Some of the biggest companies around the globe are driven by data science. For example, Amazon, Google and Facebook use data science to create algorithms designed to maximize their profits and improve customer satisfaction. Organizations around the globe see it as a key part of their business, as do many non-profit organizations within public health and the charitable sector.
Data, how we use it, and how it is interpreted, feeds into many aspects of our daily lives, and the industry is expanding and evolving every day. This means that a career in this particularly fast-growing field will open up job opportunities for many years to come.
What is data science?
Data science itself involves applying advanced analytics techniques combined with scientific principles in order to extract information from data to drive business decision-making and strategic planning.
It encompasses a wide variety of disciplines, such as data engineering, predictive analytics, data preparation, data visualization, statistics and software programming, and these are usually performed by skilled data scientists.
It is hugely important to businesses as it enables them to gather information that helps drive different aspects of the organization and its potential growth and improvement. For example, it can assist with strategies to prevent cyberattacks, analyze information to help target advertising and can help optimize supply chain management, distribution networks and product inventories.
The history of data science
The term was coined in the early 1960s to describe the work that would support the interpretation and understanding of the huge amount of data that was being collected at the time.
It began with statistics and over the years has grown to include concepts such as machine learning and artificial intelligence.
As more data becomes available, businesses collect it in larger and larger amounts. In the early years, information was gathered about shopping behaviors and trends in order to make business decisions. It has now evolved and is applied to other fields such as engineering, medicine and social sciences.
The benefits of data collection and analysis
Potential benefits to businesses from the intelligent use of the data they collect can include more efficient operations, sales growth, faster time to market and improved customer satisfaction. Organizations that invest in it can then factor in data-based quantifiable evidence into every decision that they make.
It is also utilized in healthcare in order to diagnose medical conditions and plan treatment, as well as government agencies, public policy organizations and sports teams. It can also be used for social good, by focusing the power of new technologies on the problems that people who lack the market power of private firms face.
For example, some non-profit organizations provide researchers with open data sets that relate to subjects such as problems within health care infrastructure, air quality and school enrolment. The International Aid Transparency Initiative, established in 2016, promotes the use of data resources to help developing countries and its members include development and humanitarian organizations, private businesses and governments
According to recent surveys, around 90% of non-profit organizations collected data about their operations, but only 5% said their decisions were always data driven, because many do not have the time, resources or expertise to properly benefit from the information they gather. Many data science companies now offer their services to those organizations whose work benefits the public on both a local and global scale.
The influence of algorithms
Algorithms play a huge part in our everyday lives, whether we’re using search engines, navigating our way to a park or designing a computer game. Simply put, they are a series of instructions that are followed to do something or to solve a problem. One way to understand the concept is to compare an algorithm to a recipe for making a cake. They are in essence mini instruction manuals telling computers how to complete a task.
Computers take input and apply each step of the algorithm to the information to generate an output. When used in a search engine, it takes a query as an input, searches its database for information relevant to the words within that query, then outputs the result.
They are used throughout all areas of computing and information technology and can process and manipulate data and perform actions in a variety of ways.
What is coding
Another element of data science is coding, which is the process of transforming information or observations that has been collected into a set of cohesive strategies by summarizing and presenting it to provide a systematic account of findings. Data can be gathered from a variety of sources, such as social media messages, face-to-face conversations, historical documents and newspaper articles. In essence, coding links theory and data to transform information into a form understandable by computer software.
The breadth and scope of its influence and potential growth as technology improves, means that a qualification in this particular field will be a good investment for any student. There is currently a shortage of 3,500 software engineers across Canada alone. A global shortage of experts within the discipline means job prospects are healthy and will continue to expand.
Studies show there was a 22% growth in computer engineering posts between 2013 to 2018 and that is set to increase
According to the US Bureau of Labor Statistics, the industries with the highest level of employment of data scientists include Management of Companies and Enterprises, Scientific Research and Development Services, some Credit Intermediation and Related Activities, and Computer Systems Design and Related Services. In Canada, information is available about the job prospects within the industry across each province and territory from the Job Bank of Canada
Relevant skills for data scientists
Data scientists need to combine mathematical knowledge, technological skills and commercial awareness. An understanding of statistics is key, but other ideas such as linear algebra lay the foundation for many machine learning algorithms and inferential techniques.
