these requirements, a slew of new job roles have cropped up, each with slightly different roles & responsibilities and skill requirements.

Blurring boundaries aside, these job roles are equally exciting and as much in demand. Whether you are a data hygienist, data explorer, data modeling expert, data scientist, or business solution architect, ramping up your skill portfolio is always the best way forward.

Look at these trends from Indeed.com

If you know exactly what you want to do with your coveted skillset comprising math, statistics, and computer science, then all you need to do is hone the specific combination that will make you a name to reckon with in the field of data science or data engineering.

To read more informative posts about data science and machine learning, go here.

When you read about famous women in tech talking about their experiences, you’ll have an anecdote about how she was the only woman in the male-dominated room of tech wizards. At times ignored, women had a tough time getting their voices heard and opinions valued, and that’s putting it mildly. Many of their stories have a common thread of growing up battling stereotypes at the workplace, parental pressure at home, and a myriad unconscious biases.

Well, that’s how it was. Things must have changed. Surely. We are living in such a progressive age, for heaven’s sake.

But have they?

Reading about the recent gender discrimination fiasco at Uber, you can’t be faulted for being skeptical. Uber’s tech teams have very few women—an appalling 15.1%. And to make matters worse, the “underrepresentation” came under public scrutiny only after Susan Fowler, a reliability engineer at Uber, published a traumatizing post about sexual harassment.

It is just more proof of how many battles women have to fight, to couch in nonchalant smiles...

Statistics paint a dismal picture.

In the tech world, sexism seems to be taking much longer (than one would like) to disappear. Elevating their voices is a struggle. The awareness is there. There’s enough talk about lack of gender diversity at workplaces. But where is the conversation, huh? This post is not a feminist rant. We’ll just look at what the numbers are telling us.

Data says that women don’t really enjoy equal representation.



Source: Fortune.com (February, 2017)

• In 2014, women added up to only 17% of tech workers at Google, 15% at Facebook, and 10% at Twitter according to the American Association of University Women.
• In 2014, 11 global software giants published data that only 30% of the IT workforce is female.
• In 2015, professional computing occupations in the US workforce held by women was 25%. This was the same number in 2008, whereas in 1991, it was 36%.
• In the UK, a 2014 study showed that only 1 in 21 IT job applications were women.
• In the US, 25% of the women with IT roles “feel stalled in their careers;” in India it is 45% percent and the UK it is 37%.
• In the US, a 2014 study said that “unfriendly” policies, poor pay, unfair promotion, and a bro-grammer culture resulted in 45% of women leaving their tech jobs after a year.
• Women hold only 26% of digital industry jobs; it is 16% in IT, and 13% in STEM.
• A Stack Overflow survey says that only 8% of the software developers are women.
• Women constitute just 5% of the programmers in the video game industry. However, IGDA’s survey shows an 11% increase since 2009.
• Catalyst, a nonprofit organization focused on expanding opportunities for women, reported that "women in business roles within tech companies are more likely to start at the entry level compared with men.”
• In Silicon Valley, women earn significantly lesser than men in similar roles.

Look at the findings of another study validating much of the stats above.A Study by the Center for Talent Innovation (U.S.): The Athena Factor: Reversing the Brain Drain in Science, Engineering, and Technology (2013)



There’s a reason it’s a boy’s club, and should be.No, there really isn’t.

• After surveying leaders in the IT industry, a Nominet-commissioned report, Closing the Gender Gap, revealed that the UK economy could find itself richer by £2.6 billion if it gave more IT jobs to women.
• In 2015, this is what research found while analyzing code approved by GitHub: “Women’s acceptance rates dominate over men’s for every programming language in the top 10, to various degrees.” Unfortunately, this finding held true only when the women did not disclose their gender.
• CodeFights found that women and men do almost equally well in coding challenges. Look at this infographic.
• Stats show that in specialized coding academies, women students comprise 35%.
• A McKinsey study showed that companies with over 15% of the women in top management roles had noticeably higher debt-to-equity ratios and payout ratios.
• McKinsey says that the annual global GDP could go up to 26% in 2025 if women participated equally in the economy.
• The Peterson Institute for International Economics surveyed 21,980 firms from 91 countries to conclude that increasing the representation of women to 30% in a company that had none to begin with could lead to a 15%-increase in revenue.

