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The Top Big Data And Data Science Jobs in 2020

As with most tech, big data and data science are constantly changing and evolving. As the datasets get bigger and the demands get larger so too do the tools, processing power and the expertise needed to crunch the numbers. In this article, we will take a look at the current state of big data and try to predict some of the employment trends, challenges and opportunities ahead. With growth in both the technology and the demand (and as data science diversifies even more) specialisation is going to be key when it comes to seeking data science employment opportunities.

What is big data?

Generally speaking, big data can be defined as a large amount of data that can be algorithmically analysed to look for trends, associations or patterns of behaviour and interactions either in the real world or online. That’s not a strict definition and you could argue that size doesn’t really define big data as much as the actual quality of the data, but it is a new phrase, and its definition is likely to be fluid as we go forwards. Data analytics is important across so many sectors, not just in business, but in how our healthcare systems and educational institutes function; it even plays a huge part in what we watch on TV, the music we listen to and the information we receive from the myriad of devices we interact with on a daily basis.

Data science employment trends in 2020

Industry specialization

Back in the day, a data scientist may have only have needed to know a few SQL queries allied to some old school maths, statistics and data analysis skills to get by in what would have been fairly universal roles. These days almost every industry has a vested interest and an applied need for data scientists with specific industry experience; not only are the industries varied (healthcare, entertainment, and streaming media, government, retail and so on), but also the types of data being used are increasingly varied. Big data is not just about size of the dataset, its also about the type of data and increasingly we are seeing not just fixed but also real-time data and fast data (particularly in logistics and transport or airline data, for example). Increasingly employers are looking for staff who are specialists in defined data and industry types. Specialisation might be a significant advantage for more competitive data science job searching in 2020.

When the robots take our jobs

It’s not just a dystopian vision of the future, where AI starts to make all the smart decisions for us because we simply cannot compete with their speed, efficiency, and processing power but with more and more data tasks being undertaken by learning machines we need to think about up-skilling to ensure we are at the bleeding edge of machine learning skills in our data science careers. Gartner predicted that by 2020 we could expect to see more than 40% of big data analysis to be performed automatically. The more experience a data science has in working with these automatic data analysis processes and software systems then the more employable they’ll become.

Become business smart as well as data smart

Data analysis is going to get big. The data is becoming more complex, the tools are becoming more sophisticated and the demand is increasing exponentially. As data science becomes increasingly important to the business world, it will pay dividends for the data analysts, scientists and specialists to up-skill themselves to enable them to communicate at all levels within a business environment, become part of the business’ critical decision making and understand the value of data science and analysis to the bottom line of the organisation. The more involved a data science professional can be in the day to day management of a business, the more valuable an employee they will become.

The Top Data Science Jobs in 2020

As organisations seek to become more competitive, more agile and more profitable they are increasing their investments in big data and the data scientists needed to make meaning from the numbers. Last year, online HR behemoth. LinkedIn reported massive shortages in skilled data professionals in almost every US city; this trend is set to continue with demand for these skill sets only looking to rise.

1. Data Scientist

Data scientists are intrinsic to an organisation with large amounts of big data that require analysing and interpreting. There are lots of opportunities for data scientists in a variety of industries to work with existing IT teams to design and deploy statistical models on all sorts of data types. Key skills include vector support machine experience, decision trees, regression analysis, and clustering. A Masters or Ph.D. in data science or statistics related fields would be very useful. You can find similar statistics masters programs online from reputable schools that are easy to work around your day to day life.

2. Data Engineer

Similar to a data scientist, a data engineer is far more involved with datasets that require manipulation and analysis. They get their hands dirty in prototyping code, designing new ways to make data more accessible and working on the analytics dashboards that end users might use to read or report on the data. Employers like to see a lot of hands-on experience (often 3-5 years at least) for engineer roles and experience in as many related programming languages as possible would be advantageous (Storm, Hive, Python, Java, etc).