The ability to navigate your way through technological difficulties is vital, and you have to be able to deal with complex tools and algorithms as well as being able to develop solutions in tools such as SQL and Python, and use various languages, including Java and Scala.
Data scientists create AI tools and technologies for use in a variety of applications, then gather the data, develop analytical models and then train, test and run the models against the data. On occasion, they can also be called on to create data visualizations, dashboards and reports to illustrate their findings.
They must have a combination of skills to call on, including data preparation, data mining, machine learning and statistical analysis.
Important soft skills
As well as the right technical skills and product knowledge, successful data scientists also need to call on their more personal traits too. For instance, communicating effectively is crucial in areas such as explaining findings to business users and decision-makers in both technical and non-technical ways.
Curiosity inspires data scientists to seek answers to problems, going past initial assumptions to investigate underlying causes and develop strategies and insights to overcome them. Developing an understanding of the organization and industry in which they work also means that they can interpret their findings effectively.
Critical thinking enables a data scientist to objectively analyze specific areas and problems then determine how their conclusions can be used to help their company achieve its aims. In addition to this, adaptability is a crucial soft skill to develop partly because methods and technology are constantly improving and developing, and also due to the shifts in business trends, both long and short term.
Often the role will mean you are part of a team. It’s key to recognize the importance of working with others in order to collaborate effectively and listen to the views and findings of others to present information in a cohesive way to other departments and partners.
Online Master’s degree in Computer Science
A Master’s degree in Computer Science can open up the entire world to graduates. In Canada, more and more employers are adding senior level staff to their growing teams. Embarking on a Canada Master’s in Computer Science at Wilfred Laurier University means you will be joining the only 100% online Master of Computer Science degree program in the country.
Using the MyLearningSpace online portal, it is offered as both part-time and accelerated models, and is completed in six terms or two years for part-time, or within four terms or 16 months for fast-track students.
It is designed for entry and mid-level computer science professionals with an undergraduate degree in computer science, applied computing, computer engineering, information systems, mathematics or a related field.
Challenges to be aware of
Data science as a discipline is challenging by its very nature due to the advanced nature of the analytics it utilizes. With the amount of different data being examined alongside the often complex nature of the information being extracted, the time taken to complete a particular project can become longer and longer. Jobs often involve using an array of structured, unstructured and semi-structured data and then attempting to make sense of what is there.
Another factor is how to eliminate bias in data sets and analytics applications by being aware of potential issues with the underlying data and those which are unconsciously built into algorithms and predictive models. These can alter results if they aren’t identified and addressed, leading to flawed findings which can lead to ineffective business decisions.
Keeping up to date
The world of data science is constantly evolving with the introduction of new technologies and different techniques, so ensuring you’re aware of the latest trends, both while studying as well as working is vital to remaining effective and competitive.
There are various ways to keep on top of what’s going on in the industry. One of the easiest is to read relevant publications and websites which will introduce you to new material on a regular basis in an easy-to-read and engaging way.
YouTube can also be very useful with a great many scientific channels available to subscribe to, and there is a constant stream of well-established podcasts as well as new launches to listen in on as well. Meetups are another great way to enhance your knowledge and keep abreast of what’s going on, giving you opportunities to meet other professionals as well as industry experts. Simply identify groups on the app that run events you can get to, then discipline yourself to attend the ones relevant to you regularly. You can also subscribe to webinars if it’s difficult to get to meetings you want to go to in person.
Books are an excellent way to understand technologies and concepts, and many can be accessed on the internet at relatively little cost. Reading scientific papers can also be very useful, especially if you are hoping to understand current shifts, changes and analyses in the industry.
Social media is another excellent way to network and gain knowledge of the world of data science, so following influential people on Twitter and LinkedIn as well as joining relevant groups is another easy and effective way to get a feel for what’s going on around the globe.
Benefits of a Computer Science Master’s for data scientists
Gaining an online Master’s degree in Computer Science will involve covering topics such as parametric-non parametric algorithms, support vector machines, kernels neural networks and clustering. You will also gain credits in machine learning, applied cryptography and iPhone programming, as well as many other areas.
This degree will inevitably assist you within the career you already have or the industry you wish to be a part of.