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Advice & FAQs from Founders Factory data scientist Ali Kokaz.

Search data science online, and you will find an unending trove of technical tutorials and articles, ranging from how to ingest spreadsheet data, to building a multilayer perceptron for image recognition. However, data science is much more than simply building a complex algorithm: its also about empowering your business by creating a culture of data-driven decision-making.

Indeed, as Hal Varian, Googles chief economist, said back in 2009: The ability to take data to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it thats going to be a hugely important skill in the next decades.

Today, speak to any business leader and nearly all will say that data science is a critical focus for their organization. Yet the reality is theyre struggling recent research shows many firms are unfit for data, for a myriad of reasons including organizational capability, lack of talent, poor quality data and collection processes, to name a few.

So what does it take to build a truly effective data science function?

From understanding what it means to be a data-driven organization, to conducting successful data science projects, Ive compiled the guide below using 16 FAQs I often face when helping businesses work through their data challenges.

As Tim Berners-Lee, inventor of the World Wide Web once said: Data is a precious thing and will last longer than the systems themselves.

In a nutshell, data science is the process and ability to turn raw data into information and insights to inform your business decisions. Without it, you are making decisions blind, or based on opinions and assumptions, rather than facts.

Data science can also be used to help identify opportunities, meaning you can find extra user growth, or revenue streams, by understanding your customers and markets more deeply. You can also use data science to help automate or reduce the overhead of certain processes, like evaluating and processing loan applications for a challenger bank, meaning you can cut costs and set the business up to scale.

This is largely the reason why companies are now pouring money into their data storage, analytics and science capabilities to improve operations and decision-making. It is no surprise that some of the biggest winners of the last decade were essentially data companies, like Google or Facebook, as well as less specialized examples like ASOS, who heavily optimize their shopping experience through data. Essentially, those that fail to invest in this area will quickly be left behind.

Without data youre just another person with an opinion, were the wise words of famous statistician W. Edwards Deming, which gets to the crux of what data-driven organizations are.

A data-driven organization is one that uses data to drive business decisions and processes, meaning they are informed when making choices, and decide things in a factual manner, rather than simply based on opinions and anecdotes.

For example, at my previous workplace a leading data management consultancy business decisions that needed to be made had to be backed up with data evidence, with projects prioritized based on data around how much impact they will have. That type of informed decision-making was pivotal, meaning we were so much more well-informed before undertaking work.

Creating a data-driven organization requires two foundations:

A major factor underlying these foundations is consistent vocabulary, terminology and semantics across the organization, and stressed importance on why good data is vital for this to work this is so that employees collect and store data properly rather than seeing it as another chore on their to-do list.

This is pivotal to the success of a data department within any organization. There are a few steps I take within my department to ensure this happens:

A fundamental part of building an effective DS team is to set out how youre going to measure success. This is where critical business KPIs come into play! Its always important to make sure you measure the success of the data team directly in relation to business goals. For example, this could be the number of customers gained through data science projects or time saved through automation.

You could also measure the interaction of the business with the data outputs as a measure of success. For instance, how many people are using the dashboards and reports the team has built? What decisions are being made off the back of them?

Typically, part of the project-definition process is defining success criteria. When these are hit, a project can be seen as achieving its targets; hence using these as KPIs can also be helpful.

In many aspects, this statement makes a lot of sense. However, a good data science project to me is one that produces the biggest impact on the business, in the shortest amount of time, and continues to drive business impact moving forward.

Working with various businesses, Im always most concerned with the impact a project has, rather than the accuracy, quality or performance of the model in a project.

Id also like to caveat that with the fact that fastest is not always best. Taking slightly longer with a project to future-proof or productionize more efficiently can pay off more in the longer term.

As companies collect ever more data about their customers and their product usage behaviors, a rising challenge facing many businesses is how to analyze this data to derive useful insights.

Before undertaking any project, I always start with the questions below to inform planning and objectives:

I cannot overstate the importance of this! When I work with startups, one of my first tasks is aligning on terminology, but it should be established for any team for the following reasons:

A well-defined workflow for data science applications is a great way to ensure that various teams in the organization remain in sync, which helps to avoid potential delays, financial loss, and especially projects going sideways without conclusive success or failure.

