Not everyone can make the time for being taught Data Science, AI or ML in a classroom and not everyone can also afford the costs involved with formally learning Data Science, AI or ML.

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As stated earlier in the subtitle, time and costs are big hinderances people face when attempting to learn Data Science, Artificial Intelligence or Machine Learning. Self-learning is an art that requires discipline, dedication and discipline to master. It gives you the flexibilty to couple learning with work or school if mastered properly. However, when starting to learn Data Science, AI or ML, the beginning stages are very tough but trust me when I say it is worth the worry. The key to making good progress when learning by yourself is to study at your own pace. In this story, I…

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“Simplicity is the soul of efficiency.” — Austin Freeman

Just as User Experience refers to the set of principles meant to be followed when building products that are to be used by end-users, Developer experience defines a clear guide towards building products with developers in mind.

Developers build almost all the tools that power the internet. They, one way or the other, provide access to quality software to all classes of people, regardless of their race, gender or religion and hence, need to also be prioritized when building products with them in mind.

Products built for developers need to be…

Your journey of learning to become a data scientist or ML engineer would always have to start from a point where you practically know nothing. Here’s how you can choose a learning path and make the best out of what you learn.

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Learn as though you would never be able to master it; hold it as though you would be in fear of losing it. — Confucius

Learning something normally makes people anxious, there are probably a hundred questions running through your mind about deciding to learn data science. All your fears and worries are valid, it is very safe to have questions about learning a new technology especially if you are transitioning from another field. …

Predicting Food Prices Using Linear Regression

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What is Data Science?

Data science is an interdisciplinary field of study which focuses on using the scientific process to analyze raw data and leverage the knowledge gained from analyzing the data to make data-driven decisions and build solutions.

Data science combines domain expertise, math, statistics, and problem-solving skills to make meaningful insights or inferences from raw data, and as such, data scientists need to equip themselves with modern necessary skills that will guide them in making such analyses and inferences from data.

Why Is Data Science Important?

Data science is a really interesting and fast-growing field. Over the years, data science has empowered many organizations to make data-driven…

What is cognitive AI bias and how can we fight it?

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We cannot understand the concept of bias in AI unless we first understand what the term ‘bias’ means. Bias, as defined by Wikipedia, is disproportionate weight in favor of or against an idea or thing, usually in a way that is closed-minded, prejudicial, or unfair. Biases can be innate or learned. People may develop biases for or against an individual, a group, or a belief. In science and engineering, a bias is a systematic error. Statistical bias results from an unfair sampling of a population, or from an estimation process that does not give accurate results on average.

Gender and…

The world today is evolving, there is no longer a need to work hard when you can work smart instead. The need for task automation is now more than just a hobby, it is a necessity.

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For an out-of-the-textbook definition, automation is basically the process of making a physically laborious task less easy to complete and with minimal human interference. Automating tasks helps reduce the amount of human effort wasted on that task, cut time and reduce the risk of human injury that may result in the process of completing or implementing the task. However, in this story, we would focus mainly on automating tasks in the software space. This is to say, writing automation scripts in various programming languages to get work done way easier, faster and with tremendous accuracy.

Automation is cost-cutting by tightening…

Tired of looking up hundreds of methods and functions over the internet to explore your dataset? Xplore makes the exploratory data analysis process 10x faster, all in just one line of code.

xplore logo design by Divine Alorvor

What is Data Exploration?

Data exploration, as defined by Wikipedia, is an approach similar to initial data analysis, whereby a data analyst uses visual exploration to understand what is in a dataset and the characteristics of the data, rather than through traditional data management systems.

It is at this stage in the data analysis process where data scientists or analysts, AI or ML engineers attempt to really understand the data they are working with. At this point, they seek to familiarize themselves with the data they are working with, so as to be able to know how efficient it will be in solving the…

You can most likely be successful in this field on your own, but here are a few reasons why you can achieve more and do better with a mentor.

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A mentor is someone who sees more talent and ability within you, than you see in yourself, and helps bring it out of you.— Bob Proctor

Learning to become a data scientist, AI or ML engineer can be very difficult even if you are privileged to have a formal classroom education. More often than normal, you learn so many abstract things that you find yourself wanting when you finally break into the field as a professional. Think of learning to be a data scientist, AI or ML engineer as being in a sandbox where you have a controlled environment around…

As a data scientist, artificial intelligence or machine learning engineer, you should know when to draw the line between using tools you have a soft spot for and tools that actually get the work done easily and faster.

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In the end that was the choice you made, and it doesn’t matter how hard it was to make it. It matters that you did.
― Cassandra Clare

As people who constantly write code, we develop soft spots for certain tools and technologies that we tend to use all the time even if they are not the best or advisable tools to use in certain instances. It is then our responsibility to know when to draw the line between using the right and appropriate tool, technology, or method, and using tools we just like.

When To Know What to Use

For any project you take as…

As an avid learner, building projects should be something that excites you. Instead of having to rebuild existing projects all the time, here are a few ways you can come up with awesome project ideas of your own.

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No Matter What People Tell You, Words And Ideas Can Change The World. — Robin Williams

Beginners in this field have become accustomed to reinventing existing projects as part of their portfolio projects and this is not a good thing. What makes you unique out of the hundreds of learners with the same projects you have built? What makes you stand out as a data scientist, AI, or ML engineer? In this article, I am going to share with you some tips on ‘generating’ your data science, AI, or ML projects to appear unique at what you do.

Re-invent, But With Style

This point…

Jerry Buaba

Software Developer and Machine Learning Engineer passionate about Data Science, Machine Learning and Analytics.

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