Every business, regardless of its size, collects data. Whether it is financial data, HR data, traffic data, or sales data, modern businesses using digital tools cannot avoid gathering mountains of data.

The problem with business data is that few businesses use it to its fullest potential. Buried in each companys data vaults are clues to making better decisions, identifying opportunities, and optimizing the outcomes of whatever business they do. To uncover and unravel those clues, businesses must engage in the field of data science.

Data science is no longer a nice-to-have or an expensive experiment for businesses, says Jan Maly, Data Science Lead at STRV. Its vital for gaining a competitive edge today. AI is now attainable, affordable and, most importantly, a necessity for almost all businesses.

STRV is a software design and engineering team with nearly 20 years of experience in developing digital products that help companies unlock business opportunities. STRV believes that there are four data science myths that can keep companies from embracing the power of data science.

Obviously, doing the work of data science will cost companies something. At the least, companies will need to make room in the budget to obtain or develop software that can tame data and extract understanding. However, when the impact of applied data science is understood, those expenses can be better seen as investments that lead to increased efficiency, effectiveness, and sales. The understanding gained from data science allows companies to automate processes, increase speed, and mitigate human errors, all of which save companies money.

For most retail businesses, product descriptions provide a wealth of data. Utilizing that data to categorize products can make it easier for customers to find what they want or for businesses to make suggestions about related items.

An AI solution provided by STRV allowed a company to use its available product data to categorize 30,000 types of shoes with 96 percent accuracy and a 20 millisecond per item processing time. The project was completed 500 times faster than it could have been if managed manually. Combining AI and data science decreases the cost while increasing the return on investment.

Because most science deals with natural processes that cannot be rushed or manipulated, it is not wrong to think that good science takes time. Businesses, especially businesses trying to solve problems, typically do not have a lot of time. Addressing problems with data science can seem like a luxury that your business cannot afford.

Data science is different. Data moves at the speed of light and the technology and methods for mining and understanding data, once developed, can be widely applied. STRV approaches data science projects by first developing a Proof of Concept (POC) to validate that the problem can be solved with the data that is available. By committing to get to a POC conclusion quickly, STRV allows for the entire timeline for data science solutions to be greatly reduced.

STRV has undertaken major projects for companies including Songclip, Cinnamon, and AllVoices. Even with projects that involve cutting edge technology and demand a high degree of efficiency and accuracy, the POC phase of the process has rarely taken more than one month.

In the case of Soncglips, it took STRV only four weeks to build the entire solution for mapping clips with lyrics. That solution ultimately empowered the company to increase utilization of its database of clips from 4 percent to 100 percent without adding extra workforce. When data science is done correctly, it can provide solutions on a schedule that works for any business.

Data science is not the science of tomorrow. It is a key tool being used by companies today to gain a competitive edge. There was a time when a mobile app or fancy user interface was enough to differentiate your company. Now those things are the norm. Data science makes companies smarter and better equipped to deliver a five-star customer experience.

While every company has data, successful companies are those who are building their business around that data, applying data science, and using AI as the core driver of competitiveness and success.

There are some obvious examples of companies that are benefitting from data science, such as ecommerce and online content companies. However, when AI is introduced to the equation, virtually any company can benefit from data science.

Regardless of the business that you conduct, if your company needs to make informed decisions, motivate employees, develop and adhere to best practices, explore new business opportunities, and identify target audiences, data science can help your business to succeed.

Published November 6th, 2021

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Debunking The Four Most Common Data Science Myths - Influencive

The trouble started in mid-September, when the Wall Street Journal published an expos titled Facebook Knows Instagram Is Toxic for Teen Girls, Company Documents Show. The article revealed that Facebook had identified disturbing information about the impact of their Instagram service on young users. It cited an internal company presentation, leaked to the paper by an anonymous whistle-blower, that included a slide claiming that thirty-two percent of teen girls said that when they felt bad about their bodies, Instagram made them feel worse. Another slide offered a blunter conclusion: Teens blame Instagram for increases in the rate of anxiety and depression. This reaction was unprompted and consistent across all groups.

