Career path in Data Science

Today’s world is constantly evolving with cutting-edge technology, and the analytics sector is witnessing a sharp increase in demand for highly-skilled professionals who can turn these emerging technologies into actionable insights from big data. Large enterprises and organisations are constantly looking for experts with a data science career path who can understand both the business world and the tech world. 

This paves the way for talented youth worldwide to build successful careers in data science. With numerous openings spanning across sectors, data science jobs are Glassdoor’s number 3 best jobs in the USA.

Who is a data scientist?

A data scientist represents a broad position from analysts to data visualisers and business intelligence experts. Considered a mix of mathematicians, trend-spotters, and computer scientists, data scientists have various roles and responsibilities.

• Using big data methodologies and creating a statistical network for mitigating risk and fraud.
• Delivering relevant products at the right time so companies can develop new products to meet their customers’ needs.
• Mining and extracting usable data from valuable data sources.
• Planning, implementing and assessing high-level statistical models for various problems, including sampling, classification, clustering, pattern analysis, projections, and many more.
• Machine learning tools and advancements are used to select features and create and optimise classifiers.
• Designing innovative strategies to solve difficult business problems and understand consumer trends.
• Preprocessing and analysing complex datasets to tease out insights for clearly presenting results.
• Performing data visualisation and mining to propose solutions and strategies to tackle business challenges.
• Integrating software programming and statistics for processing, cleansing, and validating the integrity of data to be used for analysis.
• Collaborating with superior data scientists to communicate obstacles and enhance business performance and decision-making.

ALSO READ: Why you should become a data scientist

Career prospects for data scientists

The booming domain among the top 20 skills in-demand in today’s workforce is data science. Data science career growth is rising with more demand for data analysts and data scientists. Large companies and organisations had to move their operations across digital platforms during the pandemic. This spanned numerous openings for data science in e-commerce. 

As one of the industry’s most lucrative jobs, companies are looking for skilled talent and expertise in programming skills, statistics, machine learning, data wrangling, and data visualisation. If you want to know how to start learning data science, start from knowing the basic roles and responsibilities.

The various roles in data science include-

• Empowering management in decision making

Acting as a trusted advisor and strategic partner to the organisation’s upper management, a data scientist has to ensure that the working staff maximises their analytics capabilities. A data scientist can influence and improve the process of decision-making by communicating and demonstrating the ideals through measuring, tracking, and recording performance metrics and other necessary information.

• Developing data collection procedures

One major role in the data science career path is to examine and explore the organisation’s data to recommend certain actions that can help to improve the institution’s performance, better engage customers, and ultimately increase profitability.

• Identifying opportunities

Driven by a mindset to continuously and constantly improve the value derived from the organisation’s data, a data scientist questions existing processes and the organisation’s current analytics system for developing additional methods and analytical algorithms.

• Adopting best practices for challenging the staff

A data scientist must ensure that the working staff is familiar and well-versed with the organisation’s analytics product. By demonstrating the effective use of the system to extract insights and drive action, they prepare the staff to understand product capabilities and address key business challenges.

Diverse roles for a data science professional

Candidates aspiring for a career path in data science can choose to be in one of the major positions: data scientist, data analyst, or data engineer. They range from entry-level interns to top-level executives in their respective positions.

How to start a career in data science?

1. Data scientist

• Junior data scientist

Candidates with a proper maths background and desirable skills in programming can start as entry-level interns as a junior data scientist. They are involved in data extraction and collection processes.

• Associate data scientist

After completing a minimum of one year’s experience working with large databases as a junior data scientist, one gets promoted to an associate data scientist. They are involved in data mining and visualisation.

• Senior data scientist

Employees with five years of experience in advanced machine learning and AI are usually considered senior data scientists. They need to work in statistical models and be involved in decision-making.

• Lead data scientist

With 5+ years of experience and expertise in handling data, one can get promoted to a lead data scientist position. They act as mentors to junior and senior levels and involve collaborative activities and team management.

• Head of data science

Employers with 10+ years of experience in data handling are responsible for running the company by hiring the right candidates, establishing high standards, and achieving worthwhile goals.

2. Data analyst

• Junior data analyst

The most basic level or entry-level role in the data science career path in India is a data analyst. Generally working in teams supervised by senior analysts or consultants, data analysts are responsible for-

• Collecting data from safe, reliable, and accurate sources followed by recording and storing the data in existing databases.
• Cleaning the required data for interpretation, analysis, or decision-making through different tasks, such as standardisation, de-duping, and identifying missing values.
• Preparing reports after conducting basic analysis and noting the results using MS Excel and Power Bi tools.
• Serving as an internal resource for key managers in all service operations.