3. Data Analyst

A data analyst straddles the line between the tech and the end-user, responsible for much of the data gathering organization along with designing and deploying surveys. They will organise the gathered data and use a variety of formats to interpret and report on the results. SQL queries and databases along with data manipulation and presentation tools like Microsoft Access, Excel and Sharepoint are key skills. Desired soft skills might include excellent communication and presentation skills as they may often be tasked with presenting and interpreting data to colleagues or stakeholders in a clear, non-technical way.

4. Security Engineer

A security engineer is responsible for the security of an organisation’s IT infrastructure. New and updated hardware and software alongside ever-changing security regulations regarding data protection means a security engineer must be continuously seeking self-improvement. Firewalls, forensic security, identifying hacks and intrusions and responding appropriately are all key skills. Employers will look for a lot of experience for this business-critical role with good all-round analytical, statistical and programming (operating systems and programming languages) skills an absolute must.

5. Database Manager or Administrator

Essentially a database related project management role; a database manager is required to lead database projects from initial business data requests, through data gathering, analysis, and use. They will be instrumental in data feed maintenance alongside source and storage evaluation. Experience in leading database projects would be useful alongside appropriate skills in various database related software such as Oracle and SQL.

6. Data Architect

Organisations with lots of stored data need somebody (or a team) to make sure that the relational databases that hold that data is appropriately maintained and organised. They need to be highly skilled in relational database related software, tools and programming languages such as XML and SQL. Architectural roles will be increasingly in demand as the amount of data that needs taking care of is growing at such a fast rate and this job could become particularly competitive.

7. Technical Recruitment

As database-related job roles become increasingly important to companies, those responsible for making recruitment decisions will need to specialize in finding appropriately skilled and educated people to fill those roles. Whether in-house or as part of a specialist recruitment agency, this job will need a core understanding of what skills will hold the most value for an organisation’s big data-related jobs.

The demand for data professionals in 2020

We can expect a huge increase in the demand for data scientists in the coming years. IBM have predicted that by 2020, we can expect to see the number of job opportunities soar by 364,000 jobs to 2,720,000 for data professionals as a whole and the annual demand for specific and new roles such as data scientists, developers, engineers, security experts and database administrators could rise by 700,000 openings.

Why is the demand so huge?

There a few key reasons why the demand for data professionals is going through the roof and they are important considerations for anybody looking to pursue or continue a career in data science- related fields. Firstly there is simply so much data. With as much a 1.7 megabytes of data being generated every second by every human on Earth by next year the need for professionals to analyse and understand all that data is critical. Secondly the skills required are unique; not only do data professionals need to be highly skilled in data science, they also need to be business savvy and have plenty of communicative soft skills too. Beyond this we will see a rise in the number of applications that can leverage the data being generated; this, in turn, will lead to more demand for skilled operatives and even new job types and roles. There has never been a better time to pursue a job in data science but it’s only going to get more crowded and more competitive in the future so a broad cross-section of skills and experience allied to the competitive advantage that only a significant, data science-related degree can offer are the key to success.

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The Top Big Data And Data Science Jobs in 2020

As with most tech, big data and data science are constantly changing and evolving. As the datasets get bigger and the demands get larger so too do the tools, processing power and the expertise needed to crunch the numbers. In this article, we will take a look at the current state of big data and try to predict some of the employment trends, challenges and opportunities ahead. With growth in both the technology and the demand (and as data science diversifies even more) specialisation is going to be key when it comes to seeking data science employment opportunities.

What is big data?

Generally speaking, big data can be defined as a large amount of data that can be algorithmically analysed to look for trends, associations or patterns of behaviour and interactions either in the real world or online. That’s not a strict definition and you could argue that size doesn’t really define big data as much as the actual quality of the data, but it is a new phrase, and its definition is likely to be fluid as we go forwards. Data analytics is important across so many sectors, not just in business, but in how our healthcare systems and educational institutes function; it even plays a huge part in what we watch on TV, the music we listen to and the information we receive from the myriad of devices we interact with on a daily basis.

