Discussions about learning to code and working as a programmer often focus on the perks and benefits associated with the field—things like flexible job schedules, high pay (even for entry-level positions), and opportunities to work remotely. But what about the job itself? From the outside looking in, coding can seem like endless hours of typing gibberish onto a computer screen—a task that, quite frankly, sounds pretty boring. Do coders put up with fundamentally boring work just to get those perks and benefits? Or is the work of coding significantly different than what a newcomer might think? To get a solid answer, we spoke with Cary Huang. The short answer to the question “is coding boring?” is—quite simply—”no.” Of course personal preferences can vary, but coding is so not boring for so many people that you’ll even find coders jumping to the profession from much flashier sounding backgrounds.
In this episode, guest Cary Huang sits down with TDS to discuss what he does to make coding an exciting thing to watch. We tried to round up some essential tips to help you have fun while programming in this podcast.

Cary Huang
https://www.linkedin.com/in/cary-huang-119637139/

The Data Standard
https://datastandard.io/
https://www.linkedin.com/company/the-data-standard/
https://www.youtube.com/channel/UCTuolowXD05RY9DkIWqRT6Q

Eitan Anzenberg, Chief Data Scientist at Bill.com, sits down with TDS to discuss his work life, mentorship, and kindness within the valley community.

Eitan Anzenberg
https://www.linkedin.com/in/eitan-anzenberg-5639b444/

The Data Standard
https://datastandard.io/
https://www.linkedin.com/company/the-data-standard/
https://www.youtube.com/channel/UCTuolowXD05RY9DkIWqRT6Q

Gal Klein (CTO) is the co-founder of Audioburst. We discuss analyzing, segmenting, packaging and making talk audio content discoverable in real-time. Audioburst is doing this daily on millions of minutes of radio and podcast content and the same technology could be applied to social audio.

Gal Klein
https://www.linkedin.com/in/gal-klein-026873a/

The Data Standard
https://datastandard.io/
https://www.linkedin.com/company/the-data-standard/
https://www.youtube.com/channel/UCTuolowXD05RY9DkIWqRT6Q

In this episode, guest Matt Thompson sits down with TDS to discuss his pandemic work-life experience.

Matt Thompson
https://www.linkedin.com/in/matt-thompson-98355651/

The Data Standard
https://datastandard.io/
https://www.linkedin.com/company/the-data-standard/
https://www.youtube.com/channel/UCTuolowXD05RY9DkIWqRT6Q

As companies collect more and more data, the ultimate goal is to translate this data into insights that can help them optimize business performance. For example, they may wonder which of their customer segments is most profitable? How they can reduce their customer acquisition costs? Which features of their flagship product are underutilized or need to be enhanced? Which of their best employees are potential flight risks? In most cases, answering one of these questions will lead to . . . more questions.
When the questions you can ask the data are unlimited but the time and resources to find the answers aren’t, you need a strategy for how you query the data for meaningful insights. We often like to say, “There’s no such thing as a stupid question.” However, when it comes to analytics, some questions can be poorly phrased, ill-conceived or misguided. They can lead to costly, time-consuming expeditions into the data that don’t yield any actionable insights.

The French philosopher Voltaire said, “Judge a man by his questions rather than his answers.” This same standard can be applied to organizations. Companies that struggle to get meaningful insights from their data are often not asking the right questions. The better the quality of your questions, the more valuable your insights will be. Preparation is a key success factor to querying data effectively and discovering meaningful insights.

Alexander Finn
https://www.linkedin.com/in/alexanderfinn/

The Data Standard
https://datastandard.io/
https://www.linkedin.com/company/the-data-standard/
https://www.youtube.com/channel/UCTuolowXD05RY9DkIWqRT6Q

Today’s TDS episode is all about data and data models, and why they matter. Deciding how to model your data ahead of time can pay off down the road. It helps ensure that the people who are using the data get what they need from it, and can reduce technical debt. Data modeling is a way of mapping out and visualizing all the different places that a software or application stores information, and how these sources of data will fit together and flow into one another. This is a hugely important stage in the design process for any business-critical IT system. When developers are figuring out how a new system will work, they establish what the most pressing needs of a business are, what kind of data they’ll need to access in order to meet those needs, and how the data will be used.From there, they can start to create a diagram (or model) of how each pocket of data will flow into each other, and how they’ll interact.

