DataGig, Austin, TX (2026)

10/31/2019

OpenTeams brings together organizations using open source software with creators and maintainers of the software to facilitate and grow funding opportunities

03/04/2019

Beginning data scientists: want a project that will blow prospective employers away? Forget that tired old "cats vs. dogs" image classifier. Build yourself a CNN that can solve this age-old problem and watch companies flock to you like groupies to Mick Jagger... :)
EDIT: for those who keep asking, no, I do not have this dataset. I'm pretty sure this is a joke and no such data set exists. Maybe some enterprising company will hire an intern to take this on...

02/28/2019

Artificial Intelligence (AI) is arguably the most revolutionary technology in several decades that would completely turn the world upside down and then shape it along with new contours.
Here’s a list of top AI trend to watch out for in 2019.
AI chipsets: This year, leading chip manufacturers like Intel, NVidia, AMD, ARM, Qualcomm will make chips that will rapidly enhance the speed of ex*****on of AI-based apps.
Confluence of AI and IoT: AI and IoT will increasingly converge at edge computing.
Rise of Automated Machine Learning: Machine Learning would undergo a radical change with the arrival of AutoML( automated Machine Learning) algorithms.
Cybersecurity and AI: This will transform the way organizations look at cybersecurity.
AI skills: There is a big gap that companies are finding it hard to overcome: the AI skills gap. ( help solve this problem)
Automation of DevOps through AI: Deploying AI in IT operations will help them perform tasks in shorter time and get at the root of the problem quickly.
Neural Network Interoperability: Interoperability in neural networks is an impediment in the path of AI adoption.
Open source AI: Multiple companies would start open sourcing their AI stacks for building a wider support network of AI communities.

01/11/2019

You're a masters student in Computer Science.
You go to Stanford, one of the top universities in the world.
You're passionate about working in AI. You take up courses such as Machine Learning, Introduction to AI etc at the university.
You apply to large tech companies for an internship in Machine Learning or Computer Vision.
Companies offer you internships, but not in ML or CV.
You keep applying. No one wants to give you an opportunity to work in ML/CV.
It's disappointing, but you keep trying.
Finally one company gives you an internship to work on vision for robotics. You're more than happy to join!
After the internship, you start a PhD and you change the way the world does CV.
You're Ian Goodfellow, the inventor of Generative Adversarial Networks or GANs as they're popularly known.
From not being offered an internship in CV to changing the way CV is done, Ian Goodfellow is an inspiration!
The next time you think about quitting, think again.

12/28/2018

Happy holidays...

11/30/2018

Today we had the pleasure of speaking about a new program we’re working on.
We’re super excited and overwhelmed with the amount of feedback and interest we got from the Applied AI conference the past two days.
Our founder came to the conference as a speaker, was featured on a podcast for Women in AI and finished as a moderator.
Thank you RE.WORK for the amazing opportunity!
More information about our apprenticeship program coming soon!
@ JW Marriott Houston Downtown

11/26/2018

Our founder would be speaking at the RE.WORK Applied AI Summit. Hear the presentation on “The digital apprenticeship marketplace for the next generation of data professionals” on November 29-30 in Houston.
The summit is a unique opportunity to hear the latest advancements in AI and how it’s being applied in many Industries.
If you’ve been following her or DataGig, you know our mission is to empower one million data scientists and make a contribution towards the advancement of new AI technologies.
If you’ll like to learn more about our new apprenticeship program, send over a private message. :)
If you’d like to attend the summit, use code SP2018 for 20% off your pass here: https://bit.ly/2CFTE4K
Hope to see you there!! .

10/30/2018

The easiest way to pick a fight with a data scientist is to make fun of their chosen tools & programming language. If I called out R or python for any number of legitimate flaws, I'd get hate mail for weeks. Our dedication to them is more faith than fact based so our responses are more fanatical than rational.
You can get most data scientists to agree that each tool has its uses & flaws. If you say python is bad for this or R is poorly suited to that, an army rises to their defense.
You are not defined by your tools or programming languages. Their flaws are not yours. Expand your skills by learning new ones. Admit where your chosen set is right & wrong for the job. Don’t worry about defending your chosen tools & languages. Their capabilities can defend themselves.
Most of all don't cling to them when they are obviously ill suited to the task. I've cleaned up enough messes caused by, "That's just what we've always used."

