Nina Okereke

Budding Data Analyst/Scientist | Mum to 3boys | Documenting my Journey to the Data World 🌎

[Nina Okereke @ninachars]

Cheers to a fresh start

I Will be documenting my journey of becoming a self-taught data scientist.
#100DaysOfCode
#DataAnalytics #DataScience

[Zach Wilson @Zachly]

Here’s everything you need to know to pass the SQL data engineer interview in big tech!

[Kshitij Mishra | AI & Tech @DAIEvolutionHub]

"Foundations of Machine Learning"

A MUST while starting AI/ML. Absolutely Beginner friendly.

To get: -
1. Follow (So I can DM you )
2. Like & retweet
3. Reply " Send "

[Teneika Askew | Analytics & Automation @teneikaask_you]

It's listed on Crunchbase with 2 investors! $1.5M Love this for him! https://t.co/p0wv5b7bVz

[Ethan Evans @EthanEvansVP]

People say the economy is tough and no one is hiring. I asked my network.

Here are 43 companies where insiders and told me they are hiring (with links):

[Splendor of SQL 🇬🇧🇩🇪💖 @iam_Uchenna]

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[Pau Labarta Bajo @paulabartabajo_]

The data science hype is over.

Companies realized that “a data science team focused on prototyping is not all they need”

Expand your toolkit.
Do you like data engineering? Learn that
Do you like ML? Enter into MLOps

It is time to grow my friend.

[Alex Xu @alexxubyte]

The Open Source AI Stack

What did we miss?

[Santiago @svpino]

AI will not replace you. A person using AI will.

[Pau Labarta Bajo @paulabartabajo_]

How do you put your ML models to work?

3 ways:

1. Batch: The model generates predictions on a fixed schedule (e.g. every hour)

2. Request-response: The model is exposed as a backend API.

3. Stream: The model continuously generates prediction on the most recent stream data.

[Pau Labarta Bajo @paulabartabajo_]

This is how you deploy ML models to production following MLOps best practices.

In 3 steps 🎬↓

[Zach Wilson @Zachly]

Here are the things that helped me go from a $100k/year data engineer to a $500k/year data engineer, in order of importance:

- move to a high paying state
You aren’t going to make this kind of money in Nebraska unless you’re Warren Buffett. You need to be in Seattle, Austin, Bay Area, or New York City to make this kind of cash

- learn the most in demand skills early
I learned Spark in 2013 way before it was cool. This made me an attractive prospect to hire when it started to boom. Identify new hot technolgy you’re willing to invest in and see what happens. Some good examples here are: data lake architecture with Iceberg, streaming pipelines with Flink, anything combining data eng and blockchain, anything combining data eng and AI

- invest in soft skills and leadership
$500k isn’t available to engineers who just code. You need to influence and impact the organization beyond yourself. Read the books: How to win friends and influence people, Radical Candor, and No Rules Rules to truly level up your leadership and soft skills.

- find the right mentors
I supercharged my career by finding Jitender Aswani when I was 22 and he taught me so much in the four years we worked together. Find someone who is further along than you who is willing to take a bet on your future!

- get lucky
There’s an element of luck with this stuff. Interviews are kind of random. Don’t get discouraged if you don’t get in the first time. Keep trying!

[Swapna Kumar Panda @swapnakpanda]

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[Ibukun Amosu 🇳🇬🇬🇭 @amosuibk]

Junior Analyst
Starting salary: USD 60k per annum
Location: Dubai, UAE (visa sponsorship & relocation assistance)
https://t.co/pfNbPZ43Ur

[Nina Okereke @ninachars]

Hi #datafam
been such a week. I haven't done my #100DaysOfCode since the last update. Will resume my learning later in the week as I'm pretty occupied with personal stuffs.

[𝕄𝕒𝕕𝕒𝕞 ℙ𝕣𝕖𝕤𝕚𝕕𝕖𝕟𝕥🚀 @MeetIjeoma]

Applications are now open for MTN-SAS Data Science Training Program 2022.

[Nina Okereke @ninachars]

@Solarithefirst Oh alright. That’s great.

[Nina Okereke @ninachars]

Day 54 to Day 56 of #100DaysOfCode
Started and completed the IBM Data Science course on Coursera. Understood the basics of data science, its definition in the simplest form and various application of it. A summary of my takeaway:

#DataScience #datafam

[Nina Okereke @ninachars]

@Solarithefirst Hi there, thank you. Congrats on completing your first course. No I did not have any issues verifying my ID.

[Nina Okereke @ninachars]

at hand via diagnostic measures and statistical significance testing.
5. Deploy and get feedbacks which then goes into improving the model until accuracy is obtained.
**Important to note that these processes are very iterative and continuous

#DataScience #DataAnalytics

[Nina Okereke @ninachars]

Day 66 of #100DaysOfCode
Completed the third course on IBM Data science. I learnt the key steps in solving data science problems which include
1. Understanding the business problem and developing an analytical approach to answering business question

#DataScience #DataAnalytics

[Nina Okereke @ninachars]

This may include the use of training dataset from historical data and subsequent use of the test data to gauge the output as well as calibrate the model for a more accurate result
4. Evaluate the resulting model and ensure that it answers the question

#DataScience #DataAnalytics