Tag: Data Science

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Small Data for more human-centric data processing

Small Data for more human-centric data processing
Small Data for more human-centric data processing

The use of Small Data allows companies to make a good interpretation of Big Data, enabling a more human-centric approach to data processing.

In this article, we discuss this new trend by introducing data consumption prediction and the evolution of data processing.

Data consumption

The amount of data consumed worldwide in 2022 was 947 ZB. And it is expected to reach 180 ZB in 2025 according to Statista data.

Data Volume 2022-2025 by Mindquest

This increase in the use and consumption of data led companies to invest in new technologies. This is to manage and analyze this data in order to gain in-depth knowledge of their customers.

Thus, in 2021, investment in Big Data and data analytics solutions by companies increased by more than 10%. And it is expected that between 2021 and 2025 the annual growth rate will be 12.8%. This investment by companies foresees the business’s need to obtain qualitative information from the data collected.

Despite this, the human aspect of data processing often falls by the wayside in the face of companies’ imperative need to know their customers in depth to achieve business growth.

As a result, companies are beginning to change their metrics to better account for their customers. Thus, as many as 78% of corporate marketing departments have changed their metrics due to the pandemic.

In this sense, Mindquest analyzes how to move from ‘data centric’ to ‘human centric’ in data processing. And this transition involves the introduction of Small Data.

Small Data implementation

Small Data implementation has become critical for companies that want to make profitable use of Big Data. In fact, interpreting Small Data helps companies ensure a service or product that meets real customer needs.
It is no longer just a matter of collecting a large amount of data, but of deriving truly useful information from it.

Following are 3 ways to move from data-centric to human-centric in the data processing

1. Evolution towards Big Data Marketing

The application of data in marketing strategies is a common practice for marketing teams in all companies. Because of this, they have improved their digital strategies.

Thus, companies use Big Data for analytics, but despite its importance in the current context, its collection and analysis is increasingly complicated. This is due to the increased regulation and knowledge of users on the treatment of the same.

For all these reasons, companies must evolve in the treatment of their data and not only take them into account for the benefit of their business, but also to offer real and tangible benefits to their customers and users, taking them into account.

2. Small Data Implementation

Although companies have invested in Big Data in recent years, according to Gartner, by 2025 70 percent of companies will have shifted the focus of their data strategy from Big Data to Small and Wide Data.

“Small data is an approach that involves less data but still provides valuable insights. This approach includes some time-series analysis techniques or few-shot learning, synthetic data or self-supervised learning.” (Gartner, PR May 19, 2021)

The use of small data enables companies to interpret Big Data well, deepening their understanding of customers and their motivations for doing so. They do this by extracting useful information from each customer and opting for data quality rather than quantity.

Its use will be essential in the coming years as companies begin to base their business strategy on the customer. Consequently, they need to know the reasons that motivate their customers’ behavior in order to adapt to them.

The use of small data will enable companies to understand and draw conclusions from the large amount of data they already have on their customers.

3. Be aware of Wide Data

There are more and more data sources or points of contact between a company and its customers. So much so that marketers use data from an average of 15 sources

In this context, Wide Data is essential for companies. This is because it links together data from a wide range of sources to reach a meaningful analysis.

Thus, “Wide data allows analysis and synergy of a variety of small and large, unstructured and structured data sources. It applies X-analytics, where X is looking for links between data sources, as well as for a variety of data formats. These formats include tables, text, images, video, audio, voice, temperature, or even smells and vibrations:” (Gartner, PR May 19, 2021)

Its use allows them to understand customers’ use of each platform and gain a more comprehensive view of them. In this way, companies are able to adjust their strategies accordingly to better engage with their customers.

Conclusion

As important as data is to business strategy, it does not speak for itself.

The entire data analysis team needs to be able to draw conclusions from the data that truly impact the company’s relationship with customers.

In this sense, understanding the work behind data interpretation is essential for each company to enhance the value of its analytics team, which continue to play a determining role in the future of the company.

Also read our article about the differences between Business Intelligence and Big Data. Both work together on data, but they do not do it in the same way. Business Intelligence software helps companies make decisions based on data and metrics. But what does Big Data have to do with it?

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Data & Business Intelligence Job Descriptions

Data Scientist: Job Description

Data Scientist Job
A Data Scientist is an expert in Data Science, whose job is to extract knowledge from data in order to be able to answer the questions he or she is asked.

Use our template to create a compelling and comprehensive Data Scientist job description to attract top talent.

A Data Scientist is an expert in Data Science, whose job is to extract knowledge from data in order to be able to answer the questions he or she is asked.

In this article you will find all you need to know about the job of Data Scientist, the skilled required, education and training, and the salary expectations.

Also discover what are the differences between Big Data and Business Intelligence

Data Scientist: the job

For the past few years, the job of data scientist has become increasingly relevant due to the growing popularity of Big Data. It is one of the most promising career paths in the IT sector.

Nature of the work

The Data Scientist’s job is based on 4 main missions:

  • Identify the needs and problems that the company entrusts to him/her (several possible areas: marketing, HR, customer loyalty, etc.)
  • Define a statistical model that will enable him/her to respond
  • Build the appropriate tools to collect the data
  • Collect and organize the data to exploit the results. The data can come from various sources.

Required skills of the Data Scientist

Ability to analyse and synthesise

The Data Scientist must be able to anticipate information needs and constantly seek new sources of information.

Technical skills

The mastery of certain technical skills is essential for the Data Scientist. Indeed, they must master NoSQL databases (MongoDB, Hadoop), R programming language, C programming with the Python language…They must also have a solid foundation in statistics as well as notions of machine learning, which can be a real asset.

