Introduction to Data Science

What is Data Science

Data science is a discipline that combines domain knowledge, programming abilities, with maths and statistics knowledge to extract useful insights from data.

Data scientists are set of people that acquire and analyse enormous sets of organised and unstructured data. A data scientist’s job entails a mix of computer science, statistics, and math. They interpret the outcomes of data analysis, processing, and modelling to generate actionable plans for businesses and other organisations.

Future of Data science

Data (numbers, text, photos, video, audio, and more) is expanding by the day, and since the beginning of time till 2005, man has generated 130 Exabytes of data, which encompasses all forms of data that humans have made, such as songs, spoken word, written word, images, and so on. In 2010, the amount of data generated was 1200 exabytes, which increased by a factor of four in 2015 to 7,900 exabytes of data, and by 2020, the amount of data generated was 40,900 exabytes. So we can see that data in the world is growing exponentially, and there is so much data that machines can use, process, and make sense of in the world that data scientists are far behind, because there is a massive gap between the amount of data and what scientists can process, and even between the amount of data a machine can process to derive value from a data scientist.

All of these gaps are widening each day, and data scientists are in high demand because companies and individuals want to extract value from their data. If you are considering a career in data science, you are making the right choice.

Areas of Data Science

Data science is split into different areas, and we need to have fundamental knowledge in these areas because most people classify it to be an intercept of computer science, statistic and domain knowledge-driven, but the modern data scientist needs to quite develop in more than those three above mentioned areas and I am going to be giving you insight on areasyou need to develop your skill-set in data science.

Statistic: First you’ll require fundamental knowledge in statistics because you’ll be working with data and hard facts, and you’ll need to be able to distinguish between real trends and random noise.

Visualization: We need to constantly represent the correlation between information and data graphically, using visual elements like charts, graphs and maps, therefore leading to the need to design models that classify populations and provide credible forecasts of future occurrences. Data scientists look beyond data representation, make meaningful insights from data and show it to others, which is why visualisation is crucial, and you should be comfortable with different visualization techniques.

Data Mining: is the creative side of data science, where you hunt for trends and discover things that no one knew the data can infer.

Data Processing: This is where you connect with your data warehouse or database, as well as data processes such as data cleaning, storage, and modification for improved prediction. Primarily, the collected data is translated into usable information.

Pattern recognition, Neuroplasticity and Machine learning: I’m lumping these three together since they all work toward achieving the same goal: teaching computers how to learn without being explicitly programmed, which is similar to advanced data science. Striking to improve your talents in this area will be quite beneficial.

Communication: Apart from the presentation of the data visualisation, you must be able to communicate and persuade your audience to let them see what only you as a scientist can see. If you are a scientist who cannot speak for more than 5 minutes, you have a problem and you must develop yourself in that aspect rather than just processing data and programming. Thousands or millions of useful business insights can be derived, but if they are not adequately communicated to key decision-makers, they are useless.

Presentation: This is another ability to hone, because you will be putting knowledge into people’s minds, and you should do so in such a professional and straightforward manner that even someone unfamiliar with the area can grasp the idea inyour presentation.

Domain Knowledge: Having a broad understanding of various aspects of the domain improves your ability to provide insights. You can easily research and develop the majority of this domain knowledge.

Real Life Practise: We can’t underestimate the value of getting your hands dirty with code and practising every day, therefore there’s a need to practise with real-world problems to sharpen your skill-set, confidence, and also your problem-solving abilities in the world of data science.

In summary, if you looking to start a career in this data science, I will congratulate you in advance and say you are making the right decision.

Note: Please follow me, and gain insight in data science once a week on this platform.

In my next post, I will be going in-depth in who is data scientist and data analyst and tools they need to carry out their assignment and give some insight in some course that they can take. Stay tune

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