Of the many developments that have come out of the Fourth Industrial Revolution, the fields of Machine Learning (ML) and Artificial Intelligence (AI) have emerged as one of the most outstanding enablers to exponential business growth. Statista reports that the global machine learning industry owned a market share of 88.71% in 2021 and is valued at over USD 16 billion by Business Wire, and pundits are predicting this as the Future of Work.
The world of self-driving cars, smart virtual assistants, and hyper-personalised online experiences have leapt from sci-fi to reality, and the implementation of these digital tools is translating to big business advantages, leading to more investments.
The edge of machine learning
Google provides recommendations in your search bar as you type. Spotify draws out a regular set of songs based on your listening history. Amazon is suggesting deals and discounts, eliminating 915,000 tons of packaging while they’re at it. Regardless of its application, machine learning (ML) has been successfully applied to not only automate business workflows and processes, but also heighten contribution to consumer needs. Its seamless integration into existing business models makes it almost impossible for anyone to reject its introduction, and the rapid development of technologies means that it has never been easier to learn or implement. Some of these include:
- Amazon Web Services, which allows important business-critical applications to easily and affordably crunch and store vast amounts of data.
- Google Tensor Flow, which allows data scientists and engineers to easily browse and use updated, sharp algorithms from the open-source libraries.
- Accelerated hardware and cloud developments offering ML management at greater speed but less cost.
- Growing integration between ML and applications that tap into Natural Language Processing, Image Recognition, etc.
After a stagnant period of growth, this burgeoning scenario of ML use for organisations is incredibly welcoming.
Impact across industries
The ubiquitous functionality of ML is also a reason for its rapid adoption. While commercial-centric organisations tend to feature more applications, ML has also driven improvements in these other fields:
- Healthcare: Reliable and accurate wearable devices have provided healthcare professionals with deeper insight into patient conditions for more accurate diagnosis and prognosis.
- Logistics: Optimising carriers and informatics to achieve efficient and high-quality deliveries for both workers and recipients.
- Financial Services: Crunch a wealth of data to analyse and project analysis, and within the organisation, support cybersurveillance and client security, and mitigate fraud scenarios.
- Government: Help map and predict future scenarios to counter anything from terrorism and civic operations to maintenance of public infrastructure.
From enabling perpetual operations to reducing operational costs, the use scenarios of ML are extensive. With such breadth, the role of ML in its application offers an optimistic career future for trained professionals, especially as old systems become obsolete.
Why python for data analytics?
Supporting this evolution is the role of Big Data and its science, an expertise that is creating generational change in careers with the emergence of roles such as data scientists, analysts, programmers and architects. Behind their work are two programming languages: Python and R - and though both have similar capabilities, Python has pulled ahead strongly as the preferred choice for most as it can be used by both data analysts and data scientists.
Python’s popularity comes from its many advantages, which is why it’s internationally recognised when dealing with work on data. When considering work in analytics, the programming language is fundamental to almost all roles in this industry - making it a valuable skill asset to learn. Some of the reasons why analytics is best handled by Python include:
- It’s easy to learn and read: For anyone who is looking to make a career switch in to the tech industry as a data analyst or scientist, Python is a great place to start. Python is a high-level language designed with ease of use in mind, and is easier to read than a low-level one. At times, it’s just like reading English. It has a simple syntax and clear vocabulary unlike complex languages like C, C++, and Java, and this makes Python great for collaborative work as well.
- There’s a large international community: The popularity of Python and its open-source nature means that it’s easy to find support no matter which level of a programmer you are or where you’re at. It even emerged as the third most popular programming language in a 2021 survey, outranking SQL. From free online tutorials to numerous training programmes in the market, it’s incredibly easy to pick up the language at your preferred pace and platform - and always with ready help at hand.
- It’s incredibly scalable: And the world's largest companies run on it. Python’s simplicity belies its potential. The enthusiasm from the community has led to extensive shared libraries with the latest and most state-of-the-art algorithms - and all free to reference and use. From Keras and TensorFlow for deep machine learning, PyTorch for natural language processing applications and Pandas for general-purpose data analysis, the flexibility of modules drives the language’s breadth of usage, and slots easily into any business function. As an independent programme, it also boasts great compatibility, working with Linux, Windows and macOS.
- It helps with visualisations: The language is extremely inclusive and includes the ability to produce fast graphical visualisations. Non-Python natives can easily get a good sense of the data with these functions.
- Career Opportunities: Across different industries and sectors, there is a sharp rise in the demand for data science-related skills. A report by HR screening and talent management platform Devskiller on top IT skills showed a 295% increase in the number of data science-related tasks recruiters were setting for candidates during the interview process in 2021. Among the various programming language and skills, Python has emerged as the most in-demand skill for data scientists, meaning that knowing python programming will be more than enough for you to find career opportunities as a data scientist or explore roles elsewhere in the computer science realm. Having been around and relevant for thirty years, Python continues to be applicable and useful for new jobs and careers. All in all, knowing Python will give you a competitive advantage especially in today's Industry 4.0 era where many career paths and occupations seem to be in question.
In the new data world, the demand for professionals with Python skills has never been higher. With interest and implementation yet at its peak, those who can contribute to Python analysis will become valued members in their respective organisation. Whether one is looking to upgrade business workflows, expand one’s scope or shift gears into another, an education in Python analytics will open many doors.
Discover the world of Python Programme and Data Analytics at SMU Academy.
