#datascience

Everything you do is feeding a monster bigger than all of us.

VO Services: https://www.tclvo.com/
Website: http://www.lecircle.net/
Email: laynetc68@gmail.com

Immerse yourself in this insightful podcast featuring special guest Victor Alasia, a Lead Solution Consultant at OpenText. The episode offers a comprehensive look into the intricacies of information systems, data management, and enterprise solutions across various sectors.

Victor discusses Geography, Systems Architecture, Solutions
Architecture, Data Management and Building Information Management.

By delving into these concepts in this conversation, you'll learn about:
- Interconnected Systems: Learn why considering sectors like construction, engineering, and asset management as isolated entities is a limited approach.
- Key Management Tools: Understand the critical role of Building Information Modelling (BIM) and Geographic Information Systems (GIS) in project and asset management.
- Organisational Challenges: Become aware of the common inefficiencies within companies and the need for a systems-level approach to remedy them.
- Data Standards: Gain insight into the importance of adhering to global data management standards and the pressing need for Environmental, Social, and Governance goals.

The episode wraps up with expert advice on the continual need for education in data management for efficient business operations. Whether you're a decision-maker in your organisation or simply interested in the complex world of data, this podcast is a valuable resource.

More about Victor Alasia, MSc, PGCert, BSc (Hons), MIAM, MCIHT, MBCS
Victor, who is currently a Lead Solutions Consultant, is a Solutions Architect, Systems
Architect, Spatial Scientist and Geographer with two decades of experience in Geospatial Solutions,
Enterprise Solutions and Enterprise Systems (including Environmental Management Systems, Urban
Management Systems, Asset Management Systems, Information Management Systems and Supply
Chain Integration and Interoperability Systems).

Victor's LinkedIn: https://www.linkedin.com/in/victor-alasia-miam-mciht-mbcs-50502820
OpenText: https://www.opentext.com/

Full References
As there is a very comprehensive list of references for this episode, these are contained in this document >references

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GET THE INTERNATIONAL INFORMATION MANAGEMENT 'BIM' STANDARDS!
ISO 19650 series and UK BIM Framework: http://www.iso19650.org/
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#datascience, #bigdata, #analytics, #machinelearning, #artificialintelligence, #cloudcomputing, #devops, #iot, #cybersecurity, #blockchain, #datavisualization, #datamanagement, #informationsystems, #enterprisesolutions, #geospatialsolutions, #bim, #gis #logistics #opentext

BimEnable Training

Visit the BimTube Podcast website at bim.tube to learn more!

BimTube's Mission: "We want to build everyone’s digital knowledge with content and conversations to inspire and instigate 'Better Information Management' to enable - better decisions, better infrastructure, better services and better outcomes for our social, economic and environmental infrastructure." Learn More: http://bim.tube

In this episode, Jon shares the key topics he recently discussed with the Open Data Science Conference. From the approach behind his extensive machine learning and deep learning content library to revealing the key tools and software he uses daily, get to know Jon and his process a little better.

Additional materials: www.superdatascience.com/558

Pandas expert Matt Harrison sits down with Jon Krohn to discuss tips, tricks and best practices for Pandas learning and mastery.

In this episode you will learn:
• Pros and cons of self-publishing and working with a publisher [5:05]
• Matt's six tips for using Pandas [17:13]
• The best way for corporate teams to level up their skills [40:04]
• How to learn anything effectively [47:14]
• Matt’s tricks for staying motivated [50:00]
• Matt’s recommendations for using Git and the Unix command line [1:00:14]
• Matt’s recommended software libraries for working with tabular data [1:19:45]

Additional materials: www.superdatascience.com/557

Discover Jon’s extensive library of machine learning content and learn why Jon's Machine Learning House forms the knowledge structure of an outstanding data scientist or ML engineer.

Additional materials: www.superdatascience.com/556

Data scientist and Youtuber Ken Jee joins Jon Krohn for a deep dive into the world of sports analytics and brings us behind the makings of his large, online data science community.

In this episode you will learn:
• The inspiration behind Ken’s YouTube videos [18:03]
• Ken’s four steps for getting started in data science [24:18]
• How sports analytics is transforming sports like golf [33:32]
• Ken’s favorite tools for software scripting as well as for production code development [41:10]
• How the #66DaysofData hashtag can supercharge your capacity as a data scientist [42:51]
• Ken’s data science podcast Ken’s Nearest Neighbors [54:11]
• LinkedIn Q&A [1:00:32]

Additional materials: www.superdatascience.com/555

In this episode, Jon shares where you can find his extensive deep learning video content and courses. Tune in to learn more about his deep learning curriculum and where you can learn for free.

Additional materials: www.superdatascience.com/554

In this episode, Dr. Josh Starmer, the creative, musical genius behind the wildly popular YouTube channel StatQuest joins the podcast to discuss statistics, learning and communication secrets, and how he grew his YouTube channel to over 650,000 subscribers.

In this episode you will learn:
• The inspiration behind Josh’s YouTube channel [18:39]
• Josh's simple approach to learning something new [34:25]
• Josh's secret tool for creating YouTube videos with over a million views [51:01]
• The StatQuest Illustrated Guide to Machine Learning [53:34]
• How and when Josh uses R vs. Python [1:07:53]
• How to cluster any types of data using the R randomForest package [1:11:24]
• Why Josh left his academic career [1:14:24]
• The two stats concepts Josh thinks everyone should know [1:38:50]

Additional materials: www.superdatascience.com/553

In this episode of Five-Minute Friday, Jon recaps the most popular SuperDataScience podcast episodes from 2021. See what you might have missed and catch up today!

