Data analytics career paths span a wide range of career options, from data scientist to data engineer. Data scientists are often interested in what they can do with the data that is analyzed, while data engineers are more focused on the analysis itself. Whether you’re looking for a career as a data scientist, data analyst, ML engineer, or AI researcher, there’s something for everyone! In this blog post, we will different types of jobs and careers available to those interested in data analytics and data science.
What are some of the career paths in data analytics?
Here are different career paths for those interested in data analytics career:
- Data Scientists: As a data scientist you will be working with the data to create recommendations, using machine learning/deep learning and AI. You might work on topics such as predictive analysis of large-scale datasets. Some of the skills required for data scientists include critical thinking, data analysis, mathematical/statistical expertise, knowledge of machine learning / deep learning algorithms, and presentation skills. Some of the tools useful for data scientists include R, Python, and SQL.
- Statistician: A career as a statistician will involve analyzing data collected on an organization to help with forecasting, decision making, and problem-solving. The expertise required to be a statistician includes knowing how to analyze data using statistical tools and techniques. A career as a statistician will also require the ability to communicate your findings in simple terms so that they can be understood by both technical and non-technical audiences. Some of the skills required for being a statistician include critical thinking, problem-solving, analysis, communication, and presentation skills.
- Data Engineers: Data engineers are responsible for designing and building data pipelines/systems that move, store and process the data. They might create databases, manage data pipelines using Hadoop or Spark MLlib, etc. They often collaborate with IT professionals to develop databases, create APIs, etc. Some of the tools useful for data engineers include Apache Hadoop, Spark, and AWS. At times, they are also referred to as big data engineers.
- Data Analyst: A career as a data analyst will involve using your knowledge of statistics and machine learning algorithms to analyze the information collected. You might work on topics such as creating data visualizations or predictive analytics within the organization you are working for.
- Machine learning (ML) engineers: ML engineers are often tasked with building and maintaining ML systems. ML engineers are different than data engineers because they are more focused on the data analysis itself, rather than managing or creating pipelines. Some of the skills required for ML engineers include critical thinking and problem-solving skills, interest in machine learning algorithms, and programming knowledge (Python preferred).
- Machine learning (ML) researchers: Machine learning researchers work to improve existing systems through research. The design algorithms and evaluate their merits. Some of the tools useful for ML researchers include TensorFlow, Apache Spark, Python, RStudio.
- MLOps engineers: MLOps engineers are data engineers who specialize in machine learning pipelines. They will work with the developers to help them build and maintain machine learning algorithms, databases, etc. Some of the skills required for being a MLOps engineer include knowing how to manage production environment (monitoring, scaling), DevOps knowledge (running automated tests & deployments).
- Data Visualization Specialists / Experts: Data visualization specialists use data to create compelling visual representations of the information, which can be used for decision making or to communicate an idea effectively. Some of the tools useful for data visualization specialists include Tableau, DensityDesigner.
- Computer Vision Engineer: A career in computer vision can involve using your skills to develop systems that process digital images and videos. Computer vision engineers might build algorithms or create code for applications like image recognition, video analysis, etc. Some tools include OpenCV, MATLAB, and Tensorflow.
- Data Architect: The data architect is responsible for designing and building the databases and data models that an organization uses. This career path might also involve managing a team of database developers or data modelers, as well as ensuring data quality across all systems.
- Data Science Architect: Data science architect career path is similar to the data architect career path, but involves more focus on the business needs vis-a-vis data science / AI solution design. A career as a data science architect might involve consulting with client stakeholders about how best to implement AI/ML solutions for their organization (useful tools include R Studio and Python).
- AI researcher: AI researchers work to improve the existing technology in artificial intelligence research fields such as machine learning. They often have a background in mathematics or computer science and their career path includes opportunities for university professor positions. It is not necessary that you must be a data scientist before becoming an AI researcher, having a background in mathematics or computer science will give you an edge.
- Data quality analyst: A career in data quality analysis will often involve looking at the information collected, analyzing it, and working on improving its accuracy. Data quality skills include process improvement, data cleansing, data auditing. Some of the tools for this career path include Talend Open Studio and Tableau Desktop.
- Data wrangler: A career as a data wrangler involves managing all the information collected by an organization or business unit. They may be responsible for maintaining databases, providing reports on analytics to management teams within an organization, etc. Data wranglers possess excellent communication skills, the ability to explain data in a way that is easily understood by business stakeholders, and have strong leadership qualities. Useful tools for this career path include Tableau Desktop v/s RStudio.
- Data lineage analyst: A career in data lineage analysis involves understanding the data, its origins, and how it is moved through various systems. Some of the tools for this career path include RStudio, Python etc. Data lineage analysts typically have a background in computer science and possess excellent data analysis skills. Data lineage topics include data provenance, data profiling.
- Data privacy engineer: The career path of a data privacy engineer involves taking care of the safety and security of personal data within an organization.
- Data governance anaIyst: A career as a data governance analyst will involve managing, auditing, and monitoring the data within an organization. Different aspects of data governance include data management, compliance, and governance.
- Business intelligence (BI) developer: As a business intelligence developer you might be working with data models or creating new features to ensure that all users of your systems are able to access useful insights easily.
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