Data science is an immensely vast field, consisting of different hierarchies which requires different amount of skills set. The more you explore this field, the more you will realize that you cannot master them all to complete the project associated with the methodology of Data Science. Instead, you will be joining a team where every team member will be having a specific set of skills which comply with a particular task, be it Data Wrangling, Cloud development or even Data Visualization.

One should understand of not being best at doing everything, but simply that you are best at doing specific things.

What are areas one can focus on?

  • Data Engineering and Data Warehousing

Data Engineering refers to extracting and transforming structured and unstructured data from various sources for analysis. It often involves tasks like managing the source, structure, quality, and storage.

Related positions: Database Developer, Data Analyst, Data Engineer

  • Data Mining

Data mining is related to statistics where one applies statistical formulas & different predictable models for explanatory analysis i.e. analysis and exploring data. The purpose for this task is to reveal patterns and trends in the data to answer any business questions associated with it.

Related positions: Business Analyst, Data Scientist

  • Cloud and Distributed Computing

Cloud and system architecture refer to designing and implementing enterprise level infrastructure for cloud and distributed computing. The role also analyzes system requirements and ensures that systems will be integrated with business users.

Related positions: Cloud Engineer, Cloud Architect

  • Database Management

This role refers to designing, managing and deploying complex databases for high volume and complex data transactions within a company.

Related positions: Database Analyst, Database Administrator

  • Business Intelligence

Some of the responsibilities associated with this field is transforming structured data, building analytics solutions, building and managing dashboards using tools like Tableau, Microsoft Excel and Power BI, storyboarding, revealing trends and patterns within historical data and giving presentations to the stakeholders.

Related positions: BI Developer, BI Analyst, BI Engineer

  • Machine Learning Development

Many people refer to Machine Learning professional as someone who develops robots. However, this is not the case as Machine Learning professional refers to the one who enables systems to analyze historical data using already available algorithms and predict outcomes of new data, based on the historical data which was learnt. Few of the most common tasks related to Machine Learning are building pipelines, convenient data sources and A/B testing. Additionally, they evaluate the algorithms using different methods like K-Fold, Train Test Split and others. Generally, Programming languages like R and Python are used for accomplishing the tasks mentioned above.

Related positions: AI Specialist, ML Engineer and Cognitive Developer

  • Data Visualization and Presentation

Although many professionals would disagree, but the domain of Data Visualization and presentation has become a vital domain within Data Science because almost every organization requires someone to invest the time for understanding the data and creating BI solutions for them to make them understand to overcoming the obstacles and business problems within the organization. In other words, professionals related to Data Visualization transforms boring data into visually appealing graphics for making the stakeholders understand and take actions in accordance with the data.

Related positions: Data Viz Engineer, Data Viz Developer

  • Operations-Related Data Analytics

This domain is suitable for the professionals who are not tech savvy but have the passion of being a problem solver and has business domains knowledge. These type of roles focuses on finding opportunities of improvement within the operations of the business. These can be professionals having a focused knowledge on logistics, technology, Human Resources, Finance etc.

Related positions: Decision Analyst, Planning Analyst, Communications Analyst

  • Market Analysis

These people focus on external data related to customers, sales and marketing, yet their purpose is similar to those in operations; analyzing performance and find opportunities of improvement.

Related positions: Market Analyst, Sales Analyst, Financial Analyst

  • Data Analytics specific to certain sectors

Lastly, for the professionals who studied Finance, Human Resources or even Healthcare, Data Science requires these domain specific professionals for simple analyst positions within the organizations of these industries.


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