Curiosity and open-mindedness

To work in this profession, you must also be able to detect the most interesting data. In addition, a passion for information processing and Big Data issues is obviously a plus.

Context

Two engineers from Facebook first used this term in 2008. Harvard Business Review voted Data Scientist as the “sexiest job of the 21st century”. As a result, in large companies, the job is divided into several sub-categories: the data miner (collects data), the data analyst (administers and creates databases), and the data scientist (interprets the data).

Thus, data scientists can be found in different fields, such as the commercial sector or security.

Salary

Depending on the company, Data Scientists work in several areas such as marketing, information systems or the finance department.

Their salary varies between 500 and 800 euros.

Data Scientist: Training and Education

In conclusion, to embark on a career as a data scientist, you need a minimum of 5 years of higher education, with a master’s degree in statistical analysis or computer programming. Many also have a doctorate (bac +8)

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How To Learn Python With Rune

How To Learn Python With Rune
Rune of Learn Python With Rune tells us about his career story and how one should go about mastering this powerful programming language.

Rune holds a PhD in Computer Science and works as a freelance Python consultant specialising in big data and back-end development. When the pandemic hit, he kickstarted the learning platform Learn Python With Rune to teach others how to learn Python and apply it. He tells us about his career story & how to learn Python, how one should go about mastering this powerful programming language.

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You might also enjoy this interview on how to code well.

How did you go from doing a PhD to working in tech?

Back in the days when I started university, I actually didn’t think of doing a PhD in the first place. I was just starting but I thought learning is awesome, so I immediately decided I wanted to get a PhD.

But while I was studying for my PhD, I realised it wasn’t really for me because it wasn’t really deeply about science. It’s more about publishing papers and getting funding to continue your career. 

So after I finished my PhD, I started as a developer mainly in the security area (I’ve been working a lot in the security business.) I realised that the one thing that I liked was getting things done, getting projects done. So, I slowly became also a manager type person and worked a few years as a manager. Then I continued working in a SaaS company as an engineering manager for architecture and back-end teams and stuff like that. 

But then you went back to development. How is that? When did you decide to kickstart Learn Python With Rune?

I realised I missed programming a lot, and that’s actually where my journey with Learn Python With Rune started. 

I wanted to learn programming again. As a manager, you slowly lose touch with programming because you’re not really doing any professional code anymore. And I kind of missed that. 

So, a bit more than a year ago, I got the idea. It was actually when the coronavirus pandemic started. I had more time and was working from home, and I was like “I want to program again.” So, I started this small project.  I started producing small projects, publishing them on a web page, and one thing led to another. And it just escalated. 

Now, I work as a freelance consultant and they hire me and I do programming again in a freelance manner. And the reason I like that is because you kind of get more freedom. So, if you want to have some vacation, you just do it. It’s more freedom. 

Why Python? What makes Python so great?

I had to start somewhere, right? I hadn’t been programming that much in Python professionally, but I’ had been programming in C a lot. C is a really low-level programming language and it’s very effective, but you can make so many errors, pointers and stuff like that. It’s just a pain when you don’t know much because you can just do what a processor can do.

But Python is abstracted away. And what happened with Python over the last maybe 10 years is that it has so many libraries. So you can do everything efficiently. It has been developed a lot, for instance, in data science and big data and stuff like that (I myself work with Python in the big data and back-end side of things.) And you can do all this processing now because you have the libraries that can do all the heavy work, but you just manage it in Python code so it can get beautiful. 

It’s easy to understand, It’s readable. It’s almost super code. That’s the main reason I love Python. But there are also some things that I’m not so fond of. 

Like what? What is Python not so great at?

It does hide some of the things away, some of the objects and how they are represented. When you are programming in C you know everything exactly on a byte level. In Python, it’s kind of hidden away.

And I see a lot of beginners having a hard time and struggling with what an object is and what object-oriented programming is, for instance. Because we say that, in Python, everything is an object, but really, is it?. I don’t know. It depends on the implementation. And then they confuse object-oriented programming on top of that.

So, I think it does a really good job, but there are some areas that are not easy to understand in Python. But the pain you get from that is way less than the efficiency and productivity you can get from writing code in Python. 

How should one learn Python? What are your main pieces of advice?

Nowadays it’s difficult to start actually, in some sense, because there’s so much information out there. So my first advice is to ask yourself: what is it that you want to achieve with Python? What is it that you want to learn? What is it you want to code? 

If you just start thinking “I want to program in Python,” then you start a little bit here, a little bit there. All the information is available. The problem is that it’s unstructured. So you get excited about this little bit here, and then you do that, but they are different types of using Python. 

If you want to program back-end like I’m doing, then that is one kind of doing. If you just want to do data science, that’s a different way. You don’t really need to master programming that well, you just need to use some libraries and understand a little about math and so on. 

So it really depends on what you want to achieve. I think people often go around too much. So, advice number one is figure out what it is that you want with it. 

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Then find one teacher, one style. It’s just easier. If you take a little bit of this tutorial on the Internet, then a different tutorial, people can do things very differently and it can be difficult to have a cohesive approach. 

The third issue is about managing your expectations about how fast it is to learn. When you learn a new language, you can listen to it and understand it. But when you have to express yourself, it’s different. It’s difficult. You don’t know how to say things, but you understand it. And it’s the same with programming. 

Suddenly, when you see the solution, how people solved it, you go “yeah, I understand it all and that makes total sense.” But when you have to write it, you might have no idea how to solve problems. And that’s kind of the same problem you have right when you start. You understand Python, but you cannot express yourself in it. 

So, I think that would be my three main pieces of advice for beginners. 