Sean Robinson
https://www.linkedin.com/in/seankrobinson/

The Data Standard
https://datastandard.io/
https://www.linkedin.com/company/the-data-standard/
https://www.youtube.com/channel/UCTuolowXD05RY9DkIWqRT6Q

Whether you are organizing a training session for your colleagues or planning to construct a huge engineering plan, project management will help you achieve what you need to do. Project management is the way we apply knowledge and processes to work in order to complete that work in the smoothest way possible. Ultimately, we are using tools, methods, skills, techniques, and the experience of the team to achieve the objectives set for the work. Projects have a clear goal, which means we can plan how to get there and work through a structured plan to ensure that the goals are achieved. Any complex project that a company undertakes has a better chance of achieving its goal, vision, and the desired results if the team executing the project employs both the science and art of project management. The science you can learn and apply because there are many courses, books, tools, techniques, and software packages out there that help you understand how to manage your project from a methodological perspective. Learning the art of project management on the other hand is a different story because it takes experience to know what you need to do when you need to do it, and how much of it to apply to a given situation.


Christine Abanilla
https://www.linkedin.com/in/christine-abanilla-ms-pmp-csm-acp-05a0a251/

The Data Standard
https://datastandard.io/
https://www.linkedin.com/company/the-data-standard/
https://www.youtube.com/channel/UCTuolowXD05RY9DkIWqRT6Q

In the insurance industry, big data is the name of the game. It provides valuable insights into all facets of company operations and performance – from consumer behavior to underwriting practices to the ROI of marketing campaigns. Companies that want to leverage that information into actionable insights turn to big data analytics. Big data technology has created an endless stream of opportunities, which have resulted in an increase in its usage – especially in the insurance and insurtech industry. Overall, big data in the insurance industry has helped businesses make processes more efficient, save on costs, predict behaviors to develop better products, and increase productivity. And big data technology isn’t going anywhere anytime soon. Join TDS and Kevin Mahoney as he discusses how analytics helps the insurance industry.

Kevin Mahoney
https://www.linkedin.com/in/kevin-mahoney-37342757/

The Data Standard
https://datastandard.io/
https://www.linkedin.com/company/the-data-standard/
https://www.youtube.com/channel/UCTuolowXD05RY9DkIWqRT6Q

Data scientists continue to be in high demand, with companies in virtually every industry looking to get the most value from their burgeoning information resources. As organizations begin to fully capitalize on the use of their internal data assets and examine the integration of hundreds of third-party data sources, the role of the data scientist will continue to expand in relevance. This role is important, but the rising stars of the business are those savvy data scientists that have the ability to not only manipulate vast amounts of data with sophisticated statistical and visualization techniques, but have a solid acumen from which they can derive forward-looking insights. So what does it take to be data science whiz?

Pranav Sharkarn
https://www.linkedin.com/in/pranav-shankar/

The Data Standard
https://datastandard.io/
https://www.linkedin.com/company/the-data-standard/
https://www.youtube.com/channel/UCTuolowXD05RY9DkIWqRT6Q

Data monetization is the process of using data to increase revenue. Direct data monetization involves selling direct access to your data to third parties. You can sell it in raw form, or you can sell it in a form that’s already transformed into analysis and insights. Typical examples may be contact lists of potential business prospects or findings that impact on buyers’ industries and businesses. Indirect data monetization is where things get interesting. Firstly, there’s data-based optimization. This involves analyzing your data to reveal insights that can improve your organization’s business performance. Data can identify how to reach customers and understand customer behavior so you can drive your sales. Data can also highlight where and how to save costs, avoid risk and streamline operations. Secondly, there are data-driven business models, where you use your data to discover new business opportunities and customers. You can embed analytics into your products or services, providing advantages for you and your customers. Customers benefit from direct access to usage analytics and other data generated by each product they already use. You benefit from offering this as a value add-on or as a new tier of service, encouraging customer loyalty. In turn, you get a better insight into how your customers are using your products. Without the right data monetization strategies in place, you risk missing critical insights that could improve your business. With the necessary strategies in place, you are well equipped to sharpen your competitive edge.

Patrick Callinan
https://www.linkedin.com/in/patrick-callinan-051b46/

The Data Standard
https://datastandard.io/
https://www.linkedin.com/company/the-data-standard/
https://www.youtube.com/channel/UCTuolowXD05RY9DkIWqRT6Q