Getting back to the original question, no, the numbers don’t quite add up, at least not in Uncle Sam’s country. It is getting worse.

With 57% of the workforce being made up of women, women account for only 5% of tech leadership jobs, 19% of developers, and less than 30% of IT jobs. Microsoft reported in 2015 that women comprise 29.1 percent of its workforce, with only 16.6 percent in technical positions and 23 percent in leadership roles. Only 21% hold leadership positions in the already poor representation of women at Twitter. Only 21% of women in its 17% women workforce have managerial roles.

Except in the UK, US, and Canada, girls do better than boys in science and math at school. But somewhere along the way, this phenomenon gets buried under layers of stereotypes and circumstances, and now we have only 3 of the Fortune 500 tech companies with women as leaders.

3 out of Fortune 500 companies with women as leaders

And you thought scaling Mount Everest was tough.

In the U.S., the percentage of women majoring in computer science fell from 36% in 1985 to 18% in 2012. Girls hold themselves back for so many reasons. Self-perception is often skewed. They are even told that looking geeky with their noses in books is a major turn off for the boys.

Data shows that a whopping share of girls are interested in the problem-solving aspects and the creativity STEM offers. But they typically pick medicine or healthcare as a career choice over computers and engineering. These girls are conscious of the pervasive bias against women; they fear the isolation, sexism, and the lack of recognition they could face at the university or workplace. Some women also find programming boring. Some others believe that programming serves a male master. And stories of a viciously misogynistic Silicon Valley can’t be helping matters.

Women don’t seem to have enough role models. If they could interact or look up to more women playing starring roles in STEM related careers, it will encourage persistence. Who is going to tell them that their contribution will make a difference in the world?

However, we are in an age where fighting for their piece of the pie has been much easier for women than ever before. And, there’s mounting evidence proving how successful skilled women can be and how the world economy can only grow with more women at all levels.

Fairness is not about statistic quality —John Bercow

Fairness is about cleaning out the closet filled with centuries’ old prejudices and fears.

It is about boys at school knowing that smart girls are not intimidating or ugly; it is about girls at school knowing that the world is as much theirs; it is about parents encouraging their daughters to bravely storm male bastions; it is about skilled young women in universities believing in themselves, dreaming, and taking for granted the opportunities that will come their way; it is about women employees knowing that they can work in a safe environment unaffected by sexism, unequal recognition, and condescension; it is about not making men feel guilty for no reason; and it is about companies recognizing that gender disparity has far-reaching consequences and making a conscious effort to mitigate them.

For female programmers, HackerEarth’s International Women’s Hackathon is an opportunity to compete with other skilled developers in an algo-intensive challenge on March 8. So, get your coding hats on and get ready to save the world. (Maybe that’s a bit much. Still.)

“Just keep it simple, silly!”

It is often both exciting and intimidating while starting to learn new stuff, but it is also the only way to keep us updated, with one foot in the future.

Although there is so much of frenzy around Mixed Reality these days, there are really very few developers currently working on AR/VR.

While there already is a lot of shortage of tech talent worldwide, the scales of demand and supply are even more skewed in this particular category.

Here is all that you need to know to start building your first application for Virtual Reality:

Start by Exploring

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Before you sit down and start developing, you’ll need to know a little bit about the ecosystem itself and the scope of the technology in the current landscape. You do that by spending enough time exploring new applications and following relevant industry insiders, developers, and media channels who are already neck deep in the stuff and listen to what they have to say.

For more information on getting started with virtual reality, go here.

Get your tools right

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Don’t worry about having the latest and most expensive VR Gear in the beginning. Just get yourself a $10 Google Cardboard; that’ll be enough to get you started. Also, do not download the latest version of Unity or Google VR as they usually come with several bugs and need a lot of fixing which at the beginning might leave you stumped. Go for the most stable build out there.

The most stable build at the time of publishing of this article is:
Unity3d 5.4.2: Unity – Get Unity – Download Archive
Google VR 1.03: Google VR SDK for Unity

(To download GVR 1.03: Go to the above link. Then, click on “33 commits” under where it says Google VR SDK for Unity and above where it says Branch: master.