One: figure out what you want to do. Two: find one tutor or one style of programming, one book. Three: manage your expectations. It takes a bit more time to learn to write Python than to read it. 

What’s the difference between a senior Python developer and a junior one?

There are actually some aspects I think people overlook. 

One of them is that, when you have a junior in a work environment, you need to help them. If you take somebody straight out of college, for instance, there are a lot of things they don’t teach in college. You know, how to do metrics, monitoring, how to ensure everything is healthy in your system. They don’t teach them that, so that’s one thing they’re lacking. It’s the experience.

Another thing that juniors tend to do is focus on building small systems. Most college-educated and self-taught people tend to do small projects because they’re easier and you have greater chances of success.

But there is an enormous difference between having one tiny system with one tiny server and a distributed system with tens and sometimes hundreds of systems that need to interact with each other and you need to figure out what to do. 

What happens when you make changes to this small thing here? How do you rebuild it when it breaks? How do you build systems that scales in features and amount of users and volume of data? 

Juniors usually can solve small-scale problems, whereas a senior developer can handle bigger scale problems. 

Another aspect I noticed over the years is that juniors are often a bit afraid. When starting in a team, when starting to develop, a junior will not be so quick to contribute to it and will want people to check the code more often and to help them more, because they are a bit afraid. 

So, when things go wrong, they don’t really have the confidence to just do stuff. and break stuff and put it back up again. They like that kind of experience and confidence. 

My advice for new people is to build something bigger. Build something with somebody else. 

You might have done tiny projects in college, or you may have worked together with other people for a bit. But try to make something bigger because you need to be able to build interfaces that interact with each other., where somebody builds one piece and somebody else builds another piece. That will teach you the kind of architecture design principles behind all of it.

I still think that’s a less important part today because there’s a tendency to go to all these microservices or services that are small in framework. And that makes them easier to understand, easier to debug, easier to maintain by other people. 

So it’s not as difficult as back in the day when you had this one big monolith that was running everything. Right now, you have small services that are easier to understand, but it also moves the problem somewhere else. How do you find where the problem is when the system goes down? You need to have really really good monitoring to find things nowadays. 

So you actually move some of the complexity over to the infrastructure guys or the SREs (Site Reliability Engineers). That’s why they are paid a higher rate now than they used to be. A good SRE is so valuable when you need to find problems in big systems. 

For more tips on how to master Python, make sure to follow Rune on Twitter, YouTube and Facebook.

He’s working on a new course portfolio focusing on how to use Python for financial analysis, so stay tuned!

Check out more of our interviews from our podcast episodes.

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A Career in Data Science: Unlocking The Power of Data with AI

A Career in Data Science: Unlocking The Power of Data with AI
A Career in Data Science: Unlocking The Power of Data with AI

We chat with Ton Badal, machine learning engineer at London-based DataOps start-up Synthesized; about pursuing a career in data science and the challenges of working with data.

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How did you get started in tech, what made you go for data science career and machine learning in particular?

Since school, I have had an engineering mentality, I’ve always had this problem-solving way of thinking. I’ve always enjoyed math and solving problems. In university, I studied telecommunications engineering and specialised in audiovisual systems, so the processing of audio, images, video and other audiovisual systems from a technical perspective.

There I started doing research in machine learning, AI and data science. I started discovering this super interesting world. After that, I was sure that I wanted to do a data science career. So I went for a master’s in AI. And that’s how I discovered this very, very interesting and challenging world.

What did you find to be the most challenging part of this process of learning data science as career and becoming a machine learning engineer?

When I started university, it was not a clear path yet. Eighteen or fifteen years ago, you couldn’t see the path of a data scientist from start to end. Data science sits between computer science and math. And, throughout my career, I’ve been closer to computer science than to math. But the challenge is that you have to know as much as possible from both worlds. But at the same time combine them as well as possible. So I think it’s been quite challenging to be able to unify both worlds.

What’s the best career advice you have ever been given?

This is not really a piece of advice that someone has given me, but rather something that I’ve seen people do. I’ve realised that, when I was starting to look for jobs and was looking for a career, I was kind of looking for anything. I felt like I was the only one selling myself. But at some point, you realise that it’s important that the company also sells itself to you. The company also has to be interested in the person who’s applying. It’s not just top-down, but also bottom-up. There has to be this mutual understanding. When I started looking for jobs, I didn’t care that much about that. But after a while, I realised that it’s really important to feel confident and be in a good environment. It’s crucial for your career development and for example a data science career.

So, I would recommend to everyone to not just get the first job and be very selective about what they want and what they seek to accomplish. Also, the people who interview you: you have to look at them and ask as many questions as you can about the company. It’s not only about selling yourself, but also about understanding the company and making sure that the step you’re going to take is the best one for you because that’s going to influence the rest of your career.

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What’s your advice for those who want to learn data science?

If you want to learn something, the best way to learn it is to get hands-on, to find a project that you’re interested in. There are a lot of open source projects that require some help. For example, at Synthesized, we’re now going to open source a fairness package. If you’re interested in this field, you can collaborate on many, many different projects. The best way to learn computer science and data science is to get a project, get a data set. Sign up for a Kaggle Competition, for example, and try to solve it and get as close as you can to the top of the ranking.

Need tips on how to find a job in IT? Check out our IT job hunting guide.

What are the biggest issues with working with data these days?

First of all, there is the problem of ending up with a poor signal-to-noise ratio. The amount of data that you can find nowadays is huge. But, many times, this data contains a lot of noise. And, if you are not careful, you are just going to end up with just a lot of noise that renders it useless. 

