Its adoption accelerated, and leaders correctly predicted growth in the industry in all aspects. Overall, organisations invested more in Data Science, and there was an upswing in the Data Science jobs. While the median salaries of analytics professionals saw a slight decline at the start of the year, a rising trend was witnessed again in the recent months, which will continue to be the case in the coming year. The inefficiencies of Data Science teams from development to deployment in the real world were observed before but they became even more evident due to the pandemic. The operationalisation and scaling of Machine Learning models through structured frameworks was the talk of 2021. These processes will start getting streamlined in the coming years. The Data Science industry also realised the breadth of roles needed for these deployments. While generalists will continue to be in demand, niche roles will play an important role going forward, especially Data Engineers. Subsequently, the role of education will also evolve. It will become further formalised with more specialisation courses introduced.

Layoff has always been the first step companies take in order to cut down cost amid an economic turmoil. And this COVID-19 pandemic has made millions of people unemployed, including data scientists and analytics professionals. Keeping business sustainable is a critical priority for any company right now, and in order to keep their finances stable as well as to reinvest in automation, many of them are taking layoffs as a resort for their business continuity. In fact, according to data, this coronavirus lockdown has made a record-high unemployment rate of 27.1% in India. In another recent report, it has been noted that the new claims for unemployment benefits in the US have raised to 281,000 by March 2020, which is the highest since 2017.

I have been a data analytics professional for the past twelve years. Throughout my career, I have seen a steady spike in the use of data across the industry, be it engineering, education, healthcare or financial services. It was in 2017 when I read about the Economist article "The world's most valuable resource is no longer oil, but data" an idea which was first coined by Clive Humby, UK Mathematician and architect of Tesco's Clubcard in 2006. Many prominent personalities like Meglena Kuneva, European Consumer Commissioner, 2009 [1] later reiterated this. I could see everyone talking about the infinite potential of data and how to use it in a million ways.

Almost every industry is seeking top quality Artificial Intelligence (AI) and analytics professionals across the world. Apart from top academic institutions, industry has also been targetting scientific research labs in order to tap those who possess competencies in quantitative techniques proficient in building models and are getting them oriented to design business solutions. The AI as a service market size was valued at $1.13 billion in 2017 and is expected to be $10.88 billion by 2023, thus opening up a huge demand for AI talent pool. The AI-powered services in the form of Application Programming Interface (API) and Software Development Kit (SDK) are primarily driving the demand for AI and analytics professionals. In addition to these, startups working on path breaking ideas are also in need of smart data science professionals.

From Artificial Intelligence to the Internet of Things, AR/VR and ML, the list doesn't seem to end. Of all these, one technology that has proven to be not only disruptive but also opportunistic is Machine Learning. Cumulative investments done in the field of machine learning is expected to rise to $58 billion by the end of 2021 . Needless to state that the pace at which the industry is growing is day beyond expectations. Nearly every sector is clouted with the inception of machine learning and is seen to actively adopt the scope of machine learning within the ecosystem to enhance and upscale business operations .

With the dawn of industrialization 4.0, there has been a tremendous shift in the technologies that we use to make our life better. From Artificial Intelligence to the Internet of Things, AR/VR and ML, the list doesn't seem to end. Of all these, one technology that has proven to be not only disruptive but also opportunistic is Machine Learning. Cumulative investments done in the field of machine learning is expected to rise to $58 billion by the end of 2021. Needless to state that the pace at which the industry is growing is day beyond expectations.

If you are a senior data scientist or pro in predictive analytics, you would probably be using both R & Python, and maybe other tools like SAS, SQL etc. But, what if you are a beginner or just thinking about to start a career in data science, machine learning, and business analytics? Which one should you learn – R or Python? It has always been a topic of great debate among data scientists, researchers and analytics professionals. In this article, we will discuss R vs Python – usability, popularity index, advantages & limitations, job opportunities, and salaries. R is a statistical and visualization language which is deep and huge and mathematical.

Most people in the field of analytics can remember writing their own analytical code. Today, our Data Scientists in the MSiA program at Northwestern, can produce analytical models from regression, decision trees, support vector machines (and more) – all with more or less one simple execution. The manual step is minor. In fact, the manual step is being removed as analytics moves into automation and artificial intelligence. Career Take Away: Develop skills in many model types.

Summary: Is everyone a'data scientist'? We do seem to need some agreement about titling. Data Scientists is still the prestige title but there are some folks lobbying to take that title away. Over just the last 12 to 18 months there has been more and more written about new job titles and roles in our data science profession. It was perhaps only three or four years ago where if you worked in data science you were called a "Data Scientist".

Now is a great time to be in data analytics. We're on a crest of a wave being propelled by sophisticated data use, including machine learning and artificial intelligence, and attractive salary packages. In Australia the median salary of an analytics professional is A$130,000 – over 60 percent more than the Australian median salary. This salary pressure and skills shortage has created a market where 67 percent of hiring managers report applicants are under-skilled. Equally, specialised skill in analytics is difficult to find and retain – 94% of managers found it the same or harder to hire. It's for all these reasons, and to support analytics role in business, that IAPA recently introduced IAPA-certified via credential – so data analysts could be recognised for the skills they bring to the workplace and employers could be confident in the skills of staff and potential candidates.