Image Credit: Franki Chamaki

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(My Tech Journey Part 2)

According to Wikipedia, “Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured”.

For me, data science is the act of a machine using data to make the best decisions. It begins with getting the data from the real world such as surveys, registrations etc, cleaning the data, extracting important feature and information, converting it to the proper format, training the model, validating and testing the model and finally, deploying your model for use. Data science and artificial intelligence are inter-related. Data powers the intelligence of these machines, and machine learning is the technique in which these computers learn to be intelligent.

Machine learning is usually applied in data science to yield artificial intelligence.

In my previous article, I tried to summarize how I ended up here. Trying my hands on various programming languages, web development, digital marketing and then finally, data science. Nsikak Thompson asked me on Twitter “Why Data Science?’. Been trying to put all my thoughts in an article for a little over a week, I hope I’m able to do this efficiently now.

I see data science as the perfect intersection of my passions (technology, business and research).

Data science obviously is a technical field, can be applied in businesses and corporate organizations and also there is a lot of room for research in the field. Data Science can be applied in Businesses, Information Search, Advertisement, Recommendation Engines, Image Recognition, Speech Recognition, Travel, Fraud detection among others. Sound knowledge of Mathematics and Statistics is a plus in this field.

My learning journey has been a beautiful one, challenging but very exciting and rewarding. Personally, I use Python 3.7 and it’s serving me well. So far, I have taken various online Introductory courses on Python, Recommendation Systems, Natural Langage Processing, Statistics and Machine Learning on platforms like Udemy, edX, Coursera and Udacity. I hope to take more advanced courses on machine learning and natural language processing in future.

Currently, I have been able to work with my team mates on analyzing data for business decisions such as user analytics, building recommender systems and text analysis. I am eager to perform more complicated tasks soon as working on real projects actually increases knowledge.

I have made some friends on Twitter that are always willing to offer me help and encourage me. My online mentors are quite many but I’ll try to list a few:

  • Siraj Raval is a YouTuber who breaks down and explains complex Data Science concepts
  • Andrew Ng creates content (books and MOOCs) on Machine Learning, Artificial Intelligence and Deep learning
  • Data Science Renee curates data science learning resources, jobs, and connects women in Data Science.

Some Community Support Groups I follow are:

For those who prefer offline learning: Coven Labs, Git Girl and AI Saturdays Lagos are places you should check out if you’re around their location.

In the future, I hope to get invested in my passion for education and somehow make impact in education in Africa. I also hope to be part of ground breaking research in my field that affects my country positively and places us on the map in a good light. If a cure for glaucoma has not been found yet, I’m willing to use my skills and resources to help glaucoma patients manage it and never lose their sight. Not forgetting my love for businesses and technology. It’s all intertwined…

Finding it hard to come up with a conclusion for this article, but I really hope you could pick up a thing or two. This is just my opinion as a newbie in this field and not an industry fact. If you’re considering Data Science or any related area, please take time to do your personal research. If there’s any error here, please reach out to me as I’m open to corrections and learning.

I also look forward to your suggestions on people in the industry you follow, your favourite resources and community support groups that are quite inclusive (for Women of Colour in Tech).

You can reach out to me on Twitter, I answer my DMs especially work related. Please, kindly share this article with people who you believe will receive a bit of help from it. Shout out to Bolaji for proof reading and editing the manuscript.

Thanks.

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