Additional materials: www.superdatascience.com/552

In this episode, gifted author and software engineer Wah Loon Keng joins the podcast to dive deep into reinforcement learning. From its history to limitations, modern industrial applications, and future developments– there's no better expert to learn from if you want to know more about this complex topic.

In this episode you will learn:
• What is reinforcement learning? [4:50]
• Deep reinforcement learning vs reinforcement learning [13:17]
• A timeline of reinforcement learning breakthroughs [16:17]
• The limitations of deep RL today [39:53]
• Deep RL applications [53:10]
• Keng's open-source SLM-Lab framework [57:51]
• Keng’s responsibilities as an AI engineer [1:02:17]
• What is the future of RL? [1:08:05]

Additional materials: www.superdatascience.com/551

Jon is back with another Five-Minute Friday habit-tracking episode! Listen in as he explains how writing morning pages has helped his data science work flourish with creativity. Inspired by Julia Cameron's book The Artist's Way, he details his morning pages routine and how it kickstarted a new chapter in his career.

Additional materials: www.superdatascience.com/550

In this episode, Glean software engineer and Stanford graduate Lauren Zhu joins us to discuss her role at a fast-growing startup, working on natural language processing projects, and how she remains inspired by pursuing her side passions.

In this episode you will learn:
• Lauren's experience as a course assistant [5:53]
• Stanford's Hacking the Coronavirus Course [11:53]
• How do you empower minority groups in AI [19:45]
• Lauren on zero-shot multilingual neural machine translation [23:25]
• Lauren's work at Glean [27:58]
• The Contrary Talent Network [34:30]
• The tools Lauren uses at Glean [43:39]
• The most important skills to possess as a data scientist [47:29]

Additional materials: www.superdatascience.com/549

Our Five-Minute Friday series on habit tracking returns with a look at one of Jon's daily mindfulness habits–meditation. Learn how to keep this habit going for the long run and discover which tools help Jon stay on track.

Additional materials: www.superdatascience.com/548

In this episode, Dr. Jonathan Flint, Professor of Psychiatry and Biobehavioral Sciences at the University of California Los Angeles, joins us to discuss how he uses data science and machine learning to explore the link between genetics and depression.

In this episode you will learn:
• Johnathan's background [2:53]
• How we know that genetics plays a role in complex human behaviors including psychiatric disorders like anxiety, depression, and schizophrenia [8:00]
• The role that data science and ML play in modern genetics research [15:08]
• About Jonathan book "How Genes Influence Behavior" [19:45]
• The day-to-day life of a world-class medical sciences researcher [32:24]
• The open-source software libraries that Jonathan uses for data modeling [40:33]
• A single question you can ask to prevent a severely depressed person from committing suicide [52:00]
• LinkedIn Q&A [54:41]
• The future of psychiatric treatments [1:05:35]

Additional materials: www.superdatascience.com/547

Our Five-Minute Friday habit-tracking series continues! Learn more about alternate-nostril breathing–the mindfulness technique that is scientifically proven to lower blood pressure and regulate the stress response.

Additional materials: www.superdatascience.com/546

Data scientist and entrepreneur Matthew Russell joins Jon Krohn to discuss the intersection of machine learning and fitness and dive deep into the strategies he and his team at Strongest AI use to scale data-intensive real-time applications.

In this episode you will learn:
• About Strongest's event platform and iOS app [6:06]
• How Strongest scaled to serve million [8:14]
• Strongest's unique approach to building a fitness app [17:50]
• How to rapidly test ML models for deployment [29:01]
• The three critical traits Matthew looks for in anyone he hires [33:11]
• Mining the Social Web [41:14]
• The values instilled in Matthew by pursuing a military education [53:30]
• The key skills Matthew wishes he’d learned earlier in his career [1:03:51]

Additional materials: www.superdatascience.com/545

Our habit-tracking series continues with a look at how making your bed can jumpstart your mornings, prevent you from taking part in negative habits and help you become happier.

Additional materials: www.superdatascience.com/544

Nicole Büttner (Founder and CEO of Merantix Labs) joins the podcast to discuss driving A.I. innovation, automation, and transformation and building the ideal A.I. start-up founding team.

In this episode you will learn:
• The three factors that spark A.I. innovation [12:48]
• How to make great use of the unlabelled, unbalanced data sets [18:54]
• How to engineer reusable data and software components [25:09]
• Merantix's A.I. Canvas framework for successful innovation [29:59]
• How to be a part of Merantix's program as a founder [45:23]

Additional materials: www.superdatascience.com/543

Revisit the much-underrated continuous calendar and get started with this uncommon planning method thanks to Jon's 2022 template. 

Additional materials: www.superdatascience.com/542

In this episode, Kevin Hu joins the podcast to talk about founding and growing the data observability startup, Metaplane. Listen in to hear about his time in academia at MIT, his experience with Y Combinator, and his current routine as a technical founder.

In this episode you will learn:
• What is data observability? [4:35]
• How to identify data quality issues? [8:56]
• Kevin's PhD research on automating data science systems using machine learning [16:18]
• Why Kevin launched Metaplane [28:50]
• The pros and cons of an academic career relative to the start-up hustle [31:57]
• Kevin's experience in Y-Combinator accelerator [39:50]
• The software tools he uses daily as a CEO [50:54]
• What Kevin looks for in data engineer hires [56:13]

Additional materials: www.superdatascience.com/541