10/24/2018

I’ll post about my experiences in data science & machine learning & I’m often asked for a case study. I am the case study. If you’re in the field using anything other than regression, random forests, Bayesian methods, etc., you are the case study too.
We often forget how young our field is. There are firsts happening every day. Many companies are now working with data science, but few have gone beyond basic approaches. Fewer have built a repeatable process for getting them into production. If you’ve done either of those things, you are the case study.
When Cassie Kozyrkov talks about building AI based systems, she is the case study. When you build a deep learning model that outperforms traditional approaches, your work is the case study.
The knowledge we share with each other is so important. We’re each learning in our own way from experience. Spread what you’ve learned. Even if you called it a failure, you’ll prevent others from going down the same path.

10/24/2018

The easiest way to pick a fight with a data scientist is to make fun of their chosen tools & programming language. If I called out R or python for any number of legitimate flaws, I'd get hate mail for weeks. Our dedication to them is more faith than fact based so our responses are more fanatical than rational.
You can get most data scientists to agree that each tool has its uses & flaws. If you say python is bad for this or R is poorly suited to that, an army rises to their defense.
You are not defined by your tools or programming languages. Their flaws are not yours. Expand your skills by learning new ones. Admit where your chosen set is right & wrong for the job. Don’t worry about defending your chosen tools & languages. Their capabilities can defend themselves.
Most of all don't cling to them when they are obviously ill suited to the task. I've cleaned up enough messes caused by, "That's just what we've always used."

10/22/2018

Much of the difficulty companies face in hiring data science & machine learning talent comes from an incomplete product strategy. Businesses know they want to build products & services with AI but don’t have a roadmap laying out exactly what they’ll be building.
That uncertainty forces them to hire expensive generalists who can build whatever AI systems the company might need instead of specialists who are experts at building exactly what the business requires.
An AI product strategy clearly outlines what projects will best support the business goals. That makes sourcing talent a targeted effort that considers both near & long-term project needs. The lengthy, hard to source generalist job descriptions are replaced with narrow specialist roles. Those are far easier to source & build teams to support.
There are several downstream impacts from an incomplete AI product strategy & generalist hiring is one of the most expensive.

09/17/2018

Here is a list of what I believe are the 10 Practical Steps for :
1. Programming
a. Python - https://lnkd.in/gGQ7cuv
b. R - https://lnkd.in/giMGbph
c. SQL - https://lnkd.in/gM8nMNP
d. Command Line - https://lnkd.in/e3EQuis
2. Stats/Prob/Math
a. Coursera's Statistics w/ R - https://lnkd.in/gGT9NEf
b. edX's Probability - https://lnkd.in/gpUyC3P
c. Khan Academy Linear Algebra - https://lnkd.in/gMshbX4
3. Data Viz
a. Python Matplotlib- https://lnkd.in/gr3ifNt
b. R ggplot2 - https://lnkd.in/eThJXNr
4. Data Manipulation
a. Python Pandas - https://lnkd.in/g9kfpX4
b. R dplyr - https://lnkd.in/gAWusih
5. hashtag
a. Google Crash Course - https://lnkd.in/gSgkVcT
b. Stanford Coursera - https://lnkd.in/g8ZG557
c. ISLR Book - https://lnkd.in/gk8GPZC
6. Experimental Design
a. Udacity A/B Testing - https://lnkd.in/gCerh4f
7. Business Sense
a. Metrics - https://lnkd.in/gZAG7bS
8. Communication
a. Storytelling - https://lnkd.in/gwjxVUu
9. Profile Building
a. GitHub - https://lnkd.in/g4r9naJ
b. LinkedIn - https://lnkd.in/g-KHHEC
c. Kaggle - https://lnkd.in/gBC77Hu
d. DS Resume - https://lnkd.in/gU8WVAF
🏅 10. Job Search
a. Daily Expert Tips & Advice -https://www.facebook.com/groups/390189848151615/

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Hope this helps! 👍

DataGig

10/31/2019

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