These revelations sparked a media firestorm. Instagram Is Even Worse Than We Thought for Kids, announced a Washington Post article published in the days following the Journals scoop. Its Not Just Teenage GirlsInstagram Is Toxic for Everyone, claimed an op-ed in the Boston Globe. Zuckerbergs public comments about his platforms effects on mental health appear to be at odds with Facebooks internal findings, noted the New York Post. In a defiant post published on his Facebook account, Mark Zuckerberg pushed back, stating that the motives of his company were misrepresented. The very fact that Facebook was conducting this research, he wrote, implies that the company cares about the health impact of its products. Zuckerberg also pointed to data, included in the leaked slides, that showed how, in eleven out of the twelve areas of concern that were studied (such as loneliness and eating issues), more teen-age girls said that Instagram helped rather than hurt. In the background, however, the company paused work on a new Instagram Kids service.

These corporate responses werent enough to stem the criticism. In early October, the whistle-blower went public in an interview on 60 Minutes, revealing herself to be Frances Haugen, a data scientist who had worked for Facebook on issues surrounding democracy and misinformation. Two days later, Haugen testified for more than three hours before a Senate subcommittee, arguing that Facebooks focus on growth over safeguards had resulted in more division, more harm, more lies, more threats, and more combat. In a rare moment of bipartisanship, Democrat and Republican members of the subcommittee seemed to agree that these social-media platforms were a problem. Every part of the country has the harms that are inflicted by Facebook and Instagram, the subcommittee chair, Senator Richard Blumenthal of Connecticut, stated in a press conference following Haugens testimony.

This is far from the first time that Facebook has faced scrutiny. What struck me about this particular pile-on, however, was less its tonewhich was near-uniformly negativethan what was missing. The commentary reacting to the Journals scoop was quick to demand punishment and constraints on Facebook. In many cases, the writers seethed with frustration about the lack of such retribution enacted to date. Both Democrats and Republicans have lambasted Facebook for years, amid polls showing the company is deeply unpopular with much of the public, noted a representative article from the Washington Post. Despite that, little has been done to bring the company to heel. Whats largely absent from the discussion, however, is any consideration of what is arguably the most natural response to the leaks about Instagrams potential harm: Should kids be using these services at all?

There was a moment in 2018, in the early stages of the Cambridge Analytica scandal, when the hashtag #DeleteFacebook began to trend. Quitting the service became a rational response to the growing litany of accusations that Facebook faced, such as engineered addiction, privacy violations, and its role in manipulating civic life. But the hashtag soon lost momentum, and the appetite for walking away from social media diminished. Big-swing Zeitgeist articlessuch as a 2017 Atlantic story that asked Have Smartphones Destroyed a Generation?gave way to smaller policy-focussed polemics about arcane regulatory responses and the nuances of content-moderation strategies. This cultural shift has helped Facebook. The reality is that young people use technology. Think about how many school-age kids have phones, Zuckerberg wrote in his post responding to the latest scandal. Rather than ignoring this, technology companies should build experiences that meet their needs while also keeping them safe. Many of the politicians and pundits responding to the Facebook leaks implicitly accept Zuckerbergs premise that these tools are here to stay, and all thats left is to argue about how they operate.

Im not sure, however, that we should be so quick to give up on interrogating the necessity of these technologies in our lives, especially when they impact the well-being of our children. In an attempt to keep this part of the conversation alive, I reached out to four academic expertsselected from both sides of the ongoing debate about the harm caused by these platformsand asked them, with little preamble or instruction, the question missing from so much of the recent coverage of the Facebook revelations: Should teen-agers use social media? I wasnt expecting a consensus response, but I thought it was important, at the very least, to define the boundaries of the current landscape of expert opinion on this critical issue.

I started with the social psychologist Jonathan Haidt, who has emerged in recent years, in both academic and public circles, as one of the more prominent advocates for issues surrounding social media and teen-age mental health. In his response to my blunt question, Haidt drew a nuanced distinction between communication technology and social media. Connecting directly with friends is great, he told me. Texting, Zoom, FaceTime, and Snapchat are not so bad. His real concern were platforms that are specifically engineered to keep the childs eyes glued to the screen for as long as possible in a never-ending stream of social comparison and validation-seeking from strangersplatforms that see the user as the product, not the customer. How did we ever let Instagram and TikTok become a large part of the lives of so many eleven-year-olds? he asked.

