Gift Ojeabulu

Increased to :

- 44 Countries.
- 8 US States.

Where the top countries (subscribers) are:

- Nigeria.
- Kenya.
- India.
- Ghana.
- South Africa.
- Uganda.
- US.
- United Kingdom


in 5 days of sending out the newsletter through the mail and It has gotten over 1400 views, 1200 reads and extra 180 subscribers.


The goal has always been expanding to every country in Africa and extending to other continents in the world.

I was a co-host for this podcast session.

We talked about the Nitty-Gritty of data engineering where we addressed the following:

- The important tools that one should be familiar with as a data engineer, such as Hadoop, Spark, Kafka, and SQL. Understanding these tools is critical for success in the field.

- We explored the key priorities of a data engineer, such as data integration, data quality, and data architecture, and how these priorities differ from those of other… Show more

I was a co-host for this podcast session.

We talked about the Nitty-Gritty of data engineering where we addressed the following:

- The important tools that one should be familiar with as a data engineer, such as Hadoop, Spark, Kafka, and SQL. Understanding these tools is critical for success in the field.

- We explored the key priorities of a data engineer, such as data integration, data quality, and data architecture, and how these priorities differ from those of other roles in the data industry and require specific skills and knowledge.

- We compared the differences between transitioning to a data engineering role from an analyst position versus from a software engineer role.

- We discussed strategies for kickstarting a data engineering journey. Show less

Learn the step-by-step approach to building a scalable data community of data engineers, data analysts, machine learning engineers and analytics engineers around a product from top data community experts.

I was involved heavily in community-building in the data analysis, data engineering and machine learning space from the African Data Community Newsletter, Data Community Africa, Lagos MLOps community, and currently Pycon Africa.

I felt the need to put together a write-up on this… Show more

Learn the step-by-step approach to building a scalable data community of data engineers, data analysts, machine learning engineers and analytics engineers around a product from top data community experts.

I was involved heavily in community-building in the data analysis, data engineering and machine learning space from the African Data Community Newsletter, Data Community Africa, Lagos MLOps community, and currently Pycon Africa.

I felt the need to put together a write-up on this as I also felt it is something barely written or talked about in the data space, but its need keeps growing from individuals to companies.

For whom is this article?
- Entry — Level, experienced developer advocates, and community managers interested in building and managing data communities.
- Data Engineers, Data Analysts, Data Scientists and Machine Learning Engineers.
- Founders, CEOs, data product managers, and companies who are interested in building or managing data communities around a product in data engineering, artificial intelligence, machine learning and data analytics space.

What will we learn?
- A typical prototype on how to build and foster great data Communities: best practices from three data community experts.

- The Community Builder ToolKit: Software Tools that help data community building and management easier. Show less

This article is for:

- Technical and non-technical data professionals.
- Data enthusiasts who are interested in joining data communities to connect with other data practitioners and to get a clear path in their data careers.
- Data professionals looking for the best data communities to join with like minds.

What you will learn:

- Data Community As A Service.
- The benefit of the data community to a data enthusiast or practitioner.
- List of top data communities… Show more

This article is for:

- Technical and non-technical data professionals.
- Data enthusiasts who are interested in joining data communities to connect with other data practitioners and to get a clear path in their data careers.
- Data professionals looking for the best data communities to join with like minds.

What you will learn:

- Data Community As A Service.
- The benefit of the data community to a data enthusiast or practitioner.
- List of top data communities for data enthusiasts, professionals, and their specific impact. Show less

As an Invitee to the Keycut Podcast, I shared my community experience on:

- How I got interested in the community through GDG Devfest Lagos
- AI Plus AAU
- DatafestAfrica as a co-founder and community lead
- MLOps Community Lagos as an organizer.
- A data scientist at CBB Analytics.

In this episode, we have a fantastic discussion with Matthew Blasa (Data Scientist @ Home Depot) about one of the most asked questions: How to get a data job? Coming from Matthew's experience, we would explore the possibility of how to secure a data job and various tips you don't want to miss!

This month you will find ADC Newsletter:
- Sponsorship.
💥👨‍👨‍👦‍👦 Data communities and events.
🚞💵 AI startup News.
✍🏾👩🏽‍💻 Technical Articles.
🥰 Featured Videos.
✍🏼 Featured Tweets.
👨🏿‍🤝‍👨🏿Jobs and opportunities.

Slik-wrangler is a data to modeling tool that helps data scientists navigate the issues of basic data wrangling and preprocessing steps.

As the Technical Documentation lead to this open-source library, I'm currently revamping & building better documentation following the best practices.

In this Article, I gave a classical introduction to JavaScript libraries for data science and machine learning, data visualization, deep learning, natural language processing and computer vision. I pointed materials to get started with using Javascript for data science.

I explained how the future of machine learning is Javascript.

In this episode of the engatica interview series, I talked about the importance of MLOps and tells us what is the significance of Open Source ML models in the industry today.

I talked about:

- Artificial intelligence.
- Open-source.
- MLOps.
- Community.

In this issue...

I wrote about the current:

- Community News in the Africa Data space.
- Featured technical articles on Data Science, Machine learning, NLP and MLOps
- Open tools & contributions.
- Africa AI Startup News.
- The African Data Twitter community and featured video.

