The Data Standard

When we think of computer science, namely Artificial Intelligence (AI), it’s easy to forget just how revolutionary it is.

Today, Artificial Intelligence can not only speak, write, listen and understand human language, it can also extract meaning from natural language to make informed decisions all thanks to Natural Language Processing. NLP is arguably one of the most exciting fields in AI and has already given rise to technologies like voice assistants, chatbots, translators, and many other tools that we use daily.
Natural Language Processing is a field of AI that enables computers to analyze, understand and arrange human language. Natural language processing software continues to advance to better interpret the nuance, context, and obscurities present within human communication.

NLP has already been adopted by many businesses that are driving results by drawing insights from large quantities of data while automating repetitive and monotonous tasks. NLP will only continue to benefit businesses as our reliance on, and understanding of, data shifts. With the amount of data at our disposal, it will be more and more pertinent to understand it, observe it, and in certain situations, censor it. Natural language processing will become more widespread in the years to come, as businesses reap the benefits it has to offer; from improving operations and reducing costs, to heightened customer satisfaction and more informed decision making. This episode features an interview with Kevin Wu, Chief Data Scientist at Consumer Edge. His company, Consumer Edge, enables its clients to obtain insights about the consumer from consumer transaction data.

Kevin Wu
https://www.linkedin.com/in/kevinwupublic/

The Data Standard is a community of data scientists, architects, engineers, and enthusiasts. In addition to regular podcasts, we host monthly events, publish through leadership pieces, and offer a stimulating ecosystem for networking and collaboration.

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

The allure of the cloud, with its promises of cost benefits, greater stability, and more recently as the lynchpin of digital transformation, is well understood. But if such promises are to be realized, tech leaders need to think beyond just lifting and shifting on-prem applications into the cloud. Companies are reprioritizing their cloud initiatives, many shifting toward multi-cloud architecture and away from more traditional infrastructure. Our guest on this episode, Amit Arora, Data Scientist at Hughes Network Systems, joins TDS to dive into this “massive paradigm shift” from a static to a dynamic world, where companies are expanding beyond the investment in their traditional data center into many other areas. We discuss the advantages of this shift as well as how to organize a successful transition from a people, process and technology standpoint. We also take a deep dive into data science and data governance.

Amit Arora

https://www.linkedin.com/in/amit-arora-539120a/ 

The Data Standard is a community of data scientists, architects, engineers, and enthusiasts. In addition to regular podcasts, we host monthly events, publish through leadership pieces, and offer a stimulating ecosystem for networking and collaboration.

https://datastandard.io 

https://www.linkedin.com/company/the-data-standard 

https://www.youtube.com/channel/UCTuolowXD05RY9DkIWqRT6Q 

Organizations across the globe are looking to organize, process, and unlock the value of the torrential amounts of data they generate and transform them into actionable and high-value business insights. Hence, hiring data scientists – highly skilled professional data science experts have become supercritical. Today, there is virtually no business function that cannot benefit from them. In fact, the Harvard Business Review has labeled data science as the “sexiest” career of the 21st century. However, no career is without its own challenges, and being a data scientist, despite its “sexiness” is no exception.

Despite all the challenges, data scientists are the most in-demand professionals in the market. With the data world changing at a rapid pace, being a successful data scientist is not just about having the right technical skills but also about having a clear understanding of the business requirements, collaborating with different stakeholders, and convincing business executives to act upon the analysis provided.

If you’re a student or beginner facing challenges in the data science world and would like to learn more about overcoming them, then this podcast is for you as our guest Chester Isamay sits down with us to discuss challenges in the data science world. 

Chester Ismay

https://www.linkedin.com/in/chesterismay/ 

The Data Standard is a community of data scientists, architects, engineers, and enthusiasts. In addition to regular podcasts, we host monthly events, publish through leadership pieces, and offer a stimulating ecosystem for networking and collaboration.

https://datastandard.io 

https://www.linkedin.com/company/the-data-standard 

https://www.youtube.com/channel/UCTuolowXD05RY9DkIWqRT6Q 

The data in your data warehouse are only valuable if they are actually used. To make your data usable, you need to consider how the data are presented to end-users and how quickly users can answer their questions. In today’s world, businesses are continuously evolving. With changing times a wave of transformation be it digital, be it predictive forecasting, or building the agile ways of operations that will sustain in crisis, having your data structured in the right format and accurate with respect to changing needs of your business is of prime importance. Data modeling plays a crucial role in enabling business users to make more informed, and data-driven decisions are key to success. Data Modeling is a way to structure your data to be stored in a database, and the approaches define agility of the database, query performance and value add to the information being extracted. In this episode, guest Dr. Maria Wang sits down with TDS to discuss best modeling practices and also cover some guidelines on how to build better data models that are more maintainable, more useful, and more performant.

Dr. Maria Wang

https://www.linkedin.com/in/zmariawang/ 

The Data Standard is a community of data scientists, architects, engineers, and enthusiasts. In addition to regular podcasts, we host monthly events, publish through leadership pieces, and offer a stimulating ecosystem for networking and collaboration.

https://datastandard.io 

https://www.linkedin.com/company/the-data-standard 

https://www.youtube.com/channel/UCTuolowXD05RY9DkIWqRT6Q 

The data in your data warehouse are only valuable if they are actually used. To make your data usable, you need to consider how the data are presented to end-users and how quickly users can answer their questions. In today’s world, businesses are continuously evolving. With changing times a wave of transformation be it digital, be it predictive forecasting, or building the agile ways of operations that will sustain in crisis, having your data structured in the right format and accurate with respect to changing needs of your business is of prime importance. Data modeling plays a crucial role in enabling business users to make more informed, and data-driven decisions are key to success. Data Modeling is a way to structure your data to be stored in a database, and the approaches define agility of the database, query performance and value add to the information being extracted. In this episode, guest Dr. Maria Wang sits down with TDS to discuss best modeling practices and also cover some guidelines on how to build better data models that are more maintainable, more useful, and more performant.