The second big issue is compliance, so GDPR, HIPAA, etc. If you have data that is not privacy-compliant or that is discriminating against some groups, that’s going to be not only useless, but it’s also going to be illegal to use. So you need to work closely with compliance teams. You need to spend time with the legal team to make sure that you make proper use of your data. 

Finally, there’s the problem of data sets becoming data silos. More and more, to access data, you need a data engineer, a data scientist or a machine learning engineer — someone who can do the magic with the data. It’s getting more and more complex to access the data because doing so requires the knowledge of a data engineer or a test engineer. 

How is Synthesized helping to solve these problems?

Synthesized has a core engine that is able to solve these problems by enabling users to easily access their data products in many different ways. So, for example, let’s take one of the problems that I was mentioning before: working with compliance and privacy. Our engine is able to generate data that is representative of the original data but is free from privacy issues and from even biases. 

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Another of the problems is related to infrastructure, to data silos. Current approaches are data warehouses and data lakes. There are some problems with these approaches, for example, the signal-to-noise ratio in the case of data lakes. There’s a lot of data in there, but it’s very difficult to use. But, the infrastructure problem is also there because the data is very centralised and you need a data engineering team to get to it. So what we’re working on is a new infrastructure called data mesh that aims to decentralise data access. It tries to decentralise all these data products so that each team can access the data independently. Both for internal and for external collaboration.

Can you tell us a bit more about your role at the company?

I’m very lucky to have been a very early employee of the company. I joined at a very early stage, and this meant that, although my official title is machine learning engineer, I’ve been able to touch a bit of everything. 

However, my main role as a machine learning engineer is making sure that the core technology is as good as possible. But that also involves a lot of what a pre-sales person would do. So, going to the clients, asking them for requirements, and making sure that the product works well for them and is as tailored as possible to their requirements. But about also improving the product. 

And there is also some marketing work involved, like developer relationships. We need to push into that direction because we’re a small company with very new technology and we need to make sure that we sell bottom-up, not top-to-bottom. We approach customers as machine learning engineers, as the nerds who sell to other developers, not as the marketing guys who are trying to sell something to them. Otherwise,+j the message doesn’t get through that well.

What’s next for data? Where do you see data science in, let’s say, five years from now?

I think that, right now, we’re in a very crucial moment for data. We are having all these privacy issues, fairness problems, and the users are more and more aware of this. So, we have to make sure that we have the best practices in place, that we make the best that we can with our data but still respect users. It’s going to be a very challenging time. 

At Synthesize, we mainly work with structured data, but I think it’s worth mentioning unstructured data. What’s happening with OpenAI, GPT-3 or other generative models — what’s being done is amazing. It’s a very exciting time. I’m very, very excited to see what the next new thing is going to be.

You’ve been based in London for a while. What do you like the most about the London tech scene?

What I like the most about it is that there are a lot of people working on the same topic, and you can very easily meet people doing really interesting things. And that’s one of the most powerful things when you are doing research or trying to improve your product. Just talking to people, understanding their problems and just having a conversation about something that probably you don’t understand and you don’t even know about. 

Discussing new tech trends with people at other companies, that can really help. You discover new things and go out of your usual boundaries. London is great for that because there are a lot of meetups. Well, there were before corona. But yeah, you can talk to and meet a lot of people. There’s this big ecosystem where a lot of things are happening and there’s so much to learn. I’m really happy to be living here.

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Horizon 2050

Edna’s Garden – Chapter 1

Edna's Garden
Enter Edna's Garden Chapter 2. How an 8-year-old girl with a passion for nature turns the world upside down with her data experiments. By Horizon 2050.

Edna’s Garden: An 8-year-old girl with a passion for nature will turn the world upside down with her data experiments

Edna’s Garden, a story by Miquel Morales.

Discover our last story: Nadia

Edna’s Garden – Chapter 1

“Edna?” an old voice croaked from above. From beneath the pile of dead leaves she had fallen on, Edna could hear the man struggling to breathe. Wheezes and sudden bursts of dry cough formed a rhythmic pattern that spoke of one-too-many cigarette puffs while walking the dog. “For the love of Christ, Edna! Where are you? Where do you think you are going?” The man’s voice was full of urgency and rage, his British accent more noticeable than usual. Edna could not remember the last time she had seen him this mad. Maybe she simply had not.

“Edna!” In her leafy igloo, Edna could hear his steps coming down the hill as he fought his way through the dense vegetation. She held her breath. “Of all the days you could have lost your mind… It had to be today, ah? Of course it had to be today!” Just a few feet away from Edna’s face, a loose branch broke into a dozen pieces under the furious step of a muddy leather shoe. Edna held onto her precious cargo in a protective embrace. It was still warm, much like the pulsating heat that had started emanating from her ankle. She must have sprinkled it upon touching the ground. A stinging pain stabbed her leg in agreement. Great.

“I am losing my patience, little lady. Come out of wherever you are hiding. Now!” The man’s voice was now further away. It was clear that he had assumed that Edna was no longer there and was venturing deeper into the thicket. No, she would not come out! She was tired of all the stupid rules and impositions. And all because of Her. “One last time, lady! Do you want me to tell your father? Is that what you want?” No, he would not tell Dad. He never did. He loved her way too much to want her any harm. “I am going to count to three, Edna. And then, I am going to pick up my phone and call your father.” Nice try, buddy. “One…” Just a ruse. “Two… Picking up the phone, Edna!” “Peter, no!” Darn it.

Edna had just a few seconds to hide her hunting prize in one of the inner pockets of her navy blue trench coat before a hand started digging into the pile of leaves. An angry pair of tired eyes peeped through a hole in the leafy dome. There stood Peter Kahn, the family’s butler. He was soaked in sweat and covered with dirt. He was holding Edna’s Totoro backpack in one hand and a cellphone on the other.

