TechforTheTechie ~ Fresh out of School & Looking for a Tech Job in the Times of The 'Rona? This might Help

I'm in a Tech Leaders' Group on Facebook, and got inspired to post this after a question was posted by the Group moderator, asking how people who are new to the Tech field and looking for a job in the industry can go about it now.

...especially given the current climate of things with the job market and the adverse impacts of Covid-19 on said job market.

Per the title of this blog post, 

for any recent graduates looking for work in the Tech Industry who are not quite sure how to go about it, this might help.

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1.

Upskill. 

(There are a ridiculous level of discounts on Udemy right now. DataCamp, LinkedIn learning, Microsoft Learn, are all fantastic alternative platforms for learning tech skills)

2.

Update your CV / LinkedIn with the right keywords

(that's how the algorithms work!)

3.

Network!

"Hi, I'm really interested in what you do. Please tell me about your career / what you do / how you got there...?" type stuff works magic!

(We're all vain and like to talk about ourselves)

On the flip-side, the "Get me a job" type messages can be off-putting to some, but more importantly, don't really make you stand out from the crowd.

4.

Find a Mentor.

(Read above. That's your "in". Play the long game)

5.

Be Patient

I know it's tough, but frankly, you want to get to a point where you get a rejection email and can't even remember applying!

6.

Learn

...what's out there (generally) and what you may like to do before your deep-dive into Data Science, Data Analytics, Data Engineering, Cyber, etc

(Those first three may sound the same or similar, but they really aren't! 

Same goes for AI / ML / RPA)

This old blog post I wrote may be a good start to help you clarify the differences if this is all new to you!

https://techforthetechie.blogspot.com/2017/02/tech-for-techie-data-science-guide.html

7.

Curiousity wins!

GOOD LUCK!

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P.E II
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Leave a comment if you found this useful and share if you know someone this might help!

Thanks

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P:S: 

Once you've learnt new Tech Skills,   ...Practice, practice, Practice!

You'd be surprised the amazing things you can learn to do by taking on tech / data challenges on websites like Kaggle.

Highly recommend.

Publish your projects on sites like GitHub as well.

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Instilling a Data Driven Decision Making Culture in Your Business – A Blueprint

#TechforTheTechie: 'Gut' based decision making or Data & Analytics driven decisions in business. Despite all the “data knowledge” around us, are our leaders still “stuck”?

Image credit: Good Audience blog

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This short form blog post was inspired by a question posed on LinkedIn by another data professional:

Q.
“Can old school corporate structure digest all these insights and incorporate them in their actual plans? Is the AI and BI capabilities moving too fast?

Would like to hear from professionals. Any insights?”

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A.
Yes. We can, and we do.
BI / MI Delivery and the Change Management Leadership it induces go hand-in-hand.

However, it's a whole different skill set being able to manage that piece too and successfully instill a data-driven decision making culture.

Formula is:
1.
Find one "big money" problem the business cares about.

2.
Solve it with data.

3.
Now you've proven yourself, and the capabilities of data insight & business intelligence, keep that momentum and your newfound supporters engaged:

Do more.

4.
Institutionalise and roll out to regular delivery on a highly scalable regular data delivery piece.

5.
Do even more in the business with data.

Feel free to include your lower ROI data "passion projects"... like building a chat bot for some customer support channel.

6.
Realise there will be pitfalls.
Some plans will not go as planned.

That's fine.

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Shake the tree. Throw sticks at the branches. Climb the tree. Cut the tree down. Doesn't matter...

...Get the fruit!

When you know what you're doing and trying to embed a data-driven decision making culture,
There's always a way!

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P.E II

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NB: As I learn more in my career in Data Science, I find it really fascinating and always want to share.
I also stay inquisitive and try to learn new things all the time.

I'm making a personal promise to myself (and to you, the reader), to blog more often in 2020! :)

Leave a comment if you like what you've read, if you've learnt something... if you have a specific tech-based topic you'd like to know my opinions on!
:)

Also, I micro-blog a lot, in 280 characters or less!
Catch me on Twitter too. Links below.

Ciao!

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Tech for The Techie: The Data Science Guide I never had

An Introduction to Data Science - Where do I begin?

 by Paul Ekwere

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It’s a very difficult thing to be interested in a very broad and ironically esoteric topic such as data science and know where to begin your research. The most difficult part of any great journey is taking that first bold step.

Unfortunately, not many of us know what that first bold step should be.

Welcome to Data Science 101.

There are quite a few varying opinions on what 'data science' is. Interestingly, the most apt definition I have read in recent days was on wikipedia.

Data Science (definition): " Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to Knowledge Discovery in Databases (KDD)."

Source - Wikipedia: https://en.wikipedia.org/wiki/Data_science

As such, a data scientist, may be categorized as one who employs scientific methods, processes or tools to analyze structured or unstructured data to produce results that make the tasks of high level analyses, and drawing inferences and conclusions from the data simpler.

Data science is something I do everyday. It is something I have always done.

From my geeky love of weird sequences such as Fibonacci and hours spent as a pre-teen on my own creating my own unique logical sequences and custom cryptographic code, to my years as that-guy-in-class-that-doesn’t-use-a-calculator-to-do-his-math-but-rather-does-it-all-in-his-head, to my late teenage conquests of being that-guy-who-solved-the-rubik’s-cube-in-1-min-27-secs-flat-while-barely-even-looking-at-it-the-whole-time; to my latter years as an aerospace engineering student / honours graduate & now doing full-fledged data science work as a professional service, I have always been involved in one way, shape or form, in data science.

