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What is data science and how do we use data?

What is data science? Explore this question by watching the video and getting involved in the discussion in this step.
London, 1854. Cholera runs unchallenged throughout the city, with people dying in the streets. John Snow, a doctor, an obstetrician with an unproven hypothesis that contaminated water transmits disease, took up the challenge. He set out to interview sick patients and recorded their locations, movements and social interactions. Plotting his findings on a map with coloured pins. By visualising the cases in this way, he was able to cross reference local water sources and successfully identified that the majority of cases centered around a well and water pump on what was then Broad Street in Soho. John Snow was able to show a link between the localised cholera outbreak and this water source, and so ordered the handle of the water pump be removed.
This didn’t stop the cholera epidemic that ravaged London in the late 19th century. But John Snow’s pioneering medical research led him to solving a problem, building evidence to support his theory, and making a decision that undoubtedly saved lives. This early example of problem solving through the collection and analysis of data using rudimentary visualisation that demonstrated clustering techniques could be considered a strong historical precursor for what we today call data science. Fast track to the modern day and digital data is being created by everyone everywhere and this big data holds the potential to transform the world around us. It’s in our smartphones. It’s in our personal records, our financial transactions, our credit history, even our social interactions.
Data is a force for change in the world, and much of that change is driven by the art of data science. Data science, by definition, is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data. Data science asks “Why” and “What” questions. “Why is this happening?” and “What will happen?” based on past and emerging data and in doing so can discover meaningful significance behind patterns and trends in the data which could predict what might happen in the future. Data science is a relatively new but rapidly growing scientific field that has seen an increase in recruitment across a range of sectors, including banking, commerce, healthcare and research.
A data scientist is a professional with the technical knowledge and skills to transform raw data into meaningful insights that can lead to data driven decision. The role of a data scientist is multidisciplinary, encompassing not only the scientific and technical skills of mathematics, statistics, and IT proficiencies in programming languages, data extraction and processing, but also requiring domain knowledge of a particular sector, company or service with an entrepreneurial mindset and the ability to visualise and communicate the stories that data holds to a diverse audience of stakeholders and decision makers. To better understand the knowledge and skills required to be a data scientist and how data science projects work to create solutions or data products, we can explore the data science life cycle.
The data science life cycle describes the process of tackling a problem or answering a question by gathering and transforming data, modeling, training, and implementing algorithms to produce data, products and solutions. A successful example of a data product is a product recommendation system on eommerce platforms. This is where product advertising is tailored to the user based on previous orders, web and search history data. 35% of Amazon’s revenue is driven by their product recommendation algorithms. That’s over 30.5 billion dollars. Data mining of GPS navigation, traffic, and commuter behavior allows logistic companies to streamline deliveries based on the best times and routes to take. Uber eats is a company that uses machine learning and statistical modelling to optimize their deliveries.
Not only does this have a positive effect on cost efficiency, but is also reducing the environmental impact as corporate social responsibility, sustainability, and reducing emissions becomes a key driver in mitigating climate change. At the heart of data science is discovering hidden patterns within data. Artificial intelligent systems are assisting us in what would otherwise be a laborious manual task, reducing what might take weeks down to just a few minutes. Neural networks are a family of machine learning techniques modelled on how the human brain learns and are being used to extract patterns from data that includes images, video, or audio. It’s an exciting time for data science, one of the fastest growing tech occupations worldwide.
Attracting data scientists and specialists from all disciplines. A LinkedIn report between 2018 and 19 saw a 56% increase in job openings in the US alone, attracting data scientists with knowledge of programming language including Python, R & SQL, as well as data mining, analysis and machine learning. There’s no better time to become a part of the change that is yet to come. It has been said that data is the new oil, but unlike fossil fuels, data is only set to expand exponentially as a resource. However, like oil, data in its unrefined form is of little value and therein lies an almost limitless potential for data science to fuel change and shape the world of tomorrow.

Welcome to the introductory course in applied data science. You have joined the course because you are interested in learning more about data science, and some of the fundamentals of data science.

Before we get started, let’s first consider what data science involves and why it has become increasingly popular and fundamental to our big data society.

Watch the video and then look at our first task.

Your task

Having watched the video what are your thoughts on data science and its practical application to our everyday lives?
Please share your thoughts on this with your fellow learners in the comments.
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Applied Data Science

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