Data science employment trends in 2020

Industry specialization

Back in the day, a data scientist may have only have needed to know a few SQL queries allied to some old school maths, statistics and data analysis skills to get by in what would have been fairly universal roles. These days almost every industry has a vested interest and an applied need for data scientists with specific industry experience; not only are the industries varied (healthcare, entertainment, and streaming media, government, retail and so on), but also the types of data being used are increasingly varied. Big data is not just about size of the dataset, its also about the type of data and increasingly we are seeing not just fixed but also real-time data and fast data (particularly in logistics and transport or airline data, for example). Increasingly employers are looking for staff who are specialists in defined data and industry types. Specialisation might be a significant advantage for more competitive data science job searching in 2020.

When the robots take our jobs

It’s not just a dystopian vision of the future, where AI starts to make all the smart decisions for us because we simply cannot compete with their speed, efficiency, and processing power but with more and more data tasks being undertaken by learning machines we need to think about up-skilling to ensure we are at the bleeding edge of machine learning skills in our data science careers. Gartner predicted that by 2020 we could expect to see more than 40% of big data analysis to be performed automatically. The more experience a data science has in working with these automatic data analysis processes and software systems then the more employable they’ll become.

Become business smart as well as data smart

Data analysis is going to get big. The data is becoming more complex, the tools are becoming more sophisticated and the demand is increasing exponentially. As data science becomes increasingly important to the business world, it will pay dividends for the data analysts, scientists and specialists to up-skill themselves to enable them to communicate at all levels within a business environment, become part of the business’ critical decision making and understand the value of data science and analysis to the bottom line of the organisation. The more involved a data science professional can be in the day to day management of a business, the more valuable an employee they will become.

The Top Data Science Jobs in 2020

As organisations seek to become more competitive, more agile and more profitable they are increasing their investments in big data and the data scientists needed to make meaning from the numbers. Last year, online HR behemoth. LinkedIn reported massive shortages in skilled data professionals in almost every US city; this trend is set to continue with demand for these skill sets only looking to rise.

1. Data Scientist

Data scientists are intrinsic to an organisation with large amounts of big data that require analysing and interpreting. There are lots of opportunities for data scientists in a variety of industries to work with existing IT teams to design and deploy statistical models on all sorts of data types. Key skills include vector support machine experience, decision trees, regression analysis, and clustering. A Masters or Ph.D. in data science or statistics related fields would be very useful. You can find similar statistics masters programs online from reputable schools that are easy to work around your day to day life.

2. Data Engineer

Similar to a data scientist, a data engineer is far more involved with datasets that require manipulation and analysis. They get their hands dirty in prototyping code, designing new ways to make data more accessible and working on the analytics dashboards that end users might use to read or report on the data. Employers like to see a lot of hands-on experience (often 3-5 years at least) for engineer roles and experience in as many related programming languages as possible would be advantageous (Storm, Hive, Python, Java, etc).

3. Data Analyst

A data analyst straddles the line between the tech and the end-user, responsible for much of the data gathering organization along with designing and deploying surveys. They will organise the gathered data and use a variety of formats to interpret and report on the results. SQL queries and databases along with data manipulation and presentation tools like Microsoft Access, Excel and Sharepoint are key skills. Desired soft skills might include excellent communication and presentation skills as they may often be tasked with presenting and interpreting data to colleagues or stakeholders in a clear, non-technical way.

4. Security Engineer

A security engineer is responsible for the security of an organisation’s IT infrastructure. New and updated hardware and software alongside ever-changing security regulations regarding data protection means a security engineer must be continuously seeking self-improvement. Firewalls, forensic security, identifying hacks and intrusions and responding appropriately are all key skills. Employers will look for a lot of experience for this business-critical role with good all-round analytical, statistical and programming (operating systems and programming languages) skills an absolute must.

5. Database Manager or Administrator

Essentially a database related project management role; a database manager is required to lead database projects from initial business data requests, through data gathering, analysis, and use. They will be instrumental in data feed maintenance alongside source and storage evaluation. Experience in leading database projects would be useful alongside appropriate skills in various database related software such as Oracle and SQL.

