The Essence of Data Science

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There is a popular belief amidst data enthusiasts that data science is about the tool. While being a data scientist involves knowledge of the tools, that is not all that there is to it.

The data science community is slowly becoming a tool-centered one rather than value-centered. Tool centered because we are more concerned with the sophistication of a tool used in data science that we forget the tool is a means towards driving value.

There is a delicate balance that must be maintained such that, as data enthusiasts learn a tool, they must also understand the tool is what helps drive value i.e …


Basic use cases for using functions in Python

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Functions are a great way for you to make recurrent processes easiser in scripting languages. They make it easy for you to divide codes into useful blocks, readable and time saving. Understanding the basic aesthetics of functions will be a great building block to help you understand how much more complex functions work.

This brief article has been carefully put together, to help you understand how functions work using very basic procedures. By the end of this article, you will understand why you need functions and how to write basic functions.

Writing Functions

You can pass data known as parameters into a function and the function can return data as a result. To define/build a function, you use the keyword…


Twitter Analytics Report by The Future of Work (First published Nov 19, 2019)

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As the nation prepared for the much-awaited Kogi elections, the trends that the voting process threw up online were worthy of analysis. It was clearly going to be a fierce contest amongst key rivals.

This analytics report was carried out in 2019 for the 2019 kogi elections. For an in-depth analysis, The Future of Work Africa collected data from November 15th 2019- November 17th 2019 using the hashtags #KogiDecides and #KogiDecides2019.

A total of 172,728 tweets were collected altogether. Breaking this down, we had 129,788 retweets from 44,893 unique handles. That’s a whole lot of handles considering the Nigerian twitter population and the fact that it was a state election. …


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One thing is clear when it comes to data science and it is that resources are readily and massively available. However, there is a constant problem data science newbies (or even professionals) constantly run into.

The field is competitive and like every other field of study, it has experts whose portfolio may seem intimidating. Looking at such a feat can either motivate or discourage you. We propose you let it be your motivation rather than discourage you.

Your focus should be on building your career. Let’s say you’re not discouraged about the portfolio, and your problem is how to start. …


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Every kind of data an organization generates if properly utilized has the ability to impact business processes. It’s no longer news that data is the new oil or some would like to call it a gold mine and data science is how we mine it.

Organizations generate data daily, in diverse ways. For example, it could be the response profit to certain price adjustments. That will show how strong the demand for their offerings are. Also, it could be a random social media post by a consumer praising the firm or dishing out a complaint. …


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Every day, petabytes of data are created from different sources and objects. Every year, the volume of data created exceeds the previous as online activity and storage capacity grows. All this data is coming in the form of online shopping history, social media posts, pictures and videos in digital format, online transactions, cell phone GPS signals, search results, passport scans, barcode readings, EKG readings, CCTV footage, voice messages, and social media activity. Cumulatively, all these forms of data constitute what is referred to as Big Data which can be analysed and monetised in the long run.

Big Data refers to a collection of data that is huge in size and keeps growing exponentially on a continuous basis. This form of data is not only large but also beyond the capacity of traditional data management tools. One of such tools is Microsoft Excel, which can only handle 800,000 rows of data. The closer it gets to the maximum point, the less efficient it is for storage and processing. A typical Big Data set could have to 30 or 40 million rows. …


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Everyone seems to be so focused on learning data science tools like Python, R, Power BI, etc. However, without a database, data science is useless. In fact, the reason why data science is continually evolving is due to the massive increase in data.

Conventional data, such as flat files were not massive enough to cause the emergence of a new field, a statistician was usually good enough to handle such data. Since the massive evolution in data collection, storage, retrieval methods, data science has majorly been burdened with the responsibility of helping stakeholders manage their data systems for insights.

Structured Query Language (SQL), is used to communicate with a database. It is the standard language for relational database management systems. SQL statements are used to perform tasks such as update data on a database or retrieve data from a database. …


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Some higher institutions have gone the long stretch to make Data Science a full-fledged academic discipline with a qualifying bachelor’s degree awarded at the end of the program. Like the field of data science, the B.Sc. Data Science covers the entire breadth of compiling, organizing and analyzing data. One of such school is the University of Utah through the School of Computing, under the College of Engineering. They also have an M.Sc. that focuses on data management and analysis.

This is however not the norm for every school. The field of Data Science is relatively new and as such, there are still dichotomies in the academic programs of schools that have started offering it as an academic discipline. In some schools, the statistics department adopts it and is renamed the ‘Department of Statistics and Data Science’. Unfortunately, Data Science is usually run as a secondary or the Statistics program, hence it doesn’t get adequate attention. …


Resolve your confusion on overlapping concepts

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The buzz in the word data science and an explosion in demand has led to the emergence of various opinions about certain basic concepts in this field.

A typical example is an assumption that once you’re involved in any aspect of data science then you’re automatically a data scientist. That may not be exactly true. Just like engineering or medical science is a household name, data science is considered a household name also.

Under the engineering field, you have subfields such as civil, electrical, mining or agro divisions, all still considered engineering but different specializations. This article attempts to demystify certain concepts that must be understood by any individual attempting to delve into this field called data science. …


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Python has been around since 1991 and is generally considered to be one of the slowest programming languages. Notwithstanding its age and speed, it is still one of the most popular programming languages.

According to the IEEE Spectrum 2019, Python is the most popular programming language with data scientists. How did the IEEE arrive at this result and what are the factors leading to this? We’ll get into all of these in this article. Generally, Python is well-known for its strong deep learning and machine learning libraries and tools such as scikit-learn, Keras and TensorFlow. …

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The Future Of Work

"The best way to create the future is to create it." This project seeks to prepare professionals for the future through the Application of Data Science and AI.

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