More hurt, than angry, Edna stared back at the man with a defiant expression. “Where is it.” said the butler. “Where did you put it?” Nothing. He proceeded to unlock his phone. “I lost it while running, ok?” said Edna. “Are you happy now?” The man directed her a suspicious look. “Peter,” said Edna pointing at the swollen ankle. “I can’t walk.”

All things considered, Edna was having a great time. She was really trying to keep herself from smiling as passerby directed inquisitive and confused looks at the man dressed in dirty, eccentric butler clothes carrying in his arms a little girl with even dirtier clothes across Central Park on a Tuesday afternoon. She could have easily piggybacked her way through the park and made it a bit less awkward, but Peter was too much of a gentleman to allow that to happen.

Edna looked at the face of the sixty-year-old butler for a moment. His eyes were focused in the winding path ahead, his face as stoic as straight was his posture. He had not spoken a single word since discovering her under the leafage. Neither was Edna expecting him to do so. She knew that look very well after spending most of her life under the care of the man. He would briskly carry her all the way across the park until reaching The Pond, where he would slow down so Edna could mentally annotate the number of swimming ducks at the time and what they were doing.

It was her dad that had introduced her to nature when she was a little kid, before everything changed. She had been studying The Pond’s ecosystem for over a year now. She had built a database and tracking computer program where she carefully registered all the data in hopes that one day her research might be of use to the cool scientists at the American Museum of Natural History. Over the months, the data she collected was enough to start building a model that simulated the little natural environment she so loved. And that was only the beginning.

But this time, Peter did not slow down. Trying to get a quick glimpse of the water over the butler’s shoulders, Edna considered for a moment dropping her precious cargo where it belonged. No. It was too vital to her project’s success.

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The outlandish butler and his broken princess did not look any more fitting while crossing Grand Army Plaza. Peter even had to stop for a couple of minutes in order to explain to a concerned police officer that they were neither part of an anticapitalist street performance nor had they been involved in a limousine crash.

Edna felt sorry for Peter. The gallantry had always been there, but his new attire was simply too much. It did not use to be that way. Not until she broke into their lives and proclaimed that “elegance and taste had to be conquered one outfit at a time.” Peter, like most modern-day family butlers, used to wear what adults called “business casual” clothes.

Edna knew this from the few occasions in which she had been invited – and forced to go – to a classmate’s birthday party. She hated those kids. They were always talking about either cars or horses, summer houses and the coolest technological gadget of the season. It seemed as though their only goal in life was to copy the nearsighted lives of their parents, the superfluous, clean, organized and ultimately sombre lives of wealthy New Yorkers.

She thought for a moment of Tom Collins, that little spoiled brat. She could picture him at the school gates, leaving for home on his ridiculous hoverboard after making fun at the fact that Edna still had to be walked home by “the nanny.” She had heard those things could catch on fire. And she certainly hoped so.

Distant church bells chimed way too many times. They were pretty late. She would be furious, thought Edna with satisfaction. She had been planning this for weeks, yet another fake jewel on her crown of shiny ego.

It all started when Dad announced over dinner that he had decided to invest some money in the new restaurant of a famous art critic he recently met at a fundraising event. The guy’s name was Jeremy Talbot, and, apparently, he was as enthusiastic as Dad about saving the endangered populations of North Pacific short-tailed albatross. “So, how short is its tail compared to that of a normal albatross?” had jumped an excited Edna when her dad mentioned that fact.

But, before she could ask more about that majestic-yet-not-too-majestic-sounding bird, Bianca Salazar – Her – had come up with the brilliant idea. “That’s it, darling. We are having a dinner party!” For a moment, Edna had thought the veins on the woman’s neck would burst out of pure elation. Of course – She had been desperately waiting for such an occasion. Bianca Salazar was tall, thin and evil; her beauty extraordinary enough to make everyone else oblivious to the latter.

She had shown up at their 57th Street penthouse three years after Mom’s death. Edna was only one year old when her mother finally succumbed to the cancer. It was impossible for Edna to recall a single thing about her. She simply had this feeling, a foggy impression of having had a mother a long time ago. Somehow, she knew she came from somewhere – or rather from someone – as opposed to just having been summoned into this world by pure chance. That was definitely what it felt like with her.

Bianca Salazar had simply come along with fake smiles and pretended she had always been there. It did not work that well with Edna. She would not go as far as calling it hate at first sight – Edna was simply too young in the beginning to understand what was going on. It had been more of an awakening. By the age of four, Edna reckoned, she had had enough interactions with well-meaning human beings to recognize one without a soul when she saw it.

Dad was probably the golden standard when it came to evaluating a person’s qualities. He had taught Edna everything cool she knew or cared about, from zoology and astronomy to The Beatles and good adventure stories – The NeverEnding Story was one of her favourites.

Then there was Peter, of course. He had taught her substantially different things, the kind of things Edna wished no one cared about: how to properly eat at the table, how a lady should introduce herself to a stranger, the list of words she was not supposed to use. Well, no – That was unfair.

With her father travelling so much and the witch being, well, a witch, Peter provided Edna and her siblings with the valuable concepts of reliability and selfless generosity.

Edna looked at the butler’s face as they crossed Fifth Avenue on a red light. Peter was an honourable man. The most honourable. Edna wished they had known each other as kids. They would have been really good friends.

To be continued…

Read the next chapter of Edna’s Garden: Edna’s Garden – Chapter 2

Need tips on how to find a job in IT? Check out our IT job hunting guide.

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Growing your career: permanent & freelance IT Consultants

Microsoft Career Paths

Microsoft Career Paths
Here is an overview of the different Microsoft career paths and what every different role implies in terms of roles and responsibilities.

