The Data Science Salon | Kopal Dhariwal

The Data Science Salon is a unique vertical focused conference which grew into the most diverse community of senior data science, machine learning and other technical specialists in the space.


SPEAKERS

Anna Anisin 

Founder and CEO at Formulated.by

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


Kenny Wei-Chih Chen 

Data Scientist at TikTok

https://www.linkedin.com/in/kenny-wei-chih-chen-8067aa6b/


Katerina Iliakopoulou 

Lead Software Engineer at The New York Times

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


David Talby

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


Mukul Krishna 

Global Practice Head - Digital Media at Frost & Sullivan and Advisory Board Member at HUBU Home

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


Matt Smith 

Experienced Digital Technologist

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


Jay Kachhadia  

Data Scientist @ ViacomCBS | Data Science Blogger

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


Mario A. Vinasco 

Director BI and Analytics at Credit Sesame

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


Harris (Huancen) Liu 

Data Science & ML at Disney Streaming Services

https://www.linkedin.com/in/harris-liu/


Quotes:

“The hardest part of data science is getting good, clean data. Cleaning data is often 80% of the work.” - Dj Patil, Us Chief Data Scientist


“Those who can imagine anything, can create the impossible.” Alan Turing


“Data Science is like teenage sex: Everyone talks about it, Everyone thinks everyone else is doing it, so everyone claims they are doing it” – KD Nuggets


SUMMARY


Driving Innovation

Developing a community allows you to position your brand in front of the right people at the right time, creating a memorable experience that makes your value clear. It can take a lot of effort to build a new community, but not necessarily a lot of monetary investment. For new communities we require few of these things : 

Knowledge: Be specific. Communities organize best around a single issue. Do the research. What communities already exist? Their strengths and weaknesses. How can you improve on existing communities or offer something they don’t already have? Who are the people best for this community? It’s also very important to use the right messaging/language to reach the target audience.

Proximity : The best connections and relationships happen in person. Physically bring your community together from time to time. Study your audience to know what type of gathering will bring the biggest value to your stakeholders? Help connect, talk and know people in the community. Listen for insights and POV. Keep the talk relevant, authentic and sincere.

Time : Communities don’t form overnight, it takes significant resources to start and maintain new communities. Stay in constant touch with your community may it be in person or digitally. Encourage them for conversations & engagement.

Science: No doubt that data science has changed the way companies make decisions at each level. Market research to identify hot markets and appropriate topics, key sponsors, influencers and speakers. Collect data at each point, twitter, email, facebook etc. Look for trends. Use insights to attract more resources. 

Data science has been a hot career field for young individuals. Everyone wants to get into data science. Earlier the supply of data scientists was less, and demand was more. But now the situation has turned around. The term ‘Data Science’ is evolving broader and broader. Due to increase in demand companies have started switching data scientist titles with product analyst, business intelligence analyst, business analyst etc because people were leaving their jobs to get the data scientist titles at companies which were giving them for doing the same job. There is a lot more to it now instead of just knowing to apply machine learning to datasets which almost anyone today can do. There are a few other crucial things which can be helpful for a data scientist role like Distributed Data Processing/Machine Learning: Getting hold of hands-on experience with technologies and Production Machine Learning.

Takeaway

There is flexibility with the application of funds to solve the problems of growth. Many companies focus on creating bespoke solutions from the rising up. This strategy doesn’t necessarily translate into grand returns. Creating a community generates more revenue for entrepreneurship and is a more sustainable lead-generation tool than other traditional methods. It just takes knowledge, time, proximity and science. Also, knowing only Machine Learning or Statistics is not gonna get you into data science to do ML, you should do networking which is very important. Business applications and domain knowledge tends to come with experience and can’t be learned beforehand other than doing internships in relevant industries.


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