6. Data Architect

Organisations with lots of stored data need somebody (or a team) to make sure that the relational databases that hold that data is appropriately maintained and organised. They need to be highly skilled in relational database related software, tools and programming languages such as XML and SQL. Architectural roles will be increasingly in demand as the amount of data that needs taking care of is growing at such a fast rate and this job could become particularly competitive.

7. Technical Recruitment

As database-related job roles become increasingly important to companies, those responsible for making recruitment decisions will need to specialize in finding appropriately skilled and educated people to fill those roles. Whether in-house or as part of a specialist recruitment agency, this job will need a core understanding of what skills will hold the most value for an organisation’s big data-related jobs.

The demand for data professionals in 2020

We can expect a huge increase in the demand for data scientists in the coming years. IBM have predicted that by 2020, we can expect to see the number of job opportunities soar by 364,000 jobs to 2,720,000 for data professionals as a whole and the annual demand for specific and new roles such as data scientists, developers, engineers, security experts and database administrators could rise by 700,000 openings.

Why is the demand so huge?

There a few key reasons why the demand for data professionals is going through the roof and they are important considerations for anybody looking to pursue or continue a career in data science- related fields. Firstly there is simply so much data. With as much a 1.7 megabytes of data being generated every second by every human on Earth by next year the need for professionals to analyse and understand all that data is critical. Secondly the skills required are unique; not only do data professionals need to be highly skilled in data science, they also need to be business savvy and have plenty of communicative soft skills too. Beyond this we will see a rise in the number of applications that can leverage the data being generated; this, in turn, will lead to more demand for skilled operatives and even new job types and roles. There has never been a better time to pursue a job in data science but it’s only going to get more crowded and more competitive in the future so a broad cross-section of skills and experience allied to the competitive advantage that only a significant, data science-related degree can offer are the key to success.

Subscribe to our newsletter!
One a month, no spam, honest

Now on air
Coming up
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The Top Big Data And Data Science Jobs in 2020

As with most tech, big data and data science are constantly changing and evolving. As the datasets get bigger and the demands get larger so too do the tools, processing power and the expertise needed to crunch the numbers. In this article, we will take a look at the current state of big data and try to predict some of the employment trends, challenges and opportunities ahead. With growth in both the technology and the demand (and as data science diversifies even more) specialisation is going to be key when it comes to seeking data science employment opportunities.

What is big data?

Generally speaking, big data can be defined as a large amount of data that can be algorithmically analysed to look for trends, associations or patterns of behaviour and interactions either in the real world or online. That’s not a strict definition and you could argue that size doesn’t really define big data as much as the actual quality of the data, but it is a new phrase, and its definition is likely to be fluid as we go forwards. Data analytics is important across so many sectors, not just in business, but in how our healthcare systems and educational institutes function; it even plays a huge part in what we watch on TV, the music we listen to and the information we receive from the myriad of devices we interact with on a daily basis.

Data science employment trends in 2020

Industry specialization

Back in the day, a data scientist may have only have needed to know a few SQL queries allied to some old school maths, statistics and data analysis skills to get by in what would have been fairly universal roles. These days almost every industry has a vested interest and an applied need for data scientists with specific industry experience; not only are the industries varied (healthcare, entertainment, and streaming media, government, retail and so on), but also the types of data being used are increasingly varied. Big data is not just about size of the dataset, its also about the type of data and increasingly we are seeing not just fixed but also real-time data and fast data (particularly in logistics and transport or airline data, for example). Increasingly employers are looking for staff who are specialists in defined data and industry types. Specialisation might be a significant advantage for more competitive data science job searching in 2020.