Here is an overview of the different Microsoft career paths and what every different role implies in terms of roles and responsibilities.

The Microsoft Career Paths

Administrator

Administrators oversee the implementation of Microsoft platforms and maintain solutions for storage, networking, computing and security.

Responsibilities and skills include:

  • Managing subscriptions and resources
  • Implementing and managing storage
  • Deploying and managing virtual machines
  • Configuring and managing virtual networks
  • Managing identities

AI Engineer

AI engineers design and implement artificial intelligence solutions by leveraging different MS tools.

Cognitive Services, Machine Learning, and Knowledge Mining are part of their toolset. For Azure, for example, areas of focus include:

  • Natural language processing
  • Speech
  • Bots and agents
  • Computer vision

Data Engineer

In charge of mapping out and executing the management, monitoring, security, and privacy of data. Data Engineers are proficient in a platform’s different data services and tools, using them to:

  • Implement data storage solutions
  • Manage and develop data processing
  • Monitor and optimize data solutions
  • Design data storage solutions
  • Design data processing solutions
  • Design for data security and compliance

Data Scientist

Not to be confused with a Data Engineer, this role requires deep knowledge of data science and machine learning. Expertise in data modelling is a must.

A good MS Data Scientist knows well how to:

  • Set up data lake relationships
  • Learning workspace
  • Run experiments and train models
  • Optimize and manage models
  • Deploy and consume models

Developer

At the frontlines of deployment and operations, developers partner with architects and administrators to design, create, test and maintain cloud applications and services.

Among the core competences of developers:

  • Development of infrastructure and storage
  • Development of platforms and solutions
  • Implementation of security
  • Monitoring, troubleshooting, and optimising solutions
  • Connecting to third-party services

DevOps Engineer

Advocates of agile methodologies for software development, DevOps professionals unify teams, processes and technologies to streamline the product pipeline.

Expertise is required in:

  • DevOps development processes
  • Continuous integration & continuous delivery (CI/CD)
  • Dependency management
  • Application infrastructure
  • Continuous feedback

IoT Developer

Designs, develops and maintains Internet of Things solutions and devices within MS environments. From coding to the set-up of physical devices, the IoT Developer is responsible for:

  • Implementing the Azure IoT solution infrastructure
  • Provision and management of devices
  • Implementing Edge Processing and managing data
  • Monitoring, troubleshooting, and optimising IoT solutions
  • Implementing security

Security Engineer

The title here says it all. Security professionals protect the integrity of data, applications and networks by implementing threat detection and security controls.

Functions include:

  • Managing identity and access
  • Implementing platform protection
  • Managing security operations
  • Securing data and applications

Solutions Architect

The Solutions Architect is actually the first person to be involved in a platform’s deployment process. Architects must have a deep understanding of the entire ecosystem to design solutions that run on it.

They must have expertise in:

  • Deployment and configuration of infrastructure
  • Implementing workloads and security
  • Creating and deploy apps
  • Implementing authentication and securing data
  • Developing for cloud and for other storage
  • Determining workload requirements
  • Designing for identity and security
  • Designing a data platform solution and a business continuity strategy
  • Designing for deployment, migration, and integration
  • Designing an infrastructure strategy

Want to learn more about the various Microsoft career paths and how to pursue them? Explore our comprehensive Microsoft Technologies careers guide.

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Press review Tech Magazine

Weekly News: How Tech Is Rewiring our Brains

Weekly News: How Tech Is Rewiring our Brains
Our brain is being rewired, pushed towards shorter attention spans and shallower forms of reading by feeds and visuals. How Tech is rewiring our brains?

How Tech is rewiring our brains, a few bumps on the road for data science, new AI beats historic videogame trap; and the newest new Internet… Discover our weekly news about Tech & IT.

How Tech Is Rewiring our Brains – ‘We shape our tools, and thereafter our tools shape us.’

How Tech is rewiring our brains?

‘We shape our tools, and thereafter our tools shape us.’ Often mistakenly attributed to the philosopher Marshall McLuhan, this quote by John Culkin has become a symbol of the symbiotic relationship between humans and tech. 

Science writer Nicholas Carr took the concept to a new level in his 2010 book The Shallows: What the Internet Is Doing to our Brains. It was not that well-received at the time. But with every new app that changes the way we communicate and consume information, it gets clearer how relevant Carr’s work was and remains. 

In the book, Carr leaves moral judgements aside and urges us to approach technology from a position of understanding. 

Our brain is being rewired, pushed towards shorter attention spans and shallower forms of reading by feeds and visuals. Only by being aware of these effects, we will be able to stay in control.

Ten years after the book’s publication, Vox’s Ezra Klein sits down with Carr to discuss where we stand today. 

An interview worth checking out.

A few bumps on the road for data science

A new report by software provider Aanaconda sheds some light on the current state of data science and its role in the enterprise. Far from being a consolidated part of today’s business world, the discipline has yet to overcome a few key challenges before reaching maturity. 

Problems demonstrating ROI across the organisation. Difficulty integrating open-source tools. Attracting and retaining top talent. Tackling AI bias and ethics — quite the line-up. 

Thankfully, the report also provides specific recommendations on how to overcome these challenges. Taking a look at them won’t hurt.

New AI beats historic videogame trap

First released in 1979, Zork set a new standard for interactive, story-driven videogames. Rich in storytelling and equipped with advanced language syntax recognition, this text-based adventure prompted players to input actions at every step. 

Now, a new AI built by Georgia Tech and Microsoft Research has become the first to overcome one of the game’s most iconic bottlenecks (i.e. times where players tend to get trapped and die).

