Beyond Data Science: Exploring Alternative Career Paths for Data Science Graduates
Data science, a fairly new field that combines different disciplines, has gained a lot of attention and been called the "sexiest job" since it first appeared a few years ago. Since then, the need for data scientists has grown very quickly, drawing in people from many different professional backgrounds. However, a lot of them don't know that studying data science can also make them eligible for other jobs, some of which might be a better fit for their backgrounds, interests, and skills. In this article, we will talk about the different jobs data science graduates can apply for, as well as the requirements and tasks connected to each role.
Without further ado, let's explore nine roles data science graduates can apply for.
Data Scientist
Data scientists analyse, interpret, and draw insights from complex data sets using statistical methods and machine learning algorithms. For example, a data scientist at an e-commerce company may analyse customer data to identify patterns that can help improve customer retention and increase sales.
Main responsibilities
Data pre-processing, feature engineering, model selection, model evaluation, visualization, and communication of insights.
Required skills
Analytical skills: statistics, probability, machine learning, data analysis and visualization
Technical skills: programming (Python, R), data manipulation (SQL, pandas), machine learning libraries (scikit-learn, TensorFlow, Keras)
Data Engineer
Data engineers design, build, and maintain the data infrastructure required to store, process, and analyse large volumes of data. For example, a data engineer at a financial institution may develop a scalable data pipeline to process and store transaction data for analysis.
Main responsibilities
Data pipeline design, implementation, optimization, and maintenance; data storage and processing solutions; data integration and ETL (extract, transform, load) processes.
Required skills
Technical skills: programming (Python, Java, Scala), data manipulation (SQL), big data frameworks (Hadoop, Spark), cloud platforms (AWS, GCP, Azure)
Analytical skills: data modelling, data warehousing, distributed computing
Business Intelligence Analyst
Business Intelligence Analysts use data to generate actionable insights that support decision-making within an organization. For example, a BI analyst at a retail company might create reports and dashboards to help managers understand store performance and identify opportunities for improvement.
Main responsibilities
Data collection and analysis, report generation, dashboard creation, KPI monitoring, and communicating findings to stakeholders.
Required skills
Analytical skills: descriptive statistics, data visualization, reporting
Technical skills: SQL, data visualization tools (Tableau, Power BI), BI software (Qlik, Looker)
Business acumen: understanding of business processes, KPIs, and industry knowledge.
Machine Learning Engineer
Machine Learning Engineers develop, implement, and optimize machine learning models to solve specific business problems. For example, a machine learning engineer at a healthcare company may develop a model to predict patient readmissions based on medical records.
Main responsibilities
Model development, feature engineering, model evaluation, optimization, deployment, and maintenance.
Required skills
Analytical skills: machine learning, deep learning, statistics
Technical skills: programming (Python, R), machine learning libraries (scikit-learn, TensorFlow, Keras), cloud platforms (AWS, GCP, Azure)
Data Analyst
Data analysts collect, process, and analyse data to help organizations make data-driven decisions. For example, a data analyst at a marketing agency might analyse campaign data to evaluate the effectiveness of different marketing channels.
Main responsibilities
Data collection, pre-processing, analysis, visualization, and reporting.
Required skills
Analytical skills: descriptive statistics, data visualization
Technical skills: programming (Python, R), SQL, data visualization tools (Tableau, Power BI)
Database Administrator
Database administrators (DBAs) manage an organization's database systems, ensuring their performance, security, and availability. For example, a DBA at a university might maintain a database of student records and ensure data integrity, security, and timely access for authorized users.
Main responsibilities
Database design, implementation, performance tuning, backup and recovery, security, and user management.
Required skills
Technical skills: SQL, database management systems (Oracle, MySQL, SQL Server), scripting languages (Python, Bash)
Analytical skills: data modelling, normalization, performance analysis
Data Architect
Data architects design and implement data infrastructure and systems to support an organization's data needs. For example, a data architect at an insurance company might design a scalable data storage solution to handle the growing volume of policyholder information.
Main responsibilities
Data modelling, system architecture design, data integration, and data warehousing.
Required skills
Technical skills: SQL, database management systems (Oracle, MySQL, SQL Server), big data frameworks (Hadoop, Spark), cloud platforms (AWS, GCP, Azure)
Analytical skills: data modelling, normalization, distributed computing
Big Data Engineer
Big data engineers develop and maintain the infrastructure required to process and analyse large volumes of structured and unstructured data. For example, a big data engineer at a social media company might build a data pipeline to ingest and process billions of user interactions daily.
Main responsibilities
Data pipeline design, implementation, and optimization; big data storage and processing solutions; data integration.
Required skills
Technical skills: programming (Java, Scala, Python), big data frameworks (Hadoop, Spark), NoSQL databases (Cassandra, MongoDB), cloud platforms (AWS, GCP, Azure)
Analytical skills: distributed computing, data modelling, data warehousing
Statistician
What they do: Statisticians apply statistical theories, methods, and techniques to analyse data and draw conclusions. For example, a statistician working for a pharmaceutical company might design and analyse clinical trials to determine the safety and efficacy of new drugs.
Main responsibilities
design experiments, data collection, data pre-processing, statistical analysis, hypothesis testing, and reporting results.
Required skills
Analytical skills: inferential statistics, probability, experimental design, time series analysis, regression analysis
Technical skills: programming (R, Python), statistical software (SAS, SPSS, Stata)