Where it says GVR Unity SDK v1.0.3, click on the “< >” button on the far right. That takes you to a version of the repository from a previous date, v1.0.3 in this case.

Finally, click on the green “Clone or download button” and select Download Zip.

*It’ll be a big file of size around 800 MB, but you’ll just need the Unity package which is about 50 MB; you’ll have to download the complete file though, then delete the rest of it later.)

Form a routine

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You might have classes or a job, but if you don’t take some time out every day, then with each day that you don’t get hands on the tools, it will push you back a lot and the next time you open the tools, you might have a hard time figuring out where you left. You might have to start all over again!

What I did and what I recommend is committing at least half an hour each day to familiarizing yourself with the gaming engine you’re working on whether you are able to make significant progress each day or not.

Also, so that you don’t lose focus and interest along the way, I suggest you form a small group of 3–5 friends who are also interested in the space on Whatsapp, Facebook, or wherever you like and make it a point to share at least one article that you come across each day in that group. It will force you to develop a habit of reading something related every day. Do it even if nobody else seems to be participating.

Practice, Practice, Practice

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This is a no brainer; most development on VR depends on how well you know your gaming engine (Unity in this case) and a little bit of programming. And no matter what you think you’ll typically have to put in at least 40–50 hours of productive time on getting a hang of these tools before you can really start building your own stuff. A lot of tutorials are available online which will guide you while making your first application without much of a background.

I suggest that you read as many tutorials as you can to understand these tools. You will be deceiving yourself if you believe that you’ve got the hang of the tools the first time you follow the tutorial and what you create will actually work. You’ll soon discover that it will be hard for you to recall any of it a day or two later, Don’t pull out your hair if your application doesn’t come up as they taught in the tutorial, as what’ll happen most of the time is you’ll have different machines, different SDKs, and different versions of packages that you’ll be using. So, it will be only natural if something doesn’t work quite as it does in the tutorial.

Combinatory Approach

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Do what you know. After spending some time on the tutorials, you’ll have learned a couple of tricks and will be comfortable implementing some of the functionalities really well. Just focus on that and nothing else. Don’t go into your first project with a specific idea. ?Learn what you can do and design an application around that. Don’t let a specific idea bog you down. Remember this, your first project is supposed to be something of a disaster, and there is no shame in accepting that. You’ll get only better from there.

Start small

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Learn that complex applications are made with huge teams over a long duration. Starting off the first time, you might not necessarily have the correct frame of reference of how long it is going to take you to build stuff, so don’t plan any project which you think is going to take more than a couple of weeks. For example, if you’re building a game like Mario, then do so by stripping all the unnecessary items like keeping it to just one level only without any enemies to dodge or points to collect and other additional conditions; just focus on the minimum number of things that will make it work, say, setting the environment and making your player jump and move. You’ll be surprised that the first time around even this is going to take you a huge amount of time. You can always add things later on. Your first application is going to take you an amount of time that is cumulative of all the practice time that you put in and even more. But just stick to it and your routine; it is a part of the process and don’t be afraid to write a little bit of code. If you design it right, you’ll need to do very little of coding anyway to get anything done.

Don’t worry about making it look pretty; worry about it working at all

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When you are just getting started, you’ll want to go in with the idea of everything being perfect. Just make a note that it is anyway going to be time consuming and also difficult the first time, so do not waste your energy trying to make it look good, just keep striving toward making it work at all. The first ever VR application that I built on my own was very rudimentary, yet it took me about a month to build it, and a lot of time was spent figuring out small details. When you get started, you’ll realize that most of the time it will be the small stuff that keeps you from moving ahead. Sometimes, things won’t work because a library that you’re working on doesn’t exist anymore or because an update in the SDK makes a lot of what you’ve done irrelevant. This is part of the process as well, allowing you to learn the details of the process of building.