I also talked to Adam Alter, a marketing professor at N.Y.U.s Stern School of Business, who was thrown into the social-media debate by the publication of his fortuitously timed 2017 book, Irresistible, which explored the mechanisms of addictive digital products. Theres more than one way to answer this question, and most of those point to no, he answered. Alter said that he has delivered this same prompt to hundreds of parents and that none of them seem happy that their teens use social media. Many of the teens he spoke with have confirmed a similar unease. Alter argued that we shouldnt dismiss these self-reports: If they feel unhappy and can express that unhappiness, even that alone suggests the problem is worth taking seriously. He went on to add that these issues are not necessarily easy to solve. He expressed worry, for example, about the difficulty of trying to move a teen-ager away from social media if most of their peers are using these platforms to organize their social lives.

On the more skeptical side of the debate about the potential harm to teen-agers is Laurence Steinberg, a psychology professor at Temple University and one of the worlds leading experts on adolescence. In the aftermath of Haugens Senate testimony, Steinberg published an Op-Ed in the Times that argued that the research linking services like Instagram to harm is still underdeveloped, and that we should be cautious about relying on intuition. Psychological research has repeatedly shown that we often dont understand ourselves as well as we think we do, he wrote. In answering my question, Steinberg underscored his frustration with claims that he thinks are out ahead of what the data support. People are certain that social media use must be harmful, he told me. But history is full of examples of things that people were absolutely sure of that science proved wrong. After all, people were certain that the world was flat.

Continued here:

The Question Weve Stopped Asking About Teen-Agers and Social Media - The New Yorker

As AI adoption increases across industries, the emphasis has shifted heavily to developing and deploying ethical, responsible AI applications.

Responsible Artificial Intelligence is a positive force. According to Gartner, Responsible AI encompasses several aspects of making the right decisions when adopting AI, aspects that are often addressed independently by organizations. A responsible AI framework focuses on bias detection, privacy, governance, and explainability to help organizations harness the power of AI.

Nevertheless, the practical implications of Responsible AI are unclear. Can this be applied across industries and domains? How can we responsibly deploy AI?

During this complimentary fireside, we will discuss the most critical aspects of responsible AI.

To address these queries and more, Tredence is conducting a Fireside Chat session with dignitaries like Professor Balaraman Ravindran, Head, Robert Bosch Centre for Data Science and AI, Professor at IIT Madras; Soumendra Mohanty, Chief Strategy Officer & Chief Innovation Officer at Tredence Inc.; and Aravind Chandramouli, Head of AI CoE at Tredence Inc. The session would be conducted around the theme of Responsible AI: Decode, Contextualise and Operationalise.

This session is designed to help you learn best practices & techniques for driving Responsible AI in your organization, achieve fairness in AI deployment and gain customer trust.

Prof Ravindran is the head of the Robert Bosch Centre for Data Science and Artificial Intelligence (RBC-DSAI) at IIT Madras and a professor in the Department of Computer Science and Engineering. He is also the co-director of the reconfigurable and intelligent systems engineering (RISE) group at IIT Madras, which has nearly 80 members associated with it currently. He received his PhD from the University of Massachusetts, Amherst. He has nearly two decades of research experience in machine learning and, specifically, reinforcement learning.

Soumendra Mohanty is the Chief Strategy Officer & Chief Innovation Officer at Tredence. He has led key growth portfolios (IIOT, Data, Analytics, AI, Intelligent RPA, Digital Integration, Digital Experience, Platforms), bringing in world-class capabilities, innovative solutions, and transformation-led, outcomes-led value propositions to our clients. Under his leadership, Tredence has established a wide range of digital and data analytics capabilities and an enviable client-centric innovation culture to solve problems at the convergence of physical and digital.