I gave a talk at MLOps World New York as the first-ever African to speak at MLOps world New York, where I talked about validating your machine learning models & data with minimal effort using DeepChecks


- What is Deepchecks?
- When should I use Deepchecks?
- Deepchecks validation structure.
- When should I validate?
- A practical scenario on how to validate your data & ML models with Deepchecks

- I gave a talk about DagsHub as a crucial open source data tool at the biggest open source conference in Africa. I'm the first person to give a talk at a conference about DVC & DagsHub open-source tools in Africa.

The talk got featured on Iterative newsletter.

Thia is my first Newsletter..

I wrote about the current:

- Community News in the Africa Data space.
- Featured technical articles on Data Science, Machine learning, NLP and MLOps
- Open tools & contributions.
- Africa AI Startup News.
- The African Data Twitter community and featured video.

I was invited to a Data Science youtube channel by a Data Scientist at Benshi ai to talk about the tips and tricks to landing your first Data Science/Machine learning Engineer Job as a newbie in the field.

One of the reasons why we decided to address this issue was because it is one of the most challenging problem beginners face in the field.

This compelled us to address this issue as experienced Data Scientist talking from a practical perspective.

STATISTICS
- 41 mins… Show more

I was invited to a Data Science youtube channel by a Data Scientist at Benshi ai to talk about the tips and tricks to landing your first Data Science/Machine learning Engineer Job as a newbie in the field.

One of the reasons why we decided to address this issue was because it is one of the most challenging problem beginners face in the field.

This compelled us to address this issue as experienced Data Scientist talking from a practical perspective.

STATISTICS
- 41 mins video length.
- Over 300 views.
- Over 45 likes. Show less

This is my slide for a physical tech-event (GDG DEVFEST ABEOKUTA 2021), Where I gave a talk as an invited Speaker about the importance and reasons for maintaining mental health for all Software Developers and Data Practitioners.

What inspired me to write about this topic is the fact that a lot of beginners reach out to me for questions like:

- What are the things I need to become a good data scientist?

- How can I improve myself as an absolute beginner?

In this Article, I explained six important productivity tips to improve yourself as a beginner,intermediate , junior, undergraduate, or aspiring data scientist.

This are tips that will fast track productivity in your data science journey as… Show more

What inspired me to write about this topic is the fact that a lot of beginners reach out to me for questions like:

- What are the things I need to become a good data scientist?

- How can I improve myself as an absolute beginner?

In this Article, I explained six important productivity tips to improve yourself as a beginner,intermediate , junior, undergraduate, or aspiring data scientist.

This are tips that will fast track productivity in your data science journey as a beginner.

STATISTICS:
- Over 1.1k views
- 4hrs LIFETIME MEMBER READING TIME.
- 4mins AVERAGE READING TIME
- Over 300 claps 👏
- 8 minutes Article (Over 1200 words) Show less

After writing the first article, I thought about it not everybody understands statistics or gets insights from statistical tables at a glance to some this might be confusing and might lead to plaintiff about the vagueness of the analysis. I explained Exploratory Data Analysis with Indepth Data visualisation with Python.

STATISTICS:
- Over 2.1K views
- 3hrs 15 mins LIFETIME MEMBER READING TIME.
- 4mins AVERAGE READING TIME
- Over 235 claps 👏
- 16 minutes Article (Over… Show more

After writing the first article, I thought about it not everybody understands statistics or gets insights from statistical tables at a glance to some this might be confusing and might lead to plaintiff about the vagueness of the analysis. I explained Exploratory Data Analysis with Indepth Data visualisation with Python.

STATISTICS:
- Over 2.1K views
- 3hrs 15 mins LIFETIME MEMBER READING TIME.
- 4mins AVERAGE READING TIME
- Over 235 claps 👏
- 16 minutes Article (Over 3200 words) Show less

In this Article, I gave an explanation using Exploratory Data Analysis to give answer to things I was curious about while Playing FIFA 2021.

The motive is to carry out statistical analysis and generate insights like a pro with python using FIFA 21 as a case study.

STATISTICS:
- Over 2.1K views
- 10hrs LIFETIME MEMBER READING TIME.
- 4mins AVERAGE READING TIME
- Over 365 👏claps
- 11 minute Article (Over 2500 words )

- Article of the month & Featured on Black… Show more

In this Article, I gave an explanation using Exploratory Data Analysis to give answer to things I was curious about while Playing FIFA 2021.

The motive is to carry out statistical analysis and generate insights like a pro with python using FIFA 21 as a case study.

STATISTICS:
- Over 2.1K views
- 10hrs LIFETIME MEMBER READING TIME.
- 4mins AVERAGE READING TIME
- Over 365 👏claps
- 11 minute Article (Over 2500 words )

- Article of the month & Featured on Black in Data blog with lots of amazing reviews. Show less

In this paper, we trained two convolutional neural network on Malaria and Pneumonia images and embed it into a web application that can run locally. This application can help health practitioners to quickly detect pneumonia and malaria infected patients. Malaria images are in the form of microscopic slide smears while pneumonia images are from chest Xrays. We show that our application can be infused into any hospital’s existing system and can be useful in areas with scarce health personnel and… Show more

In this paper, we trained two convolutional neural network on Malaria and Pneumonia images and embed it into a web application that can run locally. This application can help health practitioners to quickly detect pneumonia and malaria infected patients. Malaria images are in the form of microscopic slide smears while pneumonia images are from chest Xrays. We show that our application can be infused into any hospital’s existing system and can be useful in areas with scarce health personnel and limited resources. Show less