Here is the link of the application that I built:

https://play.google.com/store/apps/details?id=com.Pratham.ShootemVR

The project it open source, use it to learn and build if you want to. Here is the link to the assets: https://github.com/pratham2504/Shoot-em-VR

Set milestones

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Even though it sounds obvious, you’ll be surprised how easy is it to let days go by without getting much stuff done. Setting small daily milestones will help. Don’t wait if you are not making progress; just ask around or in sometime you’ll convince yourself that it is not working and just quit. Use forums and repositories such as Stackoverflow or Unity Community resources or just ask people at random who’s working on similar things. Most of the time, you’ll find help.

Just remember, anything worth doing is a struggle but if you stick to it, you’ll eventually get there.

And have fun while learning and building cool stuff. You’ll be glad when you show what you built to your friends for the first time even if it may not look all that pretty.

Excited about VR?

Register at the UnitedByHCl hackathon
Happy mixing reality!

Who is a full stack developer?

A good full stack developer is like one of those celebrities who can do it all. They can act, sing, be a DJ, host a show, even direct, and produce! They may not have won an Oscar or a Grammy, but they still have the breadth of experience.They are capable of developing full-fledged applications (Web, mobile, or desktop). They understand both the front-end and back-end and know their way around servers, databases, APIs, MVC, and hosting environments among others. (Also read - Top skills a full stack developer should have)

A good full stack developer is always in demand. There are over 10,000 open positions available on Indeed alone. However, they may not be the best option in all cases.

When to hire a full stack developer

The demand for a full stack engineer is often driven by the requirements of the role. Hiring a full stack developer is a good idea in the following instances:

1. When you need an MVP

   When your operation is lean and the company’s aim is to validate ideas by building a minimum viable product, then full stack developers are your best bet. If there is an ideal role for a full stack developer, it would be to take an idea or feature and turn into a fully-functional prototype.

2. When you need Product Managers

   Full stack developers can make excellent product managers. They understand the business requirements and, at the same time, they are aware of the engineering capabilities. When decisions have to be made by taking all the parameters into account, they are an extremely valuable resource.

3. When cost is a constraint

   When you cannot afford to hire a specialist for each layer of the development process, full stack developers are your saviors. That being said, good full stack developers don’t come cheap. Nevertheless, instead of spending $70,000 each for a front-end, back-end, and network engineer, it is better to opt for one $100,000 full stack developer. (Also read - How to recruit on a shoestring budget)

4. When you need a CTO/Co-founder

   “I have an idea for a brilliant app, but I just need someone to build it”. This is a common infuriating phrase that developers often hear. When you are looking for a CTO or co-founder for a truly symbiotic relationship that involves combining their technical expertise with a shared vision for the business, full stack developers can make great CTOs or co-founders.

When not to hire a full stack developer

Do not hire a full stack developer, when you cannot see a clear value-add. For example, a full stack engineer can be a valuable asset when you are trying to optimize your application for 20,000 users. However, when you have reached a scale where you have millions of active users every day, you will definitely need a specialist or a team for each layer such as a data and infrastructure team. In such cases, a full stack developer will not add as much value as a specialist will.

How to hire a full stack developer

When hiring a full-stack developer, you should look for certain qualities and technical skills.

Qualities of a full-stack developer

With reference to qualities, look for someone who:

• Is interested and passionate about learning new things
• Understands not only the stacks but also different technologies
• Can point you in the right direction for a solution even if they cannot solve it
• Is aware of the latest trends and developments
• Can see the big picture, the vision of the business, and understands the customer’s requirements

Technical skills to look for in a full stack developer

They should have the knowledge and skills across all the layers. For example, if you are hiring a full stack developer for a web application, then these are ideally the technical skills that you should look for:

• HTML, CSS, and Javascript (it is pretty much mandatory!)
• Programming languages (back end)
• Databases
• Version control
• Deployment and hosting
• Third-party APIs/services

(To read more about the top skills a full stack developer should have, go here.)

Things to look for in a resume

Reduce the dependency on a resume as much a possible. When it comes to technical skills, resumes are usually not a true indicator of the technical skills of a developer. The role of a resume ends with the sourcing of candidates. While scanning a resume don’t just look for relevant experience.Also look for other indicators of a good programmer such as contribution to open source, exposure to various technologies and previous projects. If you have an alternative mechanism for sourcing candidates like sourcing from Github, it is much better.