Named Q*BERT, the AI leverages NLP and reinforcement learning to avoid getting eaten by a ‘Grue’ monster whenever it moves without any lights around the game’s dungeons.

Also read our article: The Task of Rebuilding AI Infrastructure: Machines and the New Reality

The newest new Internet

Blockchain-powered Dfinity proposes a decentralized and non-proprietary type of network that takes the power away from existing monopolies. 

It does this by allowing for apps to be built and run on the network itself, rather than exist in data centres that are increasingly controlled by large companies like Facebook and Amazon.

This so-called ‘Internet Computer’ is now open to third-party developers and entrepreneurs in a bid to spur a new era of connectivity and development. 

Dfinity launched a privacy-friendly version of TikTok named CanCan to illustrate the platform’s power. Thanks to its architecture, the app is said to do in 1,000 lines of code what Facebook does in 62 million.

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Data & Business Intelligence Job Descriptions

Data Analyst

The Big Data engineer job cdescription
The Big Data engineer job consist of developing, maintaining, testing, and evaluating Big Data solutions.

Data Analyst is the go-to expert for all operations related to the company’s databases. They assemble and processes data in order to assess business activity and make appropriate recommendations. Their job allows them to “make the data speak” by interpreting them.

This relatively new digital profession is essential in all sectors: commerce, finance, banking, insurance…

Also explore the role of the IoT Consultant

What is the role of a Data Analyst?

Create and model databases

Certainly, one of the first missions of the Data Analyst is to collect, process and study statistical data to produce business analysis and provide recommendations. That is to say, the analyst creates and models the various databases necessary to accomplish the tasks, ensuring proper functioning and the regular updating of the database.

Define segmentation criteria

The Data Analyst is also responsible for defining segmentation. To do this, they must find relevant data sources that allow them, for example, to define the target of marketing campaigns or identify consumer trends.

Popularize data and make it accessible

For example, extracting and translating business data into statistical data makes it possible to synthesize and popularize information. This data processing allows company managers and teams to analyze the data and use it to improve performance.

Required skills of the Data Analyst

An appetite for numbers

Above all, to be a successful, you must first of all love statistics. Reports, tables, graphs… are the main working tools of the Data Analyst.

Knowledge in data analysis and statistical methodologies

An expert in Data Analysis musts also have mastery of statistical methodologies and associated mathematical models to set up efficient analysis systems.

Proficiency in IT tools, languages ​​and databases

Then, Proficiency in the DB SQL computer language, as well as in web analytics tools and data mining tools is often essential for data analysts.

Extreme rigour

Moreover, as this is an activity requiring the manipulation of encrypted data, the Data Analyst must be endowed with extreme rigour, having developed and analytical mind and fool-proof organization skills. Concentration is also one of the skills needed to be a good analyst. They also must keep abreast of new legal and regulatory regulations for data management.

Also read the differences between Big Data and Business Intelligence

Within the industry

The Data Analyst is a more than buoyant function which is set to keep developing strongly. With the evolution of the IT landscape, companies face exponential growth in the number of data. Therefore, large companies in areas like finance, e-commerce, marketing, industry and medicine are the most likely businesses to recruit in this area.

Salary of the Data Analyst

The average daily rate is between €400 and €800.

Training of the Data Analyst

In conclusion, to become a Data Analyst, college-level training is required. Companies tend to favour candidates who have followed courses in engineering, statistics, or even computer science.

To go higher up in this function then, it is recommended to pursue a specialized master’s program. Several career paths are possible, including as consultant positions such as Data Scientist, Business Intelligence Engineer, Data Engineer or even Chief Data Officer.

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Growing your career: permanent & freelance IT Consultants

The ‘Holy Trinity’ of Data Science

Data scientist

There are probably dozens of variants of the Venn diagram that Drew Conway proposed a few years ago to capture the core skills of a data scientist. Needless to say, the role has experienced many changes since then, while rapid technological developments and the boom of AI have further propelled the profession to the top of LinkedIn’s emerging jobs ranking.

Well — we couldn’t resist putting forward our own version of the infamous Venn diagram. Like Conway’s, ours is built on three axes. However, our model focuses on broader categories rather than on specific expertise. In today’s ever-changing business world, soft and cross-cutting skills are the truly decisive factors that, in the long run, can ensure adaptability and success.  

Thus, our “holy trinity,” if you will, of data science is made up of:

  • Curiosity
  • Technical know-how
  • Collaboration

Thinking of a career in the field, or wondering if you’re doing this right? Let’s dive into each component.

The importance of a curious mind

Probably obvious, but it’s impossible to talk about science and not mention the innate curiosity that powers it. Whether you plan to explore the possibility of life in other planets or the mysteries of quantum entanglement, it is the thirst for answers to questions and riddles that will make you advance.

This, of course, applies to the problem-solving capabilities required in data science projects. Nevertheless, well-directed technical inquiries tend to fall on shaky ground whenever there are not accompanied by a good contextual understanding. Just because you’re good at playing with data and creating models that produce intricate insights and machine learning experiences, none of it is worth anything if your work isn’t helpful to the overarching goal.

For this reason, the need for curiosity expands to the domain of expertise in which you operate (i.e. finance, political studies, marketing). The more you know about the field of work of your company or department, the better questions you will ask yourself, the useful insights and models you will produce.

Note that we’re highlighting “curiosity” rather than “knowledge.” You’re going to spend many hours working with this data. Make sure it’s something that you are passionate about or at least find interesting.  

Knowing the technical ins and outs

Some describe a data scientist as someone who knows more about math and statistics than your average programmer while having greater coding capabilities than your average mathematician. Although this definition errs on side of oversimplification, it is not totally misguided.