Dr. Maria Wang
https://www.linkedin.com/in/zmariawang/

The Data Standard is a community of data scientists, architects, engineers, and enthusiasts. In addition to regular podcasts, we host monthly events, publish through leadership pieces, and offer a stimulating ecosystem for networking and collaboration.

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

In this episode, guest Elliot Winard sits down with TDS to discuss concepts such as data and leadership.

Elliot Winard

https://www.linkedin.com/in/elliotwinard/ 

The Data Standard is a community of data scientists, architects, engineers, and enthusiasts. In addition to regular podcasts, we host monthly events, publish through leadership pieces, and offer a stimulating ecosystem for networking and collaboration.

https://datastandard.io 

https://www.linkedin.com/company/the-data-standard 

https://www.youtube.com/channel/UCTuolowXD05RY9DkIWqRT6Q 

Productionizing your Machine Learning model is an important part of any machine learning project. Deploying machine learning models from training to production requires companies to deal with the complexity of moving workloads through different pipelines and re-writing code from scratch. In this episode, guests Scott Mitchell and Charlie Moad sit down with TDS to talk about productionizing machine learning models.

Scott Mitchell

https://www.linkedin.com/in/smitchelus/ 

Charlie Moad

https://www.linkedin.com/in/charles-moad/ 

The Data Standard is a community of data scientists, architects, engineers, and enthusiasts. In addition to regular podcasts, we host monthly events, publish through leadership pieces, and offer a stimulating ecosystem for networking and collaboration.

https://datastandard.io 

https://www.linkedin.com/company/the-data-standard 

https://www.youtube.com/channel/UCTuolowXD05RY9DkIWqRT6Q 

In this episode, guest Shalini Agarwal sits down with TDS to discuss ways of automating repetitive tasks, how we need more data scientists to make our applications smarter; however, we can make them more efficient and accomplish more with data scientists by having automated workflows and tools. These tools can be used by non-data scientists to leverage the established workflows and remove the repetitive tasks from the mountain of tasks expected from a data scientist. Shalini Agarwal is the Director of Engineering at LinkedIn, responsible for building core experience of Sales Solutions enterprise product. Before this, she was responsible for delivering scalable Search and Data Applications while managing a global team at LinkedIn. Shalini spent nearly a decade at eBay where she shaped buyer experience and transformed her career from individual contributor to management. She is leading LinkedIn’s REACH apprenticeship program since its inception, a program to hire talent coming from non-linear pathways to LinkedIn’s engineering team. She has been part of Women in Tech programs for over 10 years. Her passion lies in building great software, business applications, and empowering women.

Shalini Agarwal

https://www.linkedin.com/in/shalini-agarwal-5b735b2/ 

The Data Standard is a community of data scientists, architects, engineers, and enthusiasts. In addition to regular podcasts, we host monthly events, publish through leadership pieces, and offer a stimulating ecosystem for networking and collaboration.

https://datastandard.io 

https://www.linkedin.com/company/the-data-standard 

https://www.youtube.com/channel/UCTuolowXD05RY9DkIWqRT6Q 

In this episode, guest Rachael Weiss Riley sits down with TDS to discuss her work on the data for good space, and journey in providing support for mission-driven organizations. Rachael Weiss Riley leads Data Clinic’s strategic vision and oversees the translation of data science and technology support into lasting impact for our partner organizations. Prior to Two Sigma, Rachael spent nearly a decade engaging in geospatial research to measure the influence of the built and social environment on public health outcomes. She holds a DrPH in Epidemiology from the CUNY School of Public Health, an MPH from Hunter College, a BS from Brown University, and was recently appointed to the inaugural NYC Open Data Advisory Council.

Rachael Weiss Riley

https://www.linkedin.com/in/rachael-weiss-riley-891a134/ 

The Data Standard is a community of data scientists, architects, engineers, and enthusiasts. In addition to regular podcasts, we host monthly events, publish through leadership pieces, and offer a stimulating ecosystem for networking and collaboration.

https://datastandard.io 

https://www.linkedin.com/company/the-data-standard 

https://www.youtube.com/channel/UCTuolowXD05RY9DkIWqRT6Q 

Our guest, Stas Zhukovskiy, explains that the Solutions Engineering team is there to help customers choose appropriate products for things like security, analytics, data management, and more. The products are laid out in guides and blueprints so the client can easily understand why products are chosen and how to use them. In this episode, guest Stas Zhukovskiy sits down with TDS to discuss his career in solutions engineering and the ins and outs of the industry.

Stas Zhukovskiy

https://www.linkedin.com/in/stzhuk/ 

The Data Standard is a community of data scientists, architects, engineers, and enthusiasts. In addition to regular podcasts, we host monthly events, publish through leadership pieces, and offer a stimulating ecosystem for networking and collaboration.

https://datastandard.io 

https://www.linkedin.com/company/the-data-standard 

https://www.youtube.com/channel/UCTuolowXD05RY9DkIWqRT6Q