When the robots take our jobs

It’s not just a dystopian vision of the future, where AI starts to make all the smart decisions for us because we simply cannot compete with their speed, efficiency, and processing power but with more and more data tasks being undertaken by learning machines we need to think about up-skilling to ensure we are at the bleeding edge of machine learning skills in our data science careers. Gartner predicted that by 2020 we could expect to see more than 40% of big data analysis to be performed automatically. The more experience a data science has in working with these automatic data analysis processes and software systems then the more employable they’ll become.

Become business smart as well as data smart

Data analysis is going to get big. The data is becoming more complex, the tools are becoming more sophisticated and the demand is increasing exponentially. As data science becomes increasingly important to the business world, it will pay dividends for the data analysts, scientists and specialists to up-skill themselves to enable them to communicate at all levels within a business environment, become part of the business’ critical decision making and understand the value of data science and analysis to the bottom line of the organisation. The more involved a data science professional can be in the day to day management of a business, the more valuable an employee they will become.

The Top Data Science Jobs in 2020

As organisations seek to become more competitive, more agile and more profitable they are increasing their investments in big data and the data scientists needed to make meaning from the numbers. Last year, online HR behemoth. LinkedIn reported massive shortages in skilled data professionals in almost every US city; this trend is set to continue with demand for these skill sets only looking to rise.

1. Data Scientist

Data scientists are intrinsic to an organisation with large amounts of big data that require analysing and interpreting. There are lots of opportunities for data scientists in a variety of industries to work with existing IT teams to design and deploy statistical models on all sorts of data types. Key skills include vector support machine experience, decision trees, regression analysis, and clustering. A Masters or Ph.D. in data science or statistics related fields would be very useful. You can find similar statistics masters programs online from reputable schools that are easy to work around your day to day life.

2. Data Engineer

Similar to a data scientist, a data engineer is far more involved with datasets that require manipulation and analysis. They get their hands dirty in prototyping code, designing new ways to make data more accessible and working on the analytics dashboards that end users might use to read or report on the data. Employers like to see a lot of hands-on experience (often 3-5 years at least) for engineer roles and experience in as many related programming languages as possible would be advantageous (Storm, Hive, Python, Java, etc).

3. Data Analyst

A data analyst straddles the line between the tech and the end-user, responsible for much of the data gathering organization along with designing and deploying surveys. They will organise the gathered data and use a variety of formats to interpret and report on the results. SQL queries and databases along with data manipulation and presentation tools like Microsoft Access, Excel and Sharepoint are key skills. Desired soft skills might include excellent communication and presentation skills as they may often be tasked with presenting and interpreting data to colleagues or stakeholders in a clear, non-technical way.

4. Security Engineer

A security engineer is responsible for the security of an organisation’s IT infrastructure. New and updated hardware and software alongside ever-changing security regulations regarding data protection means a security engineer must be continuously seeking self-improvement. Firewalls, forensic security, identifying hacks and intrusions and responding appropriately are all key skills. Employers will look for a lot of experience for this business-critical role with good all-round analytical, statistical and programming (operating systems and programming languages) skills an absolute must.

5. Database Manager or Administrator

Essentially a database related project management role; a database manager is required to lead database projects from initial business data requests, through data gathering, analysis, and use. They will be instrumental in data feed maintenance alongside source and storage evaluation. Experience in leading database projects would be useful alongside appropriate skills in various database related software such as Oracle and SQL.

6. Data Architect

Organisations with lots of stored data need somebody (or a team) to make sure that the relational databases that hold that data is appropriately maintained and organised. They need to be highly skilled in relational database related software, tools and programming languages such as XML and SQL. Architectural roles will be increasingly in demand as the amount of data that needs taking care of is growing at such a fast rate and this job could become particularly competitive.

7. Technical Recruitment

As database-related job roles become increasingly important to companies, those responsible for making recruitment decisions will need to specialize in finding appropriately skilled and educated people to fill those roles. Whether in-house or as part of a specialist recruitment agency, this job will need a core understanding of what skills will hold the most value for an organisation’s big data-related jobs.