There are several suggested workflows currently in circulation, with many building on existing frameworks in other data fields, such as data mining. While theres no one-size-fits-all solution to all data science projects, often components depend on the company and team objectives. In my experience, there are certain steps that should be ubiquitous in all data science teams, accompanied by common approaches. These include:

Data science and related fields of AI and machine learning are challenging assumptions upon which societies are built. The more data a business collects, the more powerful the organization is relative to the individuals.As a result, this presents a number of ethical challenges to be aware of when building data products, which include:

For further reading, its worth checking out Googles numerous blogs on fairness.

This really depends on the use case, but the majority of the time, no. Data for insights is only useful in sensible aggregation, and not on a personal level. Usually, a middle ground is reached where some PII is collected that has been agreed is useful (such as address) but not all.

First and foremost, you should securely store the sensitive data separately and limit access to this through correct permissioning and requesting. The remaining informative data can be open, with identifying data being anonymized (using a random user_id, for example). You could also impose transparency of what the data is being used for, ensuring data is only used for the reasons stated by stakeholders or the business.

Other things you can do include policies to limit accessibility, by setting minimum granularity on dashboards, for example. You can revisit these policies regularly as the business grows.

Scaling a data science team effectively is more than just hiring great people. In my experience, there are multiple areas and things you need to consider and maybe alter, including:

When thinking about building a team, its vitally important to think about the overall skillset of the team, rather than simply what each team member brings individually. There are multiple methods and approaches you can use to define what the team needs to look like, but thats a whole other guide! But what common skills/traits do I look for within any team member?

Some others to consider also include:

When working, especially in a smaller business, you will spend a large amount of time with that person, its important to try and understand whether that individual will fit in with the rest of the team, but also if they will enjoy working there. I usually do this in the form of two chats one at the start of the recruiting process and one at the end.

The reason for splitting into two is I want to see how the candidate behaves around new people, and then how they perform in front of someone they are now more comfortable with. Does their attitude change? Now they are more comfortable at the end of the process, its a chance to see if they are naturally more introverted/extroverted. Does their professionalism change?

My questions also revolve around previous experience how did they act with previous colleagues? What do they say about previous employers? What did they enjoy? What did they not enjoy?

I also use this as an opportunity to understand more about their aspirations where do they want to be? What do they want to develop? What do they look for in a role?

For culture fit, I try to involve at least one other member from the team to see how they get on. An important point here is you need to find someone right for the team, an introvert in an extroverted team wont work well and vice versa.

Typically, Ill split this into two parts:

Here, Im looking at how they approach a problem, hence a time-limited exercise means they cannot create the most complex solution, so they will have to make decisions on what to simplify. How do they assess these trade-offs? How do they communicate them? Do they identify and communicate caveats? How do they link the problem to the business? Do they try to understand the impact of the outcomes?

If I need to drill further into technical ability, I use this as an opportunity to discuss what they would have done if they had more time. What do they know about a specific topic? How in-depth is their knowledge?

I am assessing this throughout the whole interview process, especially through the take-home task stage. How do they present their work? What medium do they use? Do they cover all aspects of a project or a problem? Can they describe complex concepts clearly? In a non-technical way? Do they listen intently to my questions? Do they take time to think about an answer? Do they try to clarify questions?

I usually also reserve a few questions about how they got on with their teams and previous presentations and how did they build rapport with the business? How much contact did they have? Ask them to talk me through a good presentation they had.

Another aspect to pay close attention to is cues in their emails. How are they worded? Short? Long? Full of grammar/spelling mistakes? How formal?

This is a complex one, and will vary massively from one individual to the next, but managers still have a huge role to play in keeping staff happy. This is especially important in an area like data science, where employee churn is high, and roles are always available for superstar individuals. From my experience, there are a few areas I think about in terms of team retention:

Data science is a fast-moving field, and many data scientists feel left behind at work if not continuously developing and learning. Set aside regular time for the team to discuss and pursue development opportunities, it can be as simple as setting some time aside every Friday for members to pursue something extracurricular.

