Data Science Journey: Technical and Non-technical Skills are Equally important
Mulugheta T. SOLOMON and Armando D’ANIELLO ; Masters in Data Science, Rome Business School.
Disclaimer: this document is a draft and is continuously updated.
I think we have heard repeatedly about What data Science is all about but still the new career path is not very clear for many interested youngsters. If anyone still wonder what data science is , here it how it is in brief. As it is clearly explained in datajobs.com (datajobs.com):
Data science is a multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems.
This multidisciplinary field of study about data therefore, requests both technical knowledge but also non-technical knowledge. The data science Venn diagram below shows that data science is a role connects several field of study. Data science is ultimately about using this data in creative ways to generate business value:
The question now is the what makes someone a data scientist or rather full stack data scientist?
Experiences over several years showed that the ability to work with data and be able to create insights from given data existed for long time. However, due to the massive amount of data created every second. The need to collect or making more sense from the enormous data collected is not any easy task and needs update technical skills but also ethical understanding behind implication or impact of using the same data.
If you want to be a data scientist you need to keep the 3 fundamental things in your mind:
1. Extreme curiosity especially with numbers
2. Critical thinking
3. Asking the right questions
Data scientist should learn to asking questions. Asking questions helps to create possibilities and scenarios to consider and no possible problem solving options are left untried.
“Do you see these great buildings? Not one stone will be left upon another which will not be torn down.” (Mark 13:1–2)
On the other hand, if the someone intends to be a great data scientists, certain traits one should particularly own certain traits that others luck.
The article explains well on the full stack data scientists are needed to build end-to-end data science solutions. They are responsible for working on each stage of the data science lifecycle, which generally consists of data engineering, data analysis, data modelling, and model deployment. more information is explained
A data scientist is an umbrella term that describes people whose main responsibility is leveraging data to help other people (or machines) making more informed decisions.
An article explains Full Stack Data Scientist — The Career To Strive For In 2021 | by The Click Reader | The Click Reader | Oct, 2021 | Medium
Items a full stack data scientist should have in his/her toolbelt:
1. Knowledge of Mathematics and Statistics: Mathematics and Statistics are the foundational pillars of data analysis and data modelling. The knowledge of these two fields is crucial to be able to understand the underlying mechanisms and processes that goes into building state-of-the-art data science solutions.
2. Ability to code: Besides the theoretical understanding of Mathematics and Statistics, having concrete programming knowledge is also one of the essential skills for a full stack data scientist. Currently, Python is one of the most popular programming languages used for Data Science followed by R.
3. Experience in working with SQL/NoSQL databases: When working with data, knowing how to store them is crucial to be able to efficiently access them when and where required. Having knowledge of databases and query languages such as SQL is very helpful when working with large amounts of data in real-world projects.
4. Hands-on experience of data engineering: Engineering the data requires a lot of work and effort. It involves extracting, transforming, and loading the data prior to applying any analytical processes on it. A full stack data scientist should know how to perform data engineering as the first step in a data science lifecycle.
5. Knowledge of data modelling algorithms: A full stack data scientist should have knowledge of how data can be modelled using various Statistical or Machine Learning algorithms.
6. Knowledge of DevOps: In contrast to what general Machine Learning practice projects illustrate, real-world projects are very different. Data models should be portable, scalable, and secure when working with different systems and across multiple development teams as well as end-users. Having the knowledge of DevOps allows a full stack data scientist to securely deploy the data model into a production environment for implementation.
Karolis Urbonas — head of Data Science at Amazon and energetic data executive stated here below 3 other key traits that differentiate between a data scientist and a great data scientist, starting with — great data scientist is obsessed with solving problems, not new tools.
The spectrum of data scientist roles is so broad that I will keep this discussion for my next post. What I really want to focus is on what are the distinctive characteristics of a great data scientist.
Over the years that I have worked with data and analytics I have found that this has almost nothing to do with technical skills. Yes, you read it right. Technical knowledge is a must-have if you want to get hired but that’s just the basic absolutely minimal requirement. The features that make one a great data scientist are mostly non-technical. So what are the 3 key things that distinguish a great data scientist?
1. Great data scientist is obsessed with solving problems, not new tools.
This one is so fundamental, it is hard to believe it’s so simple. Every occupation has this curse — people tend to focus on tools, processes or — more generally — emphasize the form over the content. A very good example is the on-going discussion whether R or Python is better for data science and which one will win the beauty contest. Or another one — frequentist vs. Bayesian statistics and why one will become obsolete. Or my favorite — SQL is dead, all data will be stored on NoSQL databases.
These are just instruments that are used to solve problems. A famous American philosopher Abraham Kaplan has coined a concept called the law of the instrument — where he described it
“I call it the law of the instrument, and it may be formulated as follows: Give a small boy a hammer, and he will find that everything he encounters needs pounding.”
It was popularized by the psychologist Abraham Maslow who described it with the famous phrase “if all you have is a hammer, everything looks like a nail”.
The core function of any data-driven role is solving problems by extracting knowledge from data. A great data scientist first strives to understand the problem at hand, then defines the requirements for the solution to the problem, and only then decides which tools and techniques are best fit for the task. In most business cases, the stakeholders you will interact with do not care about the tools — they only care about answering tough questions and solving problems. Knowing how to select, use and learn tools & techniques is a minimum requirement to becoming a data scientist. A great data scientist knows that understanding the underpinnings of the business case is paramount to data science project success.
2. Great data scientist wants to find the solution and knows it’s not perfect.
A very dangerous state for any data scientist is being stuck in the infinite loop of analytic iterations — drilling in, finding insights, zooming out, looking at a macro level, re-defining hypothesis, zooming in again, looking at the most granular details, then re-thinking and round and round. This is called the analysis-paralysis which is basically over-thinking the process by trying to find the “perfect” solution.