The demand for data professionals in 2020

We can expect a huge increase in the demand for data scientists in the coming years. IBM have predicted that by 2020, we can expect to see the number of job opportunities soar by 364,000 jobs to 2,720,000 for data professionals as a whole and the annual demand for specific and new roles such as data scientists, developers, engineers, security experts and database administrators could rise by 700,000 openings.

Why is the demand so huge?

There a few key reasons why the demand for data professionals is going through the roof and they are important considerations for anybody looking to pursue or continue a career in data science- related fields. Firstly there is simply so much data. With as much a 1.7 megabytes of data being generated every second by every human on Earth by next year the need for professionals to analyse and understand all that data is critical. Secondly the skills required are unique; not only do data professionals need to be highly skilled in data science, they also need to be business savvy and have plenty of communicative soft skills too. Beyond this we will see a rise in the number of applications that can leverage the data being generated; this, in turn, will lead to more demand for skilled operatives and even new job types and roles. There has never been a better time to pursue a job in data science but it’s only going to get more crowded and more competitive in the future so a broad cross-section of skills and experience allied to the competitive advantage that only a significant, data science-related degree can offer are the key to success.

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The Top Big Data And Data Science Jobs in 2020

As with most tech, big data and data science are constantly changing and evolving. As the datasets get bigger and the demands get larger so too do the tools, processing power and the expertise needed to crunch the numbers. In this article, we will take a look at the current state of big data and try to predict some of the employment trends, challenges and opportunities ahead. With growth in both the technology and the demand (and as data science diversifies even more) specialisation is going to be key when it comes to seeking data science employment opportunities.

What is big data?

Generally speaking, big data can be defined as a large amount of data that can be algorithmically analysed to look for trends, associations or patterns of behaviour and interactions either in the real world or online. That’s not a strict definition and you could argue that size doesn’t really define big data as much as the actual quality of the data, but it is a new phrase, and its definition is likely to be fluid as we go forwards. Data analytics is important across so many sectors, not just in business, but in how our healthcare systems and educational institutes function; it even plays a huge part in what we watch on TV, the music we listen to and the information we receive from the myriad of devices we interact with on a daily basis.

Data science employment trends in 2020

Industry specialization

Back in the day, a data scientist may have only have needed to know a few SQL queries allied to some old school maths, statistics and data analysis skills to get by in what would have been fairly universal roles. These days almost every industry has a vested interest and an applied need for data scientists with specific industry experience; not only are the industries varied (healthcare, entertainment, and streaming media, government, retail and so on), but also the types of data being used are increasingly varied. Big data is not just about size of the dataset, its also about the type of data and increasingly we are seeing not just fixed but also real-time data and fast data (particularly in logistics and transport or airline data, for example). Increasingly employers are looking for staff who are specialists in defined data and industry types. Specialisation might be a significant advantage for more competitive data science job searching in 2020.

When the robots take our jobs

It’s not just a dystopian vision of the future, where AI starts to make all the smart decisions for us because we simply cannot compete with their speed, efficiency, and processing power but with more and more data tasks being undertaken by learning machines we need to think about up-skilling to ensure we are at the bleeding edge of machine learning skills in our data science careers. Gartner predicted that by 2020 we could expect to see more than 40% of big data analysis to be performed automatically. The more experience a data science has in working with these automatic data analysis processes and software systems then the more employable they’ll become.

Become business smart as well as data smart

Data analysis is going to get big. The data is becoming more complex, the tools are becoming more sophisticated and the demand is increasing exponentially. As data science becomes increasingly important to the business world, it will pay dividends for the data analysts, scientists and specialists to up-skill themselves to enable them to communicate at all levels within a business environment, become part of the business’ critical decision making and understand the value of data science and analysis to the bottom line of the organisation. The more involved a data science professional can be in the day to day management of a business, the more valuable an employee they will become.

The Top Data Science Jobs in 2020

As organisations seek to become more competitive, more agile and more profitable they are increasing their investments in big data and the data scientists needed to make meaning from the numbers. Last year, online HR behemoth. LinkedIn reported massive shortages in skilled data professionals in almost every US city; this trend is set to continue with demand for these skill sets only looking to rise.