• Associate data analyst

After a minimum of one year of experience working with large databases as a junior data analyst, one gets promoted to an associate data analyst role. They are responsible for developing VBA applications, aiding business process automation and streamlining, and helping with data validation and sanitisation for various blockchain models.

• Senior data analyst

Employees with 5 years of experience in trend forecasting, modelling, report creation, and knowledge of SQL and basic programming languages are usually considered senior data analysts. They involve building analysis, data visualisations, and multi-functional reporting that provides critical support to the operations and decision-making of the organisation through experimentation.

• Lead data analyst

With 5+ years of experience and expertise in handling data analysis software, one can get promoted to a lead data analyst position. The role involves conducting data analysis to identify patterns and meaningful insights to make recommendations for improvement, as well as providing support to other team members by coordinating, researching, and compiling data.

• Head of data science

Candidates with 10+ years of experience in scripting languages and rapid prototyping skills in programming such as SQL, Python, Perl, Java, VB, etc., are considered for the position of head of data science.

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3. Data engineer

• Junior data engineer

Candidates from a maths or computer science background with desirable skills in programming and working with datasets start as entry-level interns as a junior data engineer. They are involved in building database systems, fixing bugs, and creating interfaces.

• Associate data engineer

After completing a minimum of one year of experience working with large databases as a junior data engineer, one gets promoted to an associate data engineer position. The role involves data extraction, coding, frameworks, and preparing ETL processes.

• Senior data engineer

Employees with five years of experience in advanced areas of algorithms and data structures such as Java, Python, and SQL are usually considered senior data engineers. They need to work in data warehousing and ETL tools.

• Lead data engineer

With 5+ years of experience and expertise in handling data, one can get promoted to a lead data engineer position. They act as mentors to junior and senior levels and are involved in collaborative activities and team management.

• Head of data science

Employees with 10+ years of experience in data handling are responsible for running the company by hiring the right candidates, establishing high standards, and achieving worthwhile goals.

READ MORE: Difference between a data scientist and a data engineer

How can I start a career in data science?

MAHE is one of India’s leading education providers to facilitate a data science career path. MAHE’s Master of Science programme at Online Manipal is the perfect blend of machine learning, big data analytics, and statistics for candidates aspiring to build a distinctive career in data science. You can gain experience in various sectors such as predictive modelling, machine learning application, developing strategic and tactical recommendations, and solving problems with real-world data.

Fees structure

The M.Sc. programme consists of four semesters with suitable fee payment of Rs.65,000 per semester and a total of Rs. 2,60,000 for two years. 

Eligibility criteria

The eligibility criteria differs for national and international students – 

Indian students

Graduates from a background in statistics, mathematics or computer science with a minimum of 2 years from universities recognised by the Association of Indian Universities (AIU) along with a minimum of 50% of marks or equivalent grade, are eligible to enrol. 

International students

Students holding NRI and PIO status, who are residing outside India are considered international students. Graduates from a background in statistics, mathematics or computer science with a minimum of 2 years from universities recognised by the Association of Indian Universities (AIU) along with a minimum of 50% of marks or equivalent grade are eligible to enrol. 

Subjects covered

Semester 1 – Computational mathematics – Probability and Probability Distribution – Programming with R and Python – Statistical Inference – Database Management	Semester 2 – Linear Regression Models – Categorical Data Analysis and Generalised Linear Models – Distributed Algorithms and Optimisation with Hadoop and Spark – Stochastic Processes – Design and Analysis of Experiments – Mini Project
Semester 3 – Longitudinal Data Analysis – Machine Learning methods – Deep Learning and Text Mining – Bayesian Statistical Modelling Elective 1 (Choose one) – Health Technology Assessment – Image processing and analytics Elective 2 (Choose one) – Nonparametric and Nonlinear – Regression Models – Time Series Analysis	Semester 4 – Programming in SAS for Analytics – Applied Data Analytics – Research Methodology – Capstone Project

Conclusion

The domain of data science is a rewarding career for aspirants. Every step of the role requires persistence, skill in mathematics and statistics with a combination of engineering activities. Every big company, from Apple and Microsoft to Oracle and Walmart, regularly looks for data science graduates. With an advanced degree offered by Online Manipal, you can attain your ultimate goal of becoming a data analyst or a data scientist.