One critical thing I have experienced is that a lot of teams have training budgets to allow for courses but do not set aside time for the team members to train in those learned skills. Allow your team time to hone these skills, in addition to paying for attending courses.

Also, feedback is a two-way street. Allow your team to be able to give you feedback, too, so they can inform you how best to manage them and get the best out of them. The one point I never change, however, is where I give this feedback, its always in private, and its always constructive.

As data science becomes an increasingly integral part of any business, navigating the evolving complexities of creating a powerful data engine has never been harder. Yet, shining a light on the common challenges faced by many firms shows that good data science requires a laser-sharp focus on fundamental data principles and ethics, and building a data-driven culture. Those businesses willing to invest the time and resources to become a truly data-driven organization will be positioning themselves for success in the years ahead.

Ali Kokaz is a data scientist at Founders Factory.

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Creating a powerful data department with data science - VentureBeat

We need to improve student-staff ratio, citation, placement and perception, says Vice-Chancellor

We need to improve student-staff ratio, citation, placement and perception, says Vice-Chancellor

The performance of the University of Madras in its first-ever participation in QS (Quacquarelli Symonds) World University Ranking has pointed to the need for it to focus on several areas, Vice-Chancellor S. Gowri has said.

The university is ranked 547 th among the world universities. Its work on research in science has pushed up the rank, he said.

It has performed among the top 38% and the citation impact of its research papers is 48. The citation of the research papers globally is the strongest indicator for the university, the ranking organisation has said. The citation per faculty is 94.2/100.

The university is considered a small institution, but the research intensity is very high, QS officials have said. The university has 16.4 million recorded publications and 117.8 million citations.

Placement in the university is lower, compared with global institutions. The perception of the university needs to improve. We need to improve the student-staff ratio, citation, placement and perception. We have to introduce new subjects. These are the areas that we need to focus on to improve our ranking, the Vice-Chancellor said.

The performance motivated the university to apply for Shanghai Ranking, whose parameters would be tougher, he said.

The University of Madras recently signed an agreement with the University of Melbourne for a joint degree in physics. The university is proposing such programmes in English and psychology, too.

Madras University was once a multidisciplinary university. We have faculty in western music and architecture. According to the UGC, architecture is not engineering but science. We could revive these faculties, he said.

This year, the university has granted permission for starting undergraduate courses in artificial intelligence and data science; computer science with artificial intelligence; and computer science with data science. There has been a request for management studies and data science too.

The aim is to be ranked globally within the top 500. Our students will be able to pursue higher studies at overseas universities and will have better placement opportunities globally, Mr. Gowri said.

It has been a good year for the university as it has received 132 applications from students abroad. Last year, only seven students had been admitted. This year, 25 have been admitted so far, said Rita John, who heads the committee that oversees the International Centre for the University of Madras.

To make the university attractive, there is a proposal to offer a spoken Tamil course for foreign students.

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Research in science pushed up QS ranking for University of Madras - The Hindu

After an eight-hour day Wednesday and a judging panel deliberation Thursday, the inaugural Hackathon has concluded, and the winners have been announced.

The successful team who named themselves Team Agriculture came from a variety of universities and was able to put their study of data analytics to the test. They also receive priority consideration for JP Morgans AI & Data Science internship program

Team members included Niklas Tecklenburg from Glasgow University, Athithya Balasubramani from Queen Mary University, Tia Shah from Kent University, and Zhaomian Zhao who recently graduated from Henley Business School.

It was very interesting as a challenge, based on sustainable development goals to solve a real-world problem which you dont come across very often in university situations, so its very useful, said Balasubramani. The world is facing a food and pollution crisis, and diets are going to be impacted. In that scenario, we need solutions like these.

While the solution we actually came up with was not practically possible for anyone with dietary limitations, we managed to come up with the most optimized solution in the time allowed, he added.

Team Agriculture used a cost-function analysis in the neural network algorithm, a process that took some time to go through all the possible combinations.

We had to play around a lot with cost-function to make sure the algorithm works properly, and to make sure you get the best nutrients while minimizing carbon emissions, said Shah. It was a really good opportunity to see how data science can be used to solve genuine problems. This is an issue thats very significant especially in low-income countries, and its good to see the possibilities that are out there that also help protect the environment.