1. Data Scientist

Data scientists are intrinsic to an organisation with large amounts of big data that require analysing and interpreting. There are lots of opportunities for data scientists in a variety of industries to work with existing IT teams to design and deploy statistical models on all sorts of data types. Key skills include vector support machine experience, decision trees, regression analysis, and clustering. A Masters or Ph.D. in data science or statistics related fields would be very useful. You can find similar statistics masters programs online from reputable schools that are easy to work around your day to day life.

2. Data Engineer

Similar to a data scientist, a data engineer is far more involved with datasets that require manipulation and analysis. They get their hands dirty in prototyping code, designing new ways to make data more accessible and working on the analytics dashboards that end users might use to read or report on the data. Employers like to see a lot of hands-on experience (often 3-5 years at least) for engineer roles and experience in as many related programming languages as possible would be advantageous (Storm, Hive, Python, Java, etc).

3. Data Analyst

A data analyst straddles the line between the tech and the end-user, responsible for much of the data gathering organization along with designing and deploying surveys. They will organise the gathered data and use a variety of formats to interpret and report on the results. SQL queries and databases along with data manipulation and presentation tools like Microsoft Access, Excel and Sharepoint are key skills. Desired soft skills might include excellent communication and presentation skills as they may often be tasked with presenting and interpreting data to colleagues or stakeholders in a clear, non-technical way.

4. Security Engineer

A security engineer is responsible for the security of an organisation’s IT infrastructure. New and updated hardware and software alongside ever-changing security regulations regarding data protection means a security engineer must be continuously seeking self-improvement. Firewalls, forensic security, identifying hacks and intrusions and responding appropriately are all key skills. Employers will look for a lot of experience for this business-critical role with good all-round analytical, statistical and programming (operating systems and programming languages) skills an absolute must.

5. Database Manager or Administrator

Essentially a database related project management role; a database manager is required to lead database projects from initial business data requests, through data gathering, analysis, and use. They will be instrumental in data feed maintenance alongside source and storage evaluation. Experience in leading database projects would be useful alongside appropriate skills in various database related software such as Oracle and SQL.

6. Data Architect

Organisations with lots of stored data need somebody (or a team) to make sure that the relational databases that hold that data is appropriately maintained and organised. They need to be highly skilled in relational database related software, tools and programming languages such as XML and SQL. Architectural roles will be increasingly in demand as the amount of data that needs taking care of is growing at such a fast rate and this job could become particularly competitive.

7. Technical Recruitment

As database-related job roles become increasingly important to companies, those responsible for making recruitment decisions will need to specialize in finding appropriately skilled and educated people to fill those roles. Whether in-house or as part of a specialist recruitment agency, this job will need a core understanding of what skills will hold the most value for an organisation’s big data-related jobs.

The demand for data professionals in 2020

We can expect a huge increase in the demand for data scientists in the coming years. IBM have predicted that by 2020, we can expect to see the number of job opportunities soar by 364,000 jobs to 2,720,000 for data professionals as a whole and the annual demand for specific and new roles such as data scientists, developers, engineers, security experts and database administrators could rise by 700,000 openings.

Why is the demand so huge?

There a few key reasons why the demand for data professionals is going through the roof and they are important considerations for anybody looking to pursue or continue a career in data science- related fields. Firstly there is simply so much data. With as much a 1.7 megabytes of data being generated every second by every human on Earth by next year the need for professionals to analyse and understand all that data is critical. Secondly the skills required are unique; not only do data professionals need to be highly skilled in data science, they also need to be business savvy and have plenty of communicative soft skills too. Beyond this we will see a rise in the number of applications that can leverage the data being generated; this, in turn, will lead to more demand for skilled operatives and even new job types and roles. There has never been a better time to pursue a job in data science but it’s only going to get more crowded and more competitive in the future so a broad cross-section of skills and experience allied to the competitive advantage that only a significant, data science-related degree can offer are the key to success.

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