To be successful in data science, you need to be proficient in certain data engineering and coding-related methodologies and practices. It is important not only to know how to build effective code, but also how to efficiently extract and clean data.

Additionally, there is the crucial technical knowledge that has less to do with computer engineering and more with, for instance, data privacy compliance. You must know what data sets you can manipulate and which ones you can’t, which processes can be computed on the cloud and which ones are better reserved for on-premises infrastructure. At the same time, if you work in finance or in any other field where sector-specific concepts are a basic requirement, you will have to dominate those on top of your knowledge of data science.

Playing as a team

This is where soft skills play the biggest role. Interpersonal communication and teamwork have always been one of the key factors of success Their relevance in this hyperconnected world of ours is only increasing.

There must be good cooperation between all teams and stakeholders involved in the process, and, for that, you should be able to communicate efficiently and in a compelling way. It’s not enough with working closely with developers or analysts. Knowing how to present a project in layman’s terms becomes essential if you want to be granted the staff or computational power that you’ll need to complete it.

Apart from this, you need to be well-versed in concepts like Agile development, which help teams streamline the production pipeline. Version control, a unified repository, and a good understanding between development and production are a teamwork-must in today’s IT world.   

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IT Decision-makers Tips & errors to avoid

5 experts to hire to strengthen an IT team

5 experts to hire to strengthen an IT team
We at Club Freelance have prepared a shortlist of experts to hire to and incorporate to strengthen an IT team if you haven’t done so yet.

Beyond your department’s immediate needs, it is important that you keep in mind the mid- and long-term needs of your company. As your organization’s IT leader, you must ensure that you’re building a team capable of staying aligned with the technology and business trends that are most likely to emerge in the following years. That means not only promoting continuous education among your already existing team, but also looking for new talent that will bring in those skills and ways of thinking that will future-proof it. It’s often hard to identify the right candidate or profile in all the clutter, so we at Club Freelance have prepared a shortlist of experts to hire to and incorporate to strengthen an IT team if you haven’t done so yet.

The top 5 experts to hire to strengthen an IT team

A business-savvy data scientist

First of the experts to hire to strengthen an IT team: the data scientist. It’s no secret that a solid data strategy is one of the key components of any respectable CIO’s digital transformation plan. Organisations all over the globe are ramping up their efforts to leverage their data in deeper, more impactful ways, from business intelligence to predictive and prescriptive analytics.

It is no surprise, then, that the data scientist role was in the top 5 of LinkedIn’s latest emerging jobs study. According to the company, data scientist jobs have experienced a 37% hiring growth over the past three years.

The key skills you should look for when hiring a good data scientist include machine learning, data science, Python, R and Apache Spark. However, as data analysis and predictive analytics are increasingly being incorporated into the decision-making process of companies, there is a growing need for data scientists themselves to understand the business.

A business-savvy data scientist eliminates the need for a middleman to translate data insights into business advice and transformation. Furthermore, as someone who can see both sides of the story, they can use data in more efficient and business-critical ways.

If you don’t have such a profile in your team, consider adding it.   

Also read our article: HR Managers: How to Assess the Technical Skills of IT Candidates

A true AI specialist

Often, data scientists are the ones taking over AI and machine learning duties within IT departments. Or at least being one of the main components of the AI team. That’s fine. A data scientist can, of course, become an expert in Ai through training and experience, but it’s not always the case. A true AI expert goes a bit further than the traditional data scientist, having mastered skills such as deep learning and natural language processing.

According to the same LinkedIn report, the AI specialist role has experienced a 74% hiring growth in the last 4 years. That is because hiring a true expert in AI can result in great benefits across several departments and processes within the organisation. AI can optimize operations, help with cybersecurity, come up with valuable customer insights and help you communicate better with your stakeholders by eliminating the lower levels of customer service. But it can do much more. If you have yet to explore this area, we recommend that you do.

A cloud cybersecurity expert

For some time, IT leaders were after all-terrain cybersecurity experts that understood the company’s whole IT ecosystem and could deal with a wide array of cyber threats and vulnerabilities. As the digital environment has grown more complex and cyber-attacks more sophisticated, that figure is no longer the ideal gatekeeper. As it happens with everything else in our economy, specialization is key.

With more and more companies moving their business-critical operations to the cloud ­­ —and with hybrid, public and private cloud models becoming more intertwined— attacks via cloud infrastructure are poised to hit a new high this year. Therefore, it is of vital importance that you look into hiring a cybersecurity expert that is exclusively dedicated to protecting your cloud real estate.

Also read ou discover our interview: Cybersecurity Career Tips From a Ballerina Turned Pentester

A DevOps engineer

A DevOps engineer is a team addition you should consider if you’re looking to optimize and speed up the software development lifecycle. With a silo-breaking mentality, DevOps engineers work to get different IT teams and processes integrated and create a workflow that’s beneficial for everybody.

They achieve so by using their deep understanding of automation tools to develop digital pipelines comprising all stages in the production cycle — From concept and testing to deployment and monitoring.

Their wholistic mindset also makes them great evangelists of DevOps philosophy across your whole team. Greater awareness of process integration and collaboration across teams can only be beneficial for everyone in the longer run.  

Interested in DevOps profiles? Read about this expert’s DevOps career story.

An RPA automation engineer

Not to be confused with the kind of automation implemented by a DevOps specialist, RPA automation deals with processes internal to the IT team, like ticket generation, and to the overall company. An RPA expert can be of tremendous help anywhere where time-consuming, repetitive tasks can be automated.

Think of all the time you could save across your organization by hiring an automation engineer that would lighten your employee’s workload so that they could dedicate themselves to more productive tasks. Definitely worth it.