With a career spanning over 25 years, Soumendra has held various leadership roles at Accenture (Global Data Analytics Lead), Mindtree (SVP & Digital Lead), L&T Infotech (EVP & CDAO), leading multi-faceted P&L functions, including M&A advisory for technology growth strategies and startup ecosystems.

Dr Aravind Chandramouli has a PhD in Computer Science from the University of Kansas with a focus on Information Retrieval and Machine Learning. He started his career at Google in 2007 and stopped at Microsoft, GE Research Labs, and Fidelity Investments over a 15-year career. Currently, he heads the AI CoE at Tredence with a focus on innovation. At Tredence, his team focuses on solving complex problems for clients using the right AI techniques. These problems span a wide range of data types like text, images/videos and structured data. He has six patent grants based on solving hard industry problems that had a direct impact on the stakeholders. He has won innovation awards at Microsoft, Fidelity Investments and Tredence. In addition to the patent and innovation awards, he also has over ten publications at top international conferences and journals.

Analytics India Magazine chronicles technological progress in the space of analytics, artificial intelligence, data science & big data by highlighting the innovations, players, and challenges shaping the future of India through promotion and discussion of ideas and thoughts by smart, ardent, action-oriented individuals who want to change the world.

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Do You Want To Deploy Responsible AI In Your Organization? Join This Session To Operationalize Responsible AI - Analytics India Magazine

Organizations in nearly every industry are racing to hire qualified professionals with the skills to transform big data into big insights and better decisionsand these data scientists are in short supply.

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Data Science Master's Degree | 100% Online | University of ...

Quick, what does the following code do?

Its impossible to tell right? If you were trying to modify or debug this code, youd be at a loss unless you could read the authors mind. Even if you were the author, a few days after writing this code you might not remember what it does because of the unhelpful variable names and use of magic numbers.

Working with data science code, I often see examples like above (or worse): code with variable names such as X, y, xs, x1, x2, tp, tn, clf, reg, xi, yi, iiand numerous unnamed constant values. To put it frankly, data scientists (myself included) are terrible at naming variables.

As Ive grown from writing research-oriented data science code for one-off analyses to production-level code (at Cortex Building Intelligence), Ive had to improve my programming by unlearning practices from data science books, coursesand the lab. There are significant differences between deployable machine learning code and how data scientists learn to program, but well start here by focusing on two common and easily fixable problems:

Unhelpful, confusing or vague variable names

Unnamed magic constant numbers

Both these problems contribute to the disconnect between data science research (or Kaggle projects) and production machine learning systems. Yes, you can get away with them in a Jupyter Notebook that runs once, but when you have mission-critical machine learning pipelines running hundreds of times per day with no errors, you have to write readable and understandable code. Fortunately, there are best practices from software engineering we data scientists can adopt, including the ones well cover in this article.

Note: Im focusing on Python since its by far the most widely used language in industry data science. Some Python-specific naming rules (see here for more details) include:

More From Will KoerhsenThe Poisson Process and Poisson Distribution, Explained

There are three basic ideas to keep in mind when naming variables:

The variable name must describe the information represented by the variable. A variable name should tell you concisely in words what the variable stands for.

Your code will be read more times than it is written. Prioritize how easy your code is to read over than how quick it is to write.

Adopt standard conventions for naming so you can make one global decision in a codebase instead of multiple local decisions.

What does this look like in practice? Lets go through some improvements to variable names.

If youve seen these several hundred times, you know they commonly refer to features and targets in a data science context, but that may not be obvious to other developers reading your code. Instead, use names that describe what these variables represent such as house_features and house_prices.

What does the value represent? It could stand for velocity_mph, customers_served, efficiencyorrevenue_total. A name such as value tells you nothing about the purpose of the variable and just creates confusion.

Even if you are only using a variable as a temporary value store, still give it a meaningful name. Perhaps it is a value where you need to convert the units, so in that case, make it explicit:

If youre using abbreviations like these, make sure you establish them ahead of time. Agree with the rest of your team on common abbreviations and write them down. Then, in code review, make sure to enforce these written standards.

