What you need to know about data science, machine learning and AI skills to progress your career

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Data science, machine learning and artificial intelligence (AI) have seen many developments. There is a corresponding demand for skilled professionals able to dive deep into vast data sets and extract actionable insights to help companies generate millions of dollars in revenue. 

But there are simply too few data scientists and related specialists to keep up with the growing demand. This shouldn’t come as much of a surprise, considering the rapid growth in technology and its applications. 

If you want to take advantage of this increasing demand and have an interest in data science, AI or machine learning, now’s the time to upskill into these spaces. But first, there are a few things to know.

1. The differences between data science, machine learning and AI

There is much talk about data science, machine learning and AI, but there are just as many misconceptions. Broadly, data science is concerned with collecting and curating mass data for analysis.

Data science extracts insights from big data sets to help businesses gain unique knowledge, recognise patterns in behaviour, design effective algorithms and better protect sensitive data. Techniques from mathematics, statistics, machine learning, computer programming, data engineering, cloud computing and other fields are used.

Machine learning enables algorithms to learn and improve from the data provided, gather insights and make future predictions without explicit programming. Machine learning is a subset of AI that focuses on a small range of activities.

Artificial intelligence uses machines to simulate the human brain’s logical reasoning and learning to find optimal solutions. AI is a broad term that includes applications ranging from robotics to text analytics. It implements data in machines for understanding future data sets.

2. Practical applications of data science, machine learning and AI

As businesses continue to grow, they are constantly under pressure to manage volumes and maximise efficiencies to increase profitability. AI and machine learning help efficiency by optimising operations and reducing mundane tasks that slow down business processes.

AI enables companies to monitor customer behaviour on their websites and social media platforms, personalise their response and provide enhanced searching, recommendations and workflows. Conversational interfaces such as chatbots and voice assistants can manage incoming traffic enquiries and offer solutions through automation. 

Importantly, companies can now predict customer behaviour and anticipate their need for products and services. Machine learning can be used to protect operations, predict vulnerabilities in software, and mitigate the risk of cyber-attacks.

3. Growth in the demand for data science, machine learning and AI

The advancement of technology has led to unfounded fears of wide-scale job losses. What has actually happened is the replacement of mundane jobs involving repetitive actions with skilled operational positions that lead to more employment. 

AI technology has been pivotal in this transformation, which requires highly skilled professionals to develop and maintain complex systems. Data science and machine learning skills are also needed to fill the demand. The efficiencies generated by these disciplines result in a more highly skilled workforce and further job creation. 

The World Economic Forum (WEF) estimates in The Future of Jobs Report 2020 that AI will displace 85 million jobs and create 97 million new positions across 26 countries by 2025. The advances will create benefits for businesses and individuals, allowing them to be more creative, strategic and entrepreneurial. The WEF says that Covid-19 has accelerated the transition “into the age of the fourth Industrial Revolution”.

4. Businesses are poised to focus more on machine learning and AI

Although there is still much work to be done, self-driving cars are a great example of how AI is set to revolutionise transport. Already, AI is widely used in the transportation industry for train scheduling and helping Uber navigate driver routes. It is only a matter of time before AI is employed widely on the roads to fill the need for heavy goods vehicle drivers in certain countries. 

Big companies are always searching for ways to drive innovation. Google, IBM, Microsoft, Accenture and Amazon, among many others, constantly look for professionals in AI and machine learning. 

Financial services companies and other specialist financial concerns worldwide, use predictive analysis every day, ranging from the lightning-quick execution of stock trades and creation of accurate forecasts, to identifying key trends and other data analysis applications.

Upskilling in data science, machine learning or AI provides valuable skill sets which will help you stay ahead of the curve and prepare for future changes that will likely affect almost every industry.

5. The outlook for machine learning technology and AI

The strides made in machine learning technology and AI have increased job prospects. A report published in June 2021 by Cognizant, a Fortune 500 US digital business and technology company, says there is much scope in APAC’s digital transformation. The report, Getting Ahead With AI: How APAC Companies Replicate Success by Remaining Focused, singles out Singapore as having the highest number of AI staff as a percentage of total employees in OPAC, while Australia and China have the fewest. 

“Many businesses are in the early stages, and due to the disparity in AI adoption and related technologies, a one-size-fits-all approach is not appropriate,” the report says. “Leaders and non-leaders should prioritise these steps based on their state of adoption and their vision for the future. As they progress, opportunities come with some major hurdles, especially talent shortages, data management and regulations.”

6. How much can I expect to get paid?

Pay is a fundamental driver to those contemplating a career in data science. All positions pay well, but there is naturally a long ladder from bottom to top depending on the specialisation, experience, and location of the hiring company. A quick search on the internet will show you how wide-ranging the pay scale and benefits are. 

According to internet job recruitment agency Indeed, the average base pay in Sydney for a data scientist and machine learning engineer is around A$133,000 per year. It is best to research pay based on where you choose to work and the type of company before making conclusions. What is apparent, though, is that there has never been a better time to start training for a career in data science, machine learning or AI.

Keen to make the switch to data science, machine learning or AI? The Institute of Data full-time and part-time programs will help you achieve your career goals and connect you with thousands of industry partners seeking professionals with skills in data science. Talk to a career consultant now.

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