A great data scientist understands that there’s almost never a perfect solution, and a simple imperfect solution delivered on time is much better than a hypothetically perfect one late. In fact the Agile software development methodology seeks to prevent analysis-paralysis by employing adaptive evolutionary planning, early delivery and continuous improvement. The mindset of a great data scientist works in the same way — they think about solving their stakeholder problems and know that they need to be redefined when new insights are uncovered.
The main piece of advice here — don’t overthink and over-analyze the problem. Instead — phase out your analysis or modelling process in stages and get feedback from the problem owners. This way you will ensure that the learning process is continuous and it improves the decisions with each iteration.
3. Great data scientist is the ultimate communicator.
As you may see there’s a lot of communication involved in understanding the problem and delivering constant feedback to the stakeholders. But this is just the surface of the importance of communication — a much more important element of this is asking the right questions. Sounds easy, right?
It’s not, actually. The data scientists are much more likely to fall into a trap of the curse of knowledge cognitive bias than any other occupation. This bias “occurs when an individual, communicating with other individuals, unknowingly assumes that the others have the background to understand.”
When the data scientist is scoping out a problem together with the stakeholders or presenting the first findings, it is vital to be as explicit and detailed as possible and not assume that stakeholders know as much as you do. This is very hard as the number of assumptions and underlying methodologies that a data scientist makes can be counted in dozens, even hundreds.
The biggest risk is when the stakeholder briefly describes the problem to the data scientist who doesn’t ask enough questions and assumes what the problem is. Then the data scientist builds a solution that seems to solve the described problem. The lack of asking questions and too many assumptions result in a situation where the final solution actually solves a different problem than the original one and gives an opposite recommendation or a result.
Great data scientists never assume they know something without in-depth analysis, they think in hypotheses which need to be either rejected or proved, and they ask a lot of questions, even if they are 99.9% sure they know the answer.
Wait — what about programming, statistics, math, hacking?
The fact of the matter is — you must have the technical skills and a strong basic foundation to be hired as a data scientist — you can read more about the basic requirements in my previous blog post “How to think like a data scientist to become one” [link no longer available]. This is what’s expected and required from you as the bare minimum.
But to become a truly great data scientist you have to be an ultimate problem solver who is obsessed with understanding the ins and outs of the business case they’re handed.
Original link at cyborgus.com no longer available. Reposted with permission.
Bio: Karolis Urbonas, is Head of Data Science at Amazon and energetic data executive with a demonstrated history of building high-performing data science teams and delivering strategic analytic projects. He blogs at cyborgus.com.
Here are six common steps to consider if you’re interested in pursuing a career in data science:
Pursue an undergraduate degree in data science or a closely related field
Learn required skills to become a data scientist
Consider a specialization
Get your first entry-level data scientist job
Review additional data scientist certifications and post-graduate learning
Earn a master’s degree in data science
Technical skills are not the only thing that matters, however. Data scientists often exist in business settings and are charged with communicating complex ideas and making data-driven organizational decisions. As a result, it is highly important for them to be effective communicators, leaders and team members as well as high-level analytical thinkers.
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Experienced data scientists and data managers are tasked with developing a company’s best practices, from cleaning to processing and storing data. They work cross functionally with other teams throughout their organization, such as marketing, customer success, and operations. They are highly sought after in today’s data and tech heavy economy, and their salaries and job growth clearly reflect that.
As new data scientists ,we hear a lot about importance of the technical skills in data science. Everyone is convinced if someone is a data scientist should first master python, machine learning algorithms or how to code. It is true that these skills even more may be required to complete a data science project cycle.
A data scientist does not know everything but knows how to communicate effectively among the people who know various tasks.
However, a data scientist is not supposed to know everything. With middle and/or large companies, most of the time it is a team work. Data engineers, data analysts, programmers or senior data scientist may work together to find out insights from the data for company or organizational informed decision making. The final communication with the company’s managerial body is usually done by the Senior or data scientists who is supposed to know manage the team of data science division and clearly communicate with the technical people and managerial bodies.
In this case, the transferrable skills or soft skills such asking the right questions to the available data, critically thinking how to describe easily so that the management or large audience can make sense of the data, effective communication, collaboration, team work, presentation and interpretation skills are much more important.
What should you know to become a good data scientist can be the question of everyone who is new to the field.
Overwhelming data is being collected every single measure of time. One of the common questions one may ask: How big is big data?
When do we say data is big enough to be categorized as big data? What are the main list of skills on a Data Science job posts and why we need all those skills?
Recently an article posted in the LinkedIn clarified to some of the many questions from many subscribers who were confused why some data science skills are more seen in the job posts than others. We frequently observed data Science job posts with Microsoft Excel, SQL, Power BI/ Tableau, Python and R and AWS and many more technical skills.
Moreover, a data scientist should good at teamwork, social media mining, communication, business Savvy, Natural language processing/Machine Learning, SQL/NoSQL, Data Visualization, advanced statistics and High-Level Mathematics are among others.
Reference:
- Full Stack Data Scientist — The Career To Strive For In 2021 | by The Click Reader | The Click Reader | Oct, 2021 | Medium
- The Top Most In-Demand Technical and Non-Technical Skills for Data Scientists (risewpu.com)
- Top Technical and Non-Technical Skills Every Data Scientist Needs (mystrikingly.com)
- The value of non-technical skills — A Journey Towards Data (wordpress.com)
- What is a Data Scientist? (mastersindatascience.org)
- Post | Feed | LinkedIn
- What makes a great data scientist? — KDnuggets