Avoid machine learning-specific abbreviations. These values represent true_positives, true_negatives, false_positivesand false_negatives, so make it explicit. Besides being hard to understand, the shorter variable names can be mistyped. Its too easy to use tp when you meant tn, so write out the whole description.

The above are examples of prioritizing ease of reading code instead of how quickly you can write it. Reading, understanding, testing, modifying and debugging poorly written code takes far longer than well-written code. Overall, trying to write code faster by using shorter variable names will actually increase your programs development and debugging time! If you dont believe me, go back to some code you wrote six months ago and try to modify it. If you find yourself having to decipher your own past code, thats an indication you should be concentrating on better naming conventions.

These are often used for plotting, in which case the values represent x_coordinates and y_coordinates. However, Ive seen these names used for many other tasks, so avoid the confusion by using specific names that describe the purpose of the variables such as times and distances or temperatures and energy_in_kwh.

When Accuracy Isn't Enough...Use Precision and Recall to Evaluate Your Classification Model

Most problems with naming variables stem from:

On the first point, while languages like Fortran did limit the length of variable names (to six characters), modern programming languages have no restrictions so dont feel forced to use contrived abbreviations. Dont use overly long variable names either, but if you have to favor one side, aim for readability.

With regards to the second point, when you write an equation or use a model and this is a point schools forget to emphasize remember the letters or inputs represent real-world values!

We write code to solve real-world problems, and we need to understand the problem our model represents.

Lets see an example that makes both mistakes. Say we have a polynomial equation for finding the price of a house from a model. You may be tempted to write the mathematical formula directly in code:

This is code that looks like it was written by a machine for a machine. While a computer will ultimately run your code, itll be read by humans, so write code intended for humans!

To do this, we need to think not about the formula itself (the how)and consider the real-world objects being modeled (the what). Lets write out the complete equation. This is a good test to see if you understand the model):

If you are having trouble naming your variables, it means you dont know the model or your code well enough. We write code to solve real-world problems, and we need to understand the problem our model represents.

While a computer will ultimately run your code, itllbe read by humans, so write code intended for humans!

Descriptive variable names let you work at a higher level of abstraction than a formula, helping you focus on the problem domain.

One of the important points to remember when naming variables is: consistency counts. Staying consistent with variable names means you spend less time worrying about naming and more time solving the problem. This point is relevant when you add aggregations to variable names.

So youve got the basic idea of using descriptive names, changing xs to distances, e to efficiency and v to velocity. Now, what happens when you take the average of velocity? Should this be average_velocity, velocity_mean, or velocity_average? Following these two rules will resolve this situation:

Decide on common abbreviations: avg for average, max for maximum, std for standard deviation and so on. Make sure all team members agree and write these down. (An alternative is to avoid abbreviating aggregations.)

Put the abbreviation at the end of the name. This puts the most relevant information, the entity described by the variable, at the beginning.

Following these rules, your set of aggregated variables might be velocity_avg, distance_avg, velocity_min, and distance_max. Rule two is a matter of personal choice, and if you disagree, thats fine. Just make sure you consistently apply the rule you choose.

A tricky point comes up when you have a variable representing the number of an item. You might be tempted to use building_num, but does that refer to the total number of buildings, or the specific index of a particular building?

Staying consistent with variable names means you spend less time worrying about naming and more time solving the problem.

To avoid ambiguity, use building_count to refer to the total number of buildings and building_index to refer to a specific building. You can adapt this to other problems such as item_count and item_index. If you dont like count, then item_total is also a better choice than num. This approach resolves ambiguity and maintains the consistency of placing aggregations at the end of names.

For some unfortunate reason, typical loop variables have become i, j, and k. This may be the cause of more errors and frustration than any other practice in data science. Combine uninformative variable names with nested loops (Ive seen loops nested include the use of ii, jj, and even iii) and you have the perfect recipe for unreadable, error-prone code. This may be controversial, but I never use i or any other single letter for loop variables, opting instead for describing what Im iterating over such as

or

This is especially useful when you have nested loops so you dont have to remember if i stands for row or column or if that was j or k. You want to spend your mental resources figuring out how to create the best model, not trying to figure out the specific order of array indexes.