Technical assessment

This is the most crucial step in your hiring process. How you assess the candidates determines the quality of the hire.Conducting a generic algorithmic test as a mechanism for assessing a full stack developer is a total waste of your time.Instead, give them a real-life problem, which will allow you to assess the technical skills and knowledge across all stacks. Here is a sample problem that would give a better idea of how to use a real-life business problem for technical assessment.

Things to assess in the interview

Once you have a handful of candidates who you know to be technically qualified for the job, look for these two things in the interview:

• Ability to deal with uncertainty
• Interest and passion for learning

Apart from gauging their technical skills, give the candidates a problem that they are not familiar with. Don’t just look for a successful output, also look for candidates who are ready to try irrespective of the outcome.So when you hire your next full-stack developer, ensure that you:

• Look for inherent qualities
• Make technical assessment mandatory
• Choose an appropriate mechanism to assess the technical skills

Now that you have a good idea about how to go about hiring a consummate developer, try HackerEarth developer assessment software to make candidate assessment easy, effective, and efficient.

1. How to hire a data scientist
2. The complete guide to hiring a data scientist
3. 8 ways to hire a developer [Actionable tips]

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“What is imagination?…It is a God-like, a noble faculty. It renders earth tolerable; it teaches us to live, in the tone of the eternal.” – Ada Lovelace to Charles Babbage

When Charles Babbage, in 1837, proposed a ”Fully programmable machine” which would be later called an Analytical engine, not even the government who seed-funded his Difference Engine believed him.

Undoubtedly the most influential machine in existence in today’s modern computer.

But back in the 19th century, when the world was drooling over the industrial revolution and railway tracks and steam engines, a machine which could think and calculate looked like a distant dream.

While most see the evolution of these advanced machines such as computers and smartphones as examples of electronic innovation, what people have taken for granted had been an evolution and the hard work of transforming a mechanical device into a self-thinking smart device which would become an integral part of our lives.

Charles Babbage – The father of the computer

In the 19th century, the concept of specialization had not breached the revered halls of universities and laboratories.

Most of the geniuses were polymaths, so was the Englishman Charles Babbage. Charles Babbage was a renowned mathematician, philosopher, and mechanical engineer of his times.

During those days, mathematical tables (such as your logbook) were manually made and were used in navigation, science, and engineering.

Since most of these tables were manually updated and calculated, the values in these tables varied frequently, giving inconsistent results during studies.

While at Cambridge, Charles Babbage noticed this flaw and thought of converting this mathematical-table based calculation into a mechanical product to avoid any discrepancies.

Difference Engine

In 1822, Charles Babbage decided to make a machine to calculate the polynomial function—a machine which would calculate the value automatically.

In 1823, the British government gave Charles Babbage £1700 (probably the first ever seed funding).

He named it the Difference Engine, possibly after the finite difference method is used to calculate.

Charles Babbage invited Joseph Clement to design his ambitious massive difference engine that had about 25,000 parts, weighed around 15 tons, and was 8 feet tall.

Despite the ample funding by the government, the engine never got completed. And in the late 1840s, he planned on making an improved engine.

But that was not completed either due to lack of funds.

In 1989–1991, scientists and engineers studying Charles Babbage’s research paper built the first difference engine, which is now placed in The Museum of the History of Science, Oxford.

How Does Charles Babbage’s Difference Engine work?

Wikipedia says: “A difference engine is an automatic mechanical calculator designed to tabulate polynomial functions.

The name derives from the method of divided differences, a way to interpolate or tabulate functions by using a small set of polynomial coefficients.”

Let’s take an example with a polynomial function R = x² + 1

	X	R	Difference 1	Difference 2
Step 1	0	1	1 (D11)	2 (D21)
Step 2	1	2	3 (D12)	2 (D22)
Step 3	2	5	5 (D13)	2 (D23)
Step 4	3	10	7 (D14)	2 (D24)
Step 5	4	17	9 (D15)	2 (D2)

To solve this manually, you need to solve the equation “n+1” times, where n is the polynomial. So, for the given equation, we need threesteps.