In my life as a data scientist, I have found that some things just come naturally to me. Other things, not so much. One thing though has stayed consistent. If I want an answer, I search for it. I find it!

If I’m puzzled by a problem, I ask, I read, I google, I annoy, I pester, until I solve it or find a solution!

Data science is exactly this!

  Be curious.

  Feed your curiousity.

  Find a problem.

  Fix it (or learn 1000 ways it can’t be fixed)

  Repeat above steps.

For a lot of people though, the first step is the hardest. So I thought I’d give you a little nudge in the right direction. Awaken that curiousity if you will.

I read a lot of posts on data science, analytics, machine learning and artificial intelligence. I also post every now and then on these topics on my twitter or my flipboard microblogs.

Among some of the things I have been reading, I thought it would be helpful to compile a list of some introductory data science topics & blogs I’ve recently read that may help. A basic guideline I wish I had when I first started my career.

They are, (in no particular order):

The Basics

• Jean-Nicholas Hould On Data Science: What is a Data Scientist? http://www.jeannicholashould.com/what-is-a-data-scientist.html
• DataViz: Six categories of Data Scientists http://www.datavizualization.com/blog/six-categories-of-data-scientists
• Data Science Central: No cost training to becoming a data scientist http://www.datasciencecentral.com/profiles/blogs/how-to-become-a-data-scientist-for-free-1
• Tech Republic: Why data science is just grade school math and writing http://www.techrepublic.com/article/why-data-science-is-just-grade-school-math-and-writing/
• Tech Republic: How to make yourself a DIY data scientist http://www.techrepublic.com/article/how-to-make-yourself-a-diy-data-scientist/
• Forbes: Data Storytelling: The Essential Data Science Skill Everyone Needs http://www.forbes.com/sites/brentdykes/2016/03/31/data-storytelling-the-essential-data-science-skill-everyone-needs/#fa0e678f0c8a
• CIO: IT career roadmap: How to become a data scientist http://www.cio.com/article/3057574/careers-staffing/it-career-roadmap-data-scientist.html
• Dataconomy: 5 Actionable Insights To Make You Stand Out In Data Science
  http://dataconomy.com/2016/04/5-actionable-insights-make-stand-data-science/
• ShapeScience: 10 pieces of advice to beginner data scientists
  https://shapescience.xyz/blog/10-pieces-of-advice-to-beginner-data-scientists/

Intermediate to Advanced

• Forbes: Improve Your Analytics Skills With These 9 Tips
  http://www.forbes.com/sites/metabrown/2016/12/31/improve-your-analytics-skills-with-these-9-tips/#6c2563781eb3
• SmartDataCollective: 7 Data Modeling Mistakes that Will Sink your Analysis
  http://www.smartdatacollective.com/eran-levy/431916/7-data-modeling-mistakes-will-sink-your-analysis
• Adobe Blog: Defining Machine Learning, Predictive Analytics, and Data Science — Without the Mumbo Jumbo https://blogs.adobe.com/digitalmarketing/analytics/defining-machine-learning-predictive-analytics-data-science-without-mumbo-jumbo/
• iamwire: Top 8 Big Data Tools for Enterprise Developers http://www.iamwire.com/2016/11/top-8-big-data-tools-for-enterprise-developers/132457
• TechCrunch: Facebook’s advice to students interested in artificial intelligence
  https://techcrunch.com/2016/12/01/facebooks-advice-to-students-interested-in-artificial-intelligence/
• MachineLearnings: The Non-Technical Guide to Machine Learning & Artificial Intelligence
  https://machinelearnings.co/a-humans-guide-to-machine-learning-e179f43b67a0#.4o4bv8axk
• PCMag: The Best Data Visualization Tools of 2016
  http://uk.pcmag.com/cloud-services/83744/guide/the-best-data-visualization-tools-of-2016

(Other) Interesting Reads

• People Profile - LA Times: IT career roadmap: How I Made It: Elena Grewal leads Airbnb’s data scientists http://www.latimes.com/business/technology/la-fi-himi-grewal-snap-story.html
• HBR: The Best Data Scientists Get Out and Talk to People https://hbr.org/2017/01/the-best-data-scientists-get-out-and-talk-to-people
• Forbes: The Rise Of AI Will Force Data Scientists To Evolve Or Get Left Behind http://www.forbes.com/sites/valleyvoices/2017/01/31/the-rise-of-ai-will-force-a-new-breed-of-data-scientist/#1e157fa432be
• Wired: 2016: The Year That Deep Learning Took Over the Internet https://www.wired.com/2016/12/2016-year-deep-learning-took-internet/
• TechInAsia: McKinsey finds it’s all talk and little action with data analytics in most companies https://www.techinasia.com/mckinsey-finds-data-analytics-sham

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UPDATE: Friday 24th February, 2017 @ 18:39 hours z

If you click through to none of those links above, you should definitely not leave this blog post without reading this.

Brandon Rohrer, Principal Data Scientist at Facebook puts it ever so simply.

Check out his blog post 'How to Get a Data Science Job: A Ridiculously Specific Guide'

You'll be glad you did!

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