(In Python, if you arent using a loop variable, then use _ as a placeholder. This way, you wont get confused about whether or not the variable is used for indexing.)

All of these rules stick to the principle of prioritizing read-time understandability instead of write-time convenience. Coding is primarily a method for communicating with other programmers, so give your team members some help in making sense of your computer programs.

A magic number is a constant value without a variable name. I see these used for tasks like converting units, changing time intervals or adding an offset:

(These variable names are all bad, by the way!)

Magic numbers are a large source of errors and confusion because:

Only one person, the author, knows what they represent.

Changing the value requires looking up all the locations where it's used and manually typing in the new value.

Instead of using magic numbers in this situation, we can define a function for conversions that accepts the unconverted value and the conversion rate as parameters:

If we use the conversion rate throughout a program in many functions, we could define a named constant in a single location:

(Remember, before we start the project, we should establish with our team that usd = US dollars and aud = Australian dollars. Standards matter!)

Heres another example:

Using a NAMED_CONSTANT defined in a single place makes changing the value easier and more consistent. If the conversion rate changes, you dont need to hunt through your entire codebase to change all the occurrences, because youve defined it in only one location. It also tells anyone reading your code exactly what the constant represents. A function parameter is also an acceptable solution if the name describes what the parameter represents.

As a real-world example of the perils of magic numbers, in college, I worked on a research project with building energy data that initially came in 15-minute intervals. No one gave much thought to the possibility this could change, and we wrote hundreds of functions with the magic number 15 (or 96 for the number of daily observations). This worked fine until we started getting data in five and one-minute intervals. We spent weeks changing all our functions to accept a parameter for the interval, but even so, we were still fighting errors caused by the use of magic numbers for months.

More From Our Data Science ExpertsA Beginner's Guide to Evaluating Classification Models in Python

Real-world data has a habit of changing on you. Conversion rates between currencies fluctuate every minute and hard-coding in specific values means youll have to spend significant time re-writing your code and fixing errors. There is no place for magic in programming, even in data science.

The benefits of adopting standards are that they let you make a single global decision instead of many local ones. Instead of choosing where to put the aggregation every time you name a variable, make one decision at the start of the project, and apply it consistently throughout. The objective is to spend less time on concerns only peripherally related to data science: naming, formatting, style and more time solving important problems (like using machine learning to address climate change).

If you are used to working by yourself, it might be hard to see the benefits of adopting standards. However, even when working alone, you can practice defining your own conventions and using them consistently. Youll still get the benefits of fewer small decisions and its good practice for when you inevitably have to develop on a team. Anytime you have more than one programmer on a project, standards become a must!

Keep Clarifying Your Code5 Ways to Write More Pythonic Code

You might disagree with some of the choices Ive made in this article, and thats fine! Its more important to adopt a consistent set of standards than the exact choice of how many spaces to use or the maximum length of a variable name. The key point is to stop spending so much time on accidental difficulties and instead concentrate on the essential difficulties. (Fred Brooks, author of the software engineering classic The Mythical Man-Month, has an excellent essay on how weve gone from addressing accidental problems in software engineering to concentrating on essential problems).

Now let's go back to the initial code we started with and fix it up.

Well use descriptive variable names and named constants.

Now we can see that this code is normalizing the pixel values in an array and adding a constant offset to create a new array (ignore the inefficiency of the implementation!). When we give this code to our colleagues, they will be able to understand and modify it. Moreover, when we come back to the code to test it and fix our errors, well know precisely what we were doing.

Clarifying your variable names may seem like a dry activity, but if you spend time reading about software engineering, you realize what differentiates the best programmers is the repeated practice of mundane techniques such as using good variable names, keeping routines short, testing every line of code, refactoring, etc. These are the techniques you need to take your code from research or exploration to production-ready and, once there, youll see how exciting it is for your data science models to influence real-life decisions.

This article was originally published on Towards Data Science.

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Variable Names: Why They're a Mess and How to Clean Them Up - Built In