The prize of being bumped up the list of applicants for the JP Morgan AI & Data Science internship program is also an exciting outcome for the students, especially given they are at the beginning of their data analytics journey.

Being a student from India, it can be hard to get a part-time job while also studying that meets your visa requirements, said Balasubramani. Currently I work at McDonalds in between the hours that Im studying so if a company like JP Morgan offers an internship, its of course very appealing.

Shah similarly says an internship can open a lot of doors.

Im still figuring out exactly what I want to do in the field and its obviously very exciting to have the prospect of this kind of internship, she said. Ive been looking into software development and cyber security, as well as AI this has been a recent interest thats definitely been heightened by this conference. Even looking around today there are so many interesting things you can do with AI, and its nice to see how each area is being used in a real setting, its not just theory.

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AI Summit 2022: Team Agriculture Wins Hackathon IoT World Today - IoT World Today

Join one of the leading data science programs in the nation and accelerate your high-tech career in data science.

The MSDS degree is a professional masters program designed for students who want to begin or advance their careers in data science. The program is available full-time or part-time. Classes begin every fall quarter and meet in the evenings on the University of Washington campus.

The industry-relevant curriculum gives you the skills to extract valuable insights from big data. In this program, you will learn expertise in statistical modeling, data management, machine learning, data visualization, software engineering, research design, data ethics, and user experience to meet the growing needs of industry, not-for-profits, government agencies, and other organizations.

The curriculum consists of eight core courses and a two-quarter capstone project. The capstone project gives students the opportunity to work on a data science challenge facing an external organization.

The MSDS program can be completed full-time or part-time. Full-time students take two courses per quarter and attend classes two evenings per week. The full-time program is 1.5 years in length. Part-time students take one course per quarter and attend class one evening per week. The typical part-time student completes the program in 2.5 years. Approximately 80 percent of MSDS students are full-time and 20 percent are enrolled part-time.

Discover if the full-time program or the part-time program is the best fit for you here.

MSDS alumni work at top companies, including Amazon, Boeing, Facebook, Google, Microsoft, T-Mobile, and Zillow. Our graduates also pursue careers at leading not-for-profit organizations, such asSeattle Childrens Hospitaland theInstitute for Health Metrics and Evaluation.

Source: 2019 MSDS Alumni Survey

The MSDS program offers dedicated career services to students, including an annual Data Science Career Fair held every October. Learn more about our career outcomes and services on our Careers page.

In the MSDS program, we have a student body made up of more than numbers. Our students have strong undergraduate grades and technical skills, but we also look for more than that when making a cohort. We admit students who have a diverse set of backgrounds and perspectives. Because of this, our program is able to offer a unique, vibrant experience.

The incoming cohort reflects this diversity. There are over 20 majorsrepresented. Our incoming students have professional experience in a wide range of industries, including aerospace, energy, finance, healthcare, technology, telecommunications, and more. Of the students who will begin our program this fall, women make up more than half the total. They also come from around the world, with 59% coming from eight different countries. The countries represented include Argentina, Chile, China, Ethiopia, India, Pakistan, Taiwan, and South Korea.Read more about our incoming cohort on our Class Profile page.

The University of Washington is one of the worlds preeminent universities. The UW is ranked No. 7 in the world in U.S. News & World Reports Best Global Universities rankings.

The UW also has deep ties to the tech industry in the Seattle area and beyond.A UW degree provides alumni with a competitive edge on the job market.

Beyond its excellent academics, the University of Washington features one of the most beautiful campuses in the nation. Located just four miles from downtown Seattle, the campus offers stunning views of Mount Rainier and Lake Washington.

The M.S. in Data Science program gave me the knowledge and confidence to take the leap and change jobs.

Charles Duze, 19, Data Science Manager, Shopify

Read his story.

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Data Science Masters

The one-day event featured speakers from SAS Institute, Duke University, N.C. A&T State University, BNH.AI and The Redwoods Group.

What impact does analytics have in our society? How can traditional analytics and AI approaches be redesigned to enable responsible and transparent application to societal issues? How can analytics support social good?

