/Career as a Data Scientist: Skills needed, job roles and other important details (via Qpute.com)
Career as a Data Scientist: Skills needed, job roles and other important details

Career as a Data Scientist: Skills needed, job roles and other important details (via Qpute.com)

Post Covid-19 pandemic era has changed the nature of work and the workplace which has also further evolved over the past few decades. The next decade is expected to make strides towards Innovative technologies like artificial intelligence, machine learning, and robotics that are expected to dominate the world. The Covid-19 pandemic has hastened the digitisation of organisations, changed the dynamics of the workforce, and created space for remote working which is the new normal across sectors.

The World Economic Forum’s Future of Work Report 2020 predicts that by 2025, the job with the highest demand and growth will be that of a Data Scientist.

Data is fast becoming the world’s most valuable commodity. Experts are terming data as the future oil and analytics as the engine. All over the world, organisations are focusing on methods to organise and harness the data for their strategic goals. Data Science is a unique confluence of Computer Science, Computational Mathematics, Statistics, and Management.

Data needs to be collected from multiple sources and analysed. Data analysis and visualisation have to be performed on the data to get valuable insights into the data. Machine learning tools are deployed to build predictive models that transform the raw data into actionable information.

Knowledge Representation and Artificial Intelligence algorithms are creating intelligent machines capable of solving complex problems.

Trending technologies like cloud computing, blockchain, quantum computing are transforming data science. Effective data architecture needs to be designed for useful storage and retrieval.

All over the world, huge repositories of data are being generated. The data could be structured or unstructured and in various data formats. The data silos can be integrated and analyzed to make meaningful, data-driven decisions.

The Data Analyst is able to identify the necessary information, extract, transform the data and use it to identify significant patterns. The data analyst also helps to present the data and visualise the data for decision-making.

Career as a Data Scientist:

On the other hand, the Data scientist has more experience and can use his or her core competencies for problem-solving in diverse domains such as finance, insurance, retail, and healthcare. The data scientist is able to explore data, formulate problem statements in line with the business model, and engineer effective end-to-end solutions.’

In addition to computational and empirical skills, the most important function of a data scientist is to be able to identify problems where data science could be used, enhance human decision-making through data-driven insights.

Making sense of the enormous data available is a challenge, the Data Scientist requires skills in diverse areas such as computational mathematics, data analytics, machine learning, artificial intelligence, data visualisation, and even programming languages. In addition, knowledge is required in diverse domains like statistics, business, economics, finance, production, etc. Hence the skill set required for data science is inter-disciplinary.

However, data scientists may or may not have a STEM background, they can be from different fields, such as statistics, social sciences, economics, etc. As the size of the data repositories is growing, the demand for skilled data science professionals is increasing exponentially and currently, the demand outweighs the supply.

The Data Science job profiles include:

1. Data Scientist: The data scientist is one who has been working in diverse domains. The data scientist is able to define the problem statement, project objectives in line with the business goals. They help identify patterns and trends using artificial intelligence, machine learning and make predictions based on data. They are required to have a strong background in the related subjects of artificial intelligence, machine learning, statistics, and data engineering.

2. Data Analyst: Typically, the data analyst works with the business and management team to establish the project objectives and business needs. They facilitate the collection of relevant data and exploration of the data. They transform the data and analyse it to interpret patterns and trends. They also aid in presenting the patterns and visualising the data to help the team translate the patterns into actionable items.

They must have exceptional interpersonal skills, with technical skills like programming, database management, data analytics, and data visualization tools. Expertise in Machine learning and a thorough understanding of cloud platforms, such as Azure, IBM, and Google is expected. Business analysts specialize in business intelligence and work with business models and their relevant technology.

They need to have a good understanding of business, finance, and as well as IT technologies such as data modeling, data visualisation tools, etc. Similarly, Financial Analysts are highly specialised in the areas of finance and work on building systematic trading models, strategies, and trading signals.

