Everyone is evident of the fact that data is the future, and data-driven techniques are running the industries. The future economy will depend heavily on such data-driven insights and solutions. Businesses have spent their time and resources collecting and mining data for years. These days, new and innovative technologies using data science career paths have made data analysis much easier and more reliable.
Data scientists are at the top of such technological advancements. Their contributions help the company efficiently achieve success. Therefore, data scientists must have enough knowledge and experience to help a business grow.
So, what data science career path must you follow to become a credible data scientist?
There is no set career path for learners to stick to if they wish to pursue a data science career. It depends on your skill sets, relevant experience, and desire to learn.
The best data science career paths in 2023
We will go through some significant data science roles that are in demand. These are divided into entry-level, mid-level, and senior-level data scientist career paths.
Entry-level data science careers
Entry-level data science roles help you gain analytical, R, and Python skills. You will learn about and explore the data science field at a beginner's level. These mainly include tasks given by senior scientists. Your soft skills are very important for entry-level positions.
Data analyst
Data analysts mainly collect data from many sources, analyse the trends in data sets, and display their findings. They also interact with the stakeholders to collect and verify the data. They work with data based on the plans specified by senior management.
Furthermore, they can enhance the data systems and improve their analytical accuracy. The projects given to these positions often change. So, they may have to work with different departments, like marketing, production, etc.
Data analysts work with languages, tools, and analysis. They can opt for more specialized and niche areas in their data science career path.
Business analysts
Business analysts possess attributes similar to data analysts. They also work on analysing data from sources to improve business. The main distinction between data analysts and business analysts lies in their extensive knowledge of both companies and their operations.
They understand how businesses operate and use this knowledge to form useful insights. They can also help data analysts in their tasks as they possess business intelligence and domain knowledge.
Business Intelligence Analysts
Also called BI analysts-they aid in making decisions with the given data. Individuals often need clarification with business analysts, who have the same job role as BI analysts. However, business analysts work on the practical aspects of the insights found. At the same time, BI analysts work more closely with data to uncover insights.
The BI analyst's role involves gathering, analysing, and visualising business data. They need to discover areas of improvement from the data. For example, they analyse market data patterns to share suggestions about how the company can improve its products.
Mid-level data science careers
Mid-level roles are a step above entry-level roles. These positions have more control and expertise over their data. These data science jobs require you to quickly understand a problem and provide the correct answer to solve the problem within a short span.
During this point in your data science career path, you are given two options. You must pick your preferred side of data science from the business and technical sides. The technical part of data science is better for those with technical proficiency. In comparison, the business side of data science will benefit those who have a better grasp of how businesses use data science for decision-making.
Data scientist
A data scientist's role includes the following:
Creating ML prediction models.
Understanding data.
Discovering trends and patterns.
Working with marketing departments to develop strategies.
They also report to business partners to share their findings and understanding of the data.
It is a high-demand role sought by many. It requires a knowledge of basic statistics, programming languages, and advanced ML techniques. It should also possess skills in conveying its findings. Data scientists should understand the process of developing models and exploratory data analysis.
This position requires the fulfilment of the duties detailed below.
Knowledge of the company demands
Cleaning and preparing data
Finding data from relevant sources
Creating prescriptive or predictive systems employing R or Python programming language
Conveying results and suggestions to partners.
Data Architect
Data architects have various IT skills and are more inclined towards designing attributes. They form a plan for every data management system and infrastructure. They can analyse and understand a company's data structure, create databases, and enhance data storage and management solutions.
Data architects ensure their business's data systems are scalable and display high performance. Throughout multiple platforms, they show their analytical excellence.
Data Engineer
A data engineer is responsible for working with data pipelines to ensure that data scientists always have access to information. They also develop new and better solutions for the rising data complexity & diversity. They create an easy work module for data scientists by providing them with modified data for better analysis.
Data engineers create, build, and optimise ETL pipelines for data structuring and cleaning. They use various big data tools and modern programming languages such as Python and Scala to build these data pipelines. A data engineer is a central and particular position in any business. This role holds a strong role in the tech industry, creating huge career opportunities.
Senior-level data science careers
Senior-level data scientist roles come with complete ownership of your tasks and management responsibilities in major projects. These are responsible for hiring and building a team of credible data scientists, engineers, and analysts to get the job done. They also work together with business leaders and executives to communicate well.
These data science professionals possess leadership qualities and are able to understand and manage the entire data science process. They can connect various aspects of data science, e.g., analytical, technical, and business. This helps them interpret the findings to the business partners.
Check Out This Blog:10 Reasons Why Data Science is A Best Career Move
Principal/lead data scientist
This role requires a minimum of 5 years of experience. They lead many data science projects and look after the complete execution of the strategies. These professionals have solid business knowledge. They can figure out solutions to various business problems.
Lead data scientists perform data exploration to extract hidden business risks. As a principal data scientist, you should be able to deliver data science strategies that impact business use cases. You'll often have to work with higher management to discuss pain points and solutions. Principal data scientists also mentor junior data scientists for their specific roles in the industry.
Data science manager
Data managers work on the specifications given by data architects to develop and maintain data systems. They are responsible for collecting and storing private information safely. The role requires understanding Big Data tools like Spark, Hadoop, etc.
These professionals must interact with different teams and create data science solutions based on their requirements. Basic knowledge of machine learning, big data frameworks, and statistics is essential in this job position.
Senior Director/VP of data science
This role requires a minimum of 10 years of experience in the data science field. These professionals usually have experience working in many industries. As a result, they possess skills in managing data science projects and business knowledge. A solid understanding of big data frameworks, data science, and leadership skills is preferable.
Senior data scientists, such as Data Science Managers, Data Engineers, Principal Data Scientists, etc., work under the senior director. Their work involves using big data tools and complicated data science solutions with team heads and senior management. You'll have to understand major business needs to merge them with data science products as a VP of Data Science.
Conclusion
Choosing the right data science career path is relatively effortless, But you might need some help in the beginning. Understanding all available roles is key in selecting an ideal one. Consider the amount of competition each path deals with so you are prepared down the road.
To keep yourself in front of the competition, you should constantly study and learn new and upcoming technologies. You can get started by opting for any credible Masters In Data Science course.
Here, the Data Science and AI Master Program can help you understand both the technological and business aspects of a data science career path. You can gain knowledge of business scenarios and perform practical work on real-life data science projects with mentors through a master's in data science.
You also stand a chance to become IBM and Microsoft certified by enrolling in online IT certification courses. Furthermore, you'll also get career counselling from experts on your preferred data science career path and develop an effective plan.
Frequently Asked Questions:
1. Is a career in data science worth it?
A data science career is a great choice with tons of growth potential. It offers competitive salaries, in-demand data science roles, and major benefits for professionals.
Data scientists should have business sense and the ability to understand data effortlessly. Businesses are eager to use their talents for their growth.
2. Explain the data science career path.
Data science usually enters the field in junior positions. They can move up to bigger positions as they eventually learn and gain better responsibilities. You should possess various skills in data analysis and business sense to help companies make smart decisions.
3. Do data science roles involve IT?
As data scientists use data to help businesses, their role includes IT knowledge. They should be effective in specific technologies and tools required. They can deliver business insights using huge amounts of data. So you must have a sound knowledge of IT in data science.
