The path to becoming a data scientist: Must-have skills and competencies

Subhashis Manna
Subhashis Manna

Data science is a fast-growing field, and data scientists have become a sought-after profession in today’s data- and technology-driven, dynamic world. The responsibilities of a data scientist have broadened due to technological advancements and the growing intricacy and expansion of data environments. From analysing consumer behavior to optimising business operations, data scientists now play a pivotal role in extracting actionable insights, predicting potential outcomes, etc., from vast amounts of data. However, mastering the skills required to excel in this field is a journey that spans various domains such as data, computer science, information technology, and statistics. In this extensive guide, we will delve into the essential skills and proficiency levels needed to embark on a career as a data scientist.

Foundational skills: Building the backbone of data science

As a domain, data analytics is vast and consists of many sub-domains. These include:

Additional skills

In addition to the core domains of data analytics, data scientists should also possess expertise in the following areas:

  • Data security: Ensuring the security and confidentiality of sensitive data through robust security measures and protocols.
  • Master data administration: Managing master data entities and attributes to ensure consistency and accuracy across organisational systems and processes.
  • Metadata convention: Establishing standardised metadata conventions to facilitate data discovery, understanding, reporting, and management.
  • Data quality management: Implementing processes and tools to assess, monitor, and improve data quality throughout its lifecycle.
  • Data technology platforms: Familiarity with various data technology platforms, including both on-premises and cloud-based solutions, to support data analytics initiatives.
  • Data governance structuring: Establishing governance frameworks and policies to effectively manage and utilise organisational data assets.
  • Cloud knowledge: Understanding cloud computing concepts, architectures, and services and leveraging cloud platforms for scalable data analytics.
  • ML Ops: Implementing machine learning operations (ML Ops) practices to streamline and automate the development, finetuning, deployment, and management of machine learning models.

The domain of data science is vast, and mastering the skills required to excel in this field is a journey that spans from foundational knowledge to expert proficiency, covering various levels and areas of expertise. Let us now explore the proficiency levels needed to embark on a successful career as a data scientist.

Categories Basic Intermediate Skilled
Data management
  • Basic understanding of SQL (Structured Query Language)
  • Basic concepts of databases and data structures
  • SQL and NoSQL queries, including subqueries, nested queries, and advanced JOINs.
  • Ability to create and manage database objects, such as tables, views, indexes, and stored procedures.
  • Proficiency in data manipulation
  • Ability to integrate SQL with other data processing tools (e.g., ETL processes, integration with big data platforms, data on cloud).
  • Proficient in handling complex data analytics tasks, such as using SQL for machine learning model preparation, advanced statistical analysis, and complex reporting and visualisation.
Computer programming
  • Basic understanding of Python or SAS (Statistical Analysis System) other than SQL
  • Proficiency in SQL, SAS, Python, R, VBA, Java, Scala
  • MATLAB, FICO Model Builder, etc.
  • Expert level in some of these computer languages for advanced data management, analysis, insights, machine learning, and automation tasks.
Data visualisation
  • MS Excel, VBA, Power BI, Tableau
  • Proficiency in Power BI, Tableau, Qlik Sense, Looker, Python, etc.
  • Expert level in a few of these visualisation tools, in addition to proficiency in Ui/UX, design, dashboarding, etc.
Data engineering
  • Basic understanding of data migration and ETL (Extract, Transform, Load) processes
  • Exposure to informatica, collibra, data bricks, etc.
  • Proficiency in designing and implementing complex ETL workflows.
  • Familiarity with advanced ETL tools and platforms (e.g., Apache Airflow)
  • Expertise in designing scalable and robust data architectures.
  • Ability to design systems for real-time and batch data processing, including stream processing.
  • Knowledge of Apache Kafka, Apache Flink, Spark, Hive, Pyspark, etc.
Data analytics (including AI/ML/ Gen AI)
  • Basic understanding of statistical tests, predictive modeling, validation, and calibration
  • Exposure to timeseries forecasting, Monte Carlo simulation, VaR analysis, etc.
  • Proficiency in Financial modeling and advanced quantitative techniques
  • Exposure to ML models and AI algorithms, including NLP, Deep Learning Framework, Computer vision, etc.
  • Expertise in frameworks such as pandas, NumPy, scikit-learn, TensorFlow, PyTorch, etc.
  • Proficiency in using LLMs and advanced algorithms of Generative AI, etc.
  • Exposure to ML Ops, Finetuning of LLMs, CI/CD pipelines, model implementation to production, etc.
  • Entry-level positions in the data science domain.
  • Middle to senior-level positions (4-8 years) within data science roles.
  • Subject matter experts in particular areas of data science with a minimum of eight or more years of hands-on experience.


In conclusion, becoming a proficient data scientist requires a comprehensive understanding of various domains within data analytics and proficiency in additional skills and knowledge areas. Data scientists must continuously update their skills and adapt to evolving technologies and methodologies, from foundational concepts to advanced techniques. By acquiring and honing these essential skills, aspiring data scientists can embark on a rewarding career path, driving innovation and making meaningful contributions to their organisations and industries.