These are just a few of the questions discussed during the 2022 Elon Analytics Day, hosted by the Center for Organizational Analytics.

Cynthia Rudin, director of the Interpretable Machine Learning Lab at Duke University and recipient of the 2022 Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity and 2022 Guggenheim Foundation Fellowship, kicked off the event sharing how the lack of transparency in machine learning models used in the decision-making processes can have serious societal consequences.

Providing specific real examples, Rudin showed how using black box models, which are very complex and opaque, in combination with typographical errors in human-entered input, can result in flawed parole decisions and show bias. Black box models are also proprietary, which raises the issue of trust, particularly when accountability and transparency are needed in decision-making processes impacting human lives.

When it comes to high-stakes decisions in domains such as criminal justice and healthcare, Rudin recommends using models that allow humans to interpret the results and immediately detect obvious errors. Her research has established that in most instances of these societal contexts, transparent models are not less accurate than traditional black box models.

As examples of effective and transparent scoring models, Rudin shared optimized scoring system models that her lab has designed for medical applications, enabling doctors to better interpret patient data to diagnose certain conditions. These scoring systems are simple and accessible, and easily interpreted and applied by medical experts. Their optimal accuracy is ensured by a complex machine learning algorithm that does not need to be understood by medical experts. Rudins pioneering work has shown that it is indeed possible to combine transparency, interpretability which is important for societal applications with high predictive accuracy and technical fidelity.

Patrick Hall, principal scientist at BNH.AI, echoed the need for transparency and accountability in his session on fairness, governance and the future of AI. Hall, who is a co-author of a National Institute of Standards and Technology paper on this topic, noted there are serious risks with AI, such as algorithm discrimination and data bias, lack of transparency and accountability, and data privacy problems. These risks can lead to serious harm, which is often concentrated on marginal groups and people who lack fundamental access to the internet and therefore do not even appear in data.

Halls advice on how to address these problems lies with the need to govern the people who create AI and computer software. You cannot govern the software because it is an inanimate object, he said. You need to govern the creators of the software. He suggests there should be incentives for AI fairness and serious legal consequences when there is harm done due to misuse and avoidable biases in AI models.

Presenters also shared examples of how analytics can help nonprofit organizations optimize their limited resources while achieving their goals. Natalia Summerville, who leads a team of data science practitioners within the Advanced Analytics Center of Excellence at SAS Institute, shared four prescriptive analytics applications that helped:

Optimization and prescriptive analytics provide a smart way of coming up with the best solution without necessarily having to enumerate all possible solutions, Summerville said.

In looking at how analytics can be applied in hunger relief supply chains, Lauren Davis, professor in the Department of Industrial & Systems Engineering at North Carolina Agricultural and Technical State University, presented predictive and descriptive models to quantify the availability of supply over time, characterize demand, and optimize the distribution of uncertain supply to ensure equity and improve food access. She used data from a local nonprofit hunger relief organization and took into consideration the distinguishing features of the hunger relief supply chains uncertain supply, varying shelf life, volunteer resources, storage capacity, balance equity, efficiency, effectiveness, and uncertain demand.

Davis noted the management of donated food supply in non-profit food distribution is challenging, and the pandemic added more challenges, such as increased demand and fewer donations due to food supply chain shortages.

In the executive wrap-up, Kevin Trapani, founder and CEO of The Redwoods Group and Love School of Business executive-in-residence, encouraged the audience to:

Elon Analytics Day provided a wonderful opportunity for students, faculty and the larger community to learn from prominent leaders in the field of analytics about what is involved in applying analytics or AI to decisions that have a human impact, said Manoj Chari, director of the Center for Organizational Analytics and assistant professor of management information systems. Like any other technological or scientific innovation, analytics and AI can make great contributions to the solution of societal problems, but its ill-considered and careless use, even with the best of intentions, can cause harm to individuals lives and can perpetuate historical biases and inequities. The presentations during the Analytics Day conference shed light on both of these important aspects of Analytics applications to society, while highlighting related analytical, legal and ethical issues that are subjects of current research and debate.

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Elon Analytics Day explores the role of analytics in society - Today at Elon