The Data Standard

Product Data Science means building data products, tools and measurement strategies that impact the consumer using a rigorous statistical methodology, expertise, and experience. In this episode, guest  Katrina Poole sits down with TDS to discuss Product Data Science.Katrina Poolehttps://www.linkedin.com/in/katrina-glaeser-poole-ab57a714/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.iohttps://www.linkedin.com/company/the-data-standardhttps://www.youtube.com/channel/UCTuolowXD05RY9DkIWqRT6Q

Human resource departments are changing. What was an administrative function, running payroll and managing recruitment and training, is now a key player in corporate strategies and a major influence in employees’ everyday work experience. How has this come about? The answer lies in people analytics.

People analytics (PA) applies the power of artificial intelligence (AI) to the large data sets about people held by human resources in order to solve business problems. If a company has a problem retaining good staff, PA will tell them why and what to do about it. If sales in some shops are not as good as others, PA will identify the root of the problem in staff engagement so that the company can change managerial behavior.

The range of data available to PA includes not just HR staff records, but an increasingly wide range of data types that could have movement and health data from wearable devices. Using machine learning, HR departments can identify new trends and influence people's management decisions across the company.

The pitch of PA is that it replaces the vagaries of human intuition and professional experience with hard facts to create evidence-driven human resource management which brings massive efficiencies and benefits for the business.

The effects of the coronavirus pandemic on employment have accelerated the practice and influence of PA. The need for safe distancing at work and expansion of home working has increased the reach of HR and the amount of data available.

In this episode, guest Zach Frank sits down with TDS to discuss people Analytics and AI

Zach Frankhttps://www.linkedin.com/in/zachlfrank/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.iohttps://www.linkedin.com/company/the-data-standardhttps://www.youtube.com/channel/UCTuolowXD05RY9DkIWqRT6Q

What do space, art, and virtual reality have in common? In two words: Sasha Samochina. In this episode, guest Sasha Samochina sits down with TDS to discuss Data visualization in the immersive space. You’ll hear about Sasha’s last-minute choice to go to art school and the lightbulb moment that led her to merge her love of art and science.Sasha Samochinahttps://www.linkedin.com/in/sashasamochina/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.iohttps://www.linkedin.com/company/the-data-standardhttps://www.youtube.com/channel/UCTuolowXD05RY9DkIWqRT6Q

Stephen Drollinger, Director of Data Engineering at ICX Media, sits down with TDS to discuss his journey through tech, military life, and this company ICX Media.

Stephen Drollinger

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

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 Nikhil Goyal sits down with TDS to discuss which specializations are key to building an effective data team. Prior to LeafLink, Nikhil led the data science function for Gartner’s Marketing practice, gaining deep expertise in building scaled data products for large consumer brands. He has extensive experience in generating insights from e-commerce, search and transactional data leveraging experimentation, machine learning, and implementing business intelligence solutions. Nikhil has also held positions managing data science teams at and consulting F500 clients on marketing and advertising. Currently, Nikhil is responsible for continuing to evolve the data platform that powers the LeafLink marketplace.

Nikhil Goyal

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

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

Management can make or break a workplace, as it is the most effective and organic way to raise productivity. Employees are usually more likely to have positive results under the right administration. There are many things that go hand-in-hand with good management. Great managers are a way of ensuring that the work that takes place somewhere will be a smooth ride to success. We can all attest to great bosses helping us thrive in our positions, but what really is it about them that makes them great? How are these people making sure that their workers stay happy while also managing to capitalize on it? Behind great management stand strategic thinkers. These are people that make strategic moves in a very humane way — they see their employees as actual people and learn to locate where their uniqueness and strengths lie. In this episode, guest Paul Carter sits down with TDS to discuss his philosophies of good management.

Paul Carter

https://www.linkedin.com/in/paul-w-carter/

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

Alessandro Pregnolato, VP of Data at Preply sits down with TDS to discuss business intelligence, big data, and his experience with running teams.

Alessandro Pregnolato

https://www.linkedin.com/in/alessandro-pregnolato-5472501/

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

From chatbots, personalized recommendations on social media, traffic predictions, and virtual personal assistants including Siri and Alexa, advances in machine learning are becoming an integral tool that helps individuals navigate the modern world. Increased adoption of such technology across the world has driven massive growth in the volume of data, requiring businesses to harness the power of machine learning to make decisions, learn about and predict customer behavior to drive strategic advantage.

Combining the fields of engineering, statistics, mathematics, and computing, machine learning is one of the leading data science methodologies revolutionizing business. This course will cover a wide range of machine learning methods, both model-based and algorithmic.

In this episode, guest Pierce Freeman sits down with TDS to discuss ways of bringing ML data into practice.

Pierce Freeman

https://www.linkedin.com/in/pierce-freeman

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

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 

Product teams are growing and with them a plethora of product suffixes: there are now product managers, owners, designers, analysts, strategists, and others all sitting on product teams. Product managers now also have more adjectives attached to their titles, whether it’s based on their seniority or their field, and as the discipline evolves, so will job descriptions. But there is one conspicuous function on many product teams today that doesn’t always have “product” in the title: data scientist. Data scientists facilitate data-based decision-making, experimentation, and innovation, key drivers that can put the product team ahead of the market. Being on the product team ensures they have alignment with- and a sharp focus on the product and business objectives.

In this episode, guest Martin Schmitz sits down with TDS to attempt to answer the question of does every product needs a Data Scientist?

Martin Schmidtz

https://www.linkedin.com/in/dr-martin-schmitz/ 

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