3. Data Engineer: Traditionally, organisations hire, database administrators to administer and manage the data on a daily basis. They are responsible for maintaining the integrity and performance of the organisation’s databases and ensuring the security of the organization’s data. They are required to have knowledge of traditional relational databases, disaster recovery and database backup procedures, and familiarity with reporting tools.

The Data Engineer, on the other hand, is responsible for developing and maintaining scalable data pipelines and building APIs to support the data repositories. The data models have become diverse in nature and knowledge in data formats, big data technologies to populate data models have become a necessity.

4. Enterprise Data Architect: Data architects and stewards provide data management services for the enterprise at a strategic level while ensuring data quality, accessibility, and security. The Enterprise Data architects are the ones who create blueprints for data management, pipelines, and its repositories at a strategic level. They build and maintain an organisation’s database by identifying the layers of technology, the performance, and database size requirements.

They also work with the data engineers and administrators to ensure the strategic use of the data while ensuring performance, privacy, and security.

The top technology trends that could affect the future are artificial intelligence and quantum computers. Quantum computers have processing units that could potentially support the computational requirements of the next generation of applications.

AI enabled Biometrics allows a person to be identified and authenticated based on recognizable and verifiable data such as voice, iris, fingerprint and face. Biometrics-based technology such as DNA matching, Retina recognition, voice recognition, etc. will transform security and authentication systems.

With the help of AI and ML, computer vision-enabled technology is able to accurately identify and classify objects in images and videos. The proliferation of IoT, smart personal devices, and wearable devices will further spawn the application of AI and ML in transportation, healthcare, and medical sciences. The use of natural language processing methods is important to ensure human commands result in automation. The use of artificial intelligence in self-driving vehicles can help in reducing collisions and the burden on drivers.

Data science should be part of the undergraduate curriculum: AICTE

Keeping in mind, the huge demand for skilled data scientists, the recent notification from AICTE listed data science as one of the emerging areas which need to be part of the undergraduate curriculum. Several Indian technical institutions have proposed a new undergraduate engineering programme in data science.

The course content, its delivery, the faculty, and the capacity to deliver are crucial for the success of the course. The industry is looking for talent who have the required skill set along with domain knowledge.

The typical undergraduate programme in Data Science and Engineering should combine three non-overlapping streams of Computer Science, Mathematics/Statistics, and Advanced technologies. To apply their data science skills to a real-world problem, they need to rapidly gain expertise in new domains.

The industry is hiring experts from varied domains, and as a technical expert, the data scientist needs to interact and collaborate with a diverse team.

The graduate should possess domain knowledge in areas like Business, Insurance, Health Care, where data scientist is currently deployed. It’s better to include such domain knowledge within the undergraduate curriculum.

Courses on Data Science had to be taught by the right mix of academia from various disciplines as well as data science practitioners. Course delivery should incorporate projects to help learn the concepts by applying them to case studies and datasets.

The rapidly changing industry demands a graduate with more adaptable skills, which is very rare to see in the recent past. The lack of soft skills and adaptability is attributed to the education system amongst Indian universities. It is advisable that during the undergraduate programme, the students should be exposed to different environments, pedagogy, and multiple cultures. This will make a graduate a team player and a much more open and adaptable person.

It is always advantageous for a student to get an opportunity to study a semester abroad, preferably in a top-notch university. The student is exposed to the global view of data science and gains an early exposure. This will help the budding data scientist become a global citizen which is the need of the hour.

But there are other cross-functional, 21st century skills employers look for in a well-rounded data scientist. This field demands analytical creativity. Alongside knowledge of standard techniques for analysis, your success as a data scientist depends on your ability to interpret data, innovate and bring a creative approach to problem-solving.

Finally, data science is a team sport – data scientists need strong interpersonal and communication skills to work effectively with both technical and non-technical partners.

Article by Rohini R Rao, Assistant Professor and Program Coordinator, BTech Data Science & Engineering, Department of Computer Applications, Manipal Institute of Technology.

Read: Future prospects for AI learners in different sectors and professions

Read: Why cyber forensics courses can give you lucrative career options

This is a syndicated post. Read the original post at Source link .