When X = 0, result of R = 1; X= 1, R =2; X=2, R= 5, and so on.

Difference 1 : D11 = R2 (Step 2) – R1 (Step 1) or D12 (Step 2) = R3 (Step 3) – R2 ( Step 2) and so on

So for the Difference 1 column in the table above,

D11 = 2 (R2) – 1(R1) = 1

D12 = 5 (R3) – 2(R2) = 3

D13 = 10 (R4) – 5(R3) = 5

Difference 2 : D21 = D12 (Difference 1 -Step 2) – D11( Difference 1- Step 1), and so on.

By subtracting two consecutive values from the Difference 1 column,

D21 = 3 (D12) – 1(D11) = 2

D22 = 5 (D13) – 3 (D12) = 2

Similarly, for a third-order equation, we can prepare a new column called Difference 3, and calculate it by subtracting two consecutive numbers from the last column.

*The values in the last column or the highest power value always remain constant in the last difference column.*

Since the engine could only add and subtract, some of the values from each column are given to the difference engine to feed the engine with information necessary for further calculations.

Working of a difference engine

Let’s take another example where you have to calculate the result for x = 3 from the above equation (R = x² + 1), and the engine was already given the values of Step 1 and Step 2 columns (Refer to above table). The engine would follow the following steps:

Step 1: To calculate the value for D12, Step 1 difference 2 is added to Step 1 Difference 1, which is 2(D21) +1( D11)=3.

Step 2: This D12 when added with R2, which gives the result for Step 3 = 3 (D12) + 2( R2) = 5

Similarly, to calculate the result for x = 4

Step 1 – For X = 4, Step 2 – Difference 2 added to Difference 1 = 2 (D22) +1 (D12) = 5

Step 2 – Add value from Step 1 to Step 3 result R3, which is 5+5, giving the final value as 10

A difference engine (shown above) consisted of N+1 columns, where column N could only store constants and Column 1 showed the value of the current iteration.

And the machine was only capable of adding values from column n+1 to N.

The engine is programmed by setting initial values to the columns. Column 1 is set to the value of the polynomial at the start of computation.

Column 2 is set to a value derived from the first and higher derivatives of the polynomial at the same value of X.

Each column from 3 to N is set to a value derived from the first-order derivative. To simplify what the difference engine did, here is a simple code for Polynomial Function calculation using C++ –

#include <iostream>
#include <math.h>
using namespace std;

int d; // degree of the polynomial
int i; // 
int c; // 
int value; 
int j;
int p;
int sum;



int main()
{
    cout << "Enter the degree of the polynomial: " << endl;
    cin >> d; // degree of the polynomial
    cout << "The degree of the polynomial you entered was " << d << endl;

    
    int *c = new int[i];
    
    for(i = 0; i <= d; i++)
    {
        cout << "Enter coefficients: " << endl;
        cin >> c[i];
        int c[d+1];

    }
    
        cout << "There are " << d + 1 << " coefficients";
        cout << " The coefficients are: ";
        
        for (i = 0; i < d + 1; i++)
        cout << "\n   " << c[i];
        cout << endl;
        
        cout << " Enter the value for evaluating the polynomials" << endl;
        cin >> value;
        sum = 0;
        cout << " The value is " << value << endl;
        
        cout << "First polynomial is: " << endl;
        cout << c[0] << "x^3 + " << c[1] << "x^2 + " << c[2] << "x + " << c[3] << endl;
        {

            for (i = 0; i <= d; i++)
            p = 1;
            {


                for (j = 0; j <= (d - 1); j++)
                p = p * value;
                sum = sum + p;
                sum = pow(c[0]*value,d)+pow(c[1]*value,d-1)+pow(c[2]*value,d-2)+pow(c[3]*value,d-3);
                
                cout << "The sum is " << sum << endl;
            }
            
        }
             
}

The difference engine was never finished, and during its construction, Charles Babbage had a brilliant idea of using Punch Cards for calculation.

Till then, punch cards that had been used only for the mundane job of weaving would form the basis of future computer programming.

Punch Cards

Before Joseph Jacquard came up with the idea of punch cards, the weaving was done using draw looms. A drawloom generally used a “figure harness” to control the weaving pattern.

The drawloom required two operators to control the machine.

Although till 1801, punch cards were only used for individual weaving, Jacquard decided to use perforated papers with the mechanism, because he found that though being intricate, weaving was mechanical and repetitive.

Working

In the most basic form, a weaving design is made by passing onethread over another.

In a patterned weave, the threads crossing each other are not synchronized by equal blocks but are changed according to the required pattern.

A weaver controls the threads by pulling and releasing them.

When Joseph Jacquard came up with the idea of a loom, the fabric design in it was first copied on square papers.

This design on the square was translated into punch cards. These cards are stitched together in a continuous belt and fed into the loom.

The holes in the card controlled which threads are raised into the weaving pattern.

This automation allowed Jacquard to make designs and produce them again at lesser costs. Keeping this bunch of cards helped to reproduce the same design repeatedly with perfection on the same or another machine.

“Visualizing” the concept of using these punch cards to calculate, Charles Babbage described using them for the analytical engine.

In 1883, Charles Babbage was introduced to ayoung brilliant mathematician, Ada, who later became Countess of Lovelace, byher tutor.

He was impressed with Ada’sanalytical skills and invited her to look the difference engine, which fascinated her.

This formed the basis of a lasting friendship that continued until her death.

Ada Lovelace – The first programmer

Born to British poet Lord Byron and Annabella Milbanke, Augusta Ada Byron married William King-Noel, who was the first Earl of Lovelace.

Ada was a natural poet who found mathematics poetic.

Growing up, Ada’s education and her families’ influential presence got her in touch with a few prestigious innovators and literary figures of her time.

While studying mathematics, her tutor Mary Somerville introduced her to Charles Babbage, who, after his work on the unsuccessful Difference Engine, was working on an ambitious project of a machine which could solve any complex mathematical function (the Analytical Engine).

What you see below is a caricature image of the Analytical Engine as proposed by Charles Babbage.

The important parts of this engine still constitute our modern computers.

Part 1 – The Store, was what we now call Hard disk or memory

Part 2 – The Mill, was what we now call Central Processing Unit (Mill where the churning or production is done)

Part 3 – Steam engine, which would be the source of energy

Ada, impressed by the theory and concept of the Analytical Engine, decided to work with Charles Babbage onthe construction of the engine.

During her study of the Analytical Engine, she wrote a series of notes which explained the difference between a Difference Engine and an Analytical Engine.

She took up Bernoulli number theory and built a detailed algorithm on the process of calculating Bernoulli numbers using an Analytical engine which was demonstrated in Note G of her article shown below.

This made her the first programmer in the world. (This is disputed.)

Though her notes were never accepted, and as there was no funding or investment to back Charles Babbage’s fantastic idea, the analytical engine was never completed.

Here is a simple C++ program to the algorithm developed by Ada Lovelace in her lengthy notes:

// bernoulli_distribution
#include <iostream>
#include <random>

int main()
{
  const int nrolls=10000;

  std::default_random_engine generator;
  std::bernoulli_distribution distribution(0.5);

  int count=0;  // count number of trues

  for (int i=0; i<nrolls; ++i) if (distribution(generator)) ++count;

  std::cout << "bernoulli_distribution (0.5) x 10000:" << std::endl;
  std::cout << "true:  " << count << std::endl;
  std::cout << "false: " << nrolls-count << std::endl;

  return 0;

Charles Babbage declined both the title of Knighthood and baronetcy and instead asked for a life peerage, but that wish wasn’t granted in his lifetime.

He died in 1871 ate the age of 79. Ada Lovelace died at the young age of 36 in 1852.

Her contribution to computer science for having come up with the “first” algorithm still remains one of the greatest controversies in technology history.

You can read one such article here.

Irrespective of these facts, their contribution to the field of computer and programming cannot be ignored.

A super calculator which would be able to solve any mathematical problem and a device which would have the ability to think of ways to approach a problem is what Charles Babbage and Ada Lovelace thought of; this was the founding stone of the first programmable computer.

In the next article, we will discuss the use of Punch Cards and how with all technological developments in Europe, the USA got the first computer!