Business

What is the Data Science career path?

In a world where data rules, businesses rely heavily on data to make decisions, solve problems, and stay ahead of the competition. Data determines the path that any business operations will take hence it is not business as usual. Companies that do not harness data risk operating on an uneven playing field with the competition which beats their very existence. With the need to leverage insights from data comes the demand for professionals that can make sense of the large sets of data available to businesses to help drive profitable operations.

Data science is a highly valued role because it helps companies to translate data into actionable insights using various tools. For the last more than four years, the data scientist role was the highest in demand in the United States. In the Philippines, the demand is coming up sharply with an average annual salary of P432,000 according to Glassdoor. Speaking about the Data Science Course in Kolkata, colleges have seen more and more students that are interested in pursuing data science enrolling to take advantage of opportunities in this growing field.

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What Does a Data Scientist do?

Data scientists drive business decisions. They are problem solvers with a mix of skills in mathematics, computer science, business intelligence, and IT skills. Their roles cut across both IT and the business world thus they create a link and a balance between the two.

The primary role of a data scientist is to collect, analyze, and interpret data, delivering insight that will enhance the growth, operations, and profitability of the company. Some of the important roles that a data scientist takes on include:

  • Identifying and prioritizing business issues with the greatest reward to the business that requiring data analysis
  • Collect and organize relevant data from various sources
  • Clean and validate data to ensure quality and accuracy
  • Developing and implementing statistical analysis models and algorithms for data analysis
  • Analyze data, identifying patterns, insights, and trends for the business
  • Interpret and present the data to stakeholders to facilitate decision-making.

Top data science skills

The data science professional needs to acquire relevant skills to be able to carry out his/her duties as expected. Whether you are just launching your career as a data scientist or seeking to advance in the field, the Bureau of Labor Statistics ranks the following as the top data science skills.

  • Analytical skills
  • Mathematics and statistics
  • Programming particularly Python and R
  • Database and SQL knowledge
  • Data visualization
  • Critical and logical thinking
  • problem-solving
  • Communication

The Data Science Career Path

The data science role cuts across all industries. Therefore, apart from acquiring data science skills and advancing to their desired specializations, data scientists also need to develop domain knowledge and experience in their specific fields to provide better value to the business.

Want to know more about what it takes to rise the ranks in data science? We put together these comprehensive guidelines for this very purpose.

1. Would you make a good data scientist?

Your first step is to evaluate your background and qualifications. In other words, do you have the relevant background and qualifications to pursue a career in data science? A data science career requires:

  • A Mathematics, Statistics, computer science, IT, Information systems, or any other relevant degree.
  • Masters degree in computer science or a related field
  • Background in computer programming to help you design models and algorithms to mine large datasets
  • If you are pursuing a mid or advanced-level career in data science, you need substantial work experience.
  • Possess skills and some experience in data collection, cleaning, analysis, and visualization.
  • Demonstrate critical thinking, problem-solving, and communication skills.
  • Business acumen to align data analysis with business strategy

2. Data science entry-level

You can either begin your data science career from scratch or transition into the data science field from other roles like data analysis. This is where it all begins before ascending to senior data science positions where you will most likely pursue a specialization. Also, you may opt for a management path or decide to be a highly specialized data scientist. Whichever path you opt for, how you start your career journey determines its success.

Some entry-level data science positions you can consider applying for include:

  • Data analyst
  • Junior data scientist
  • Associate data scientist
  • Data science paid intern

Entry-level data scientists handle roles like testing new models or algorithms, debugging models, contributing code, improving code quality, running analyses, and more.

3. Additional qualifications

An entry-level position will only help you set your foot into the field and perhaps give you a bearing on which path to pursue. At this point, consider gaining additional qualifications through training and certifications to build your knowledge, skills, and experience. These include certifications in:

  • Business intelligence
  • Relational database management
  • Python or R Programming
  • Data visualization
  • Data mining

4. Mid-Level data science roles

With at least three years of relevant experience, it is possible to land mid-level data science roles such as:

  • Senior data scientist where you will be responsible for architecting ETL pipelines on different environments, writing reusable code, working on core data science projects, preparing high-quality data, and advanced analyses.
  • AI/Machine learning engineer where, in addition to some senior data scientist roles, you will develop end-to-end machine learning solutions, build complex machine learning models, and visualize data. Here you will have built working knowledge around AI, ML, deep learning, and natural language processing.

Well, this level comes with higher responsibilities therefore if you still do not have certifications or advanced qualifications in your portfolio, it is time to seriously consider ones that are relevant to your specialization of interest. This is because while your experience can speak for you, recruiters prefer certified professionals.

5. Data science management roles

If you intend to pursue data science management roles, you need to be a people person because you will be managing people and resources. To be able to do this, you will have acquired substantial experience and demonstrated an ability to work autonomously and as part of a team in data science projects. Also, soft skills like leadership skills, communication, problem-solving, and critical thinking become crucial at this level. These are people who not only rise up the ranks but also mentor associates, educate others, and can answer to the management and other stakeholders.

They also possess the ability to scale data science strategies and steer the organization to making data-driven decisions and exploring opportunities in current and future trends. In addition, you will demonstrate your business acumen and prioritize data science projects based on their ROI on business strategies.

Some data science management roles include:

  • Principal data scientist
  • Data science manager
  • Data science architect

6. Advanced data science roles

Advanced data scientists, as we had already mentioned, are either overall managers, directors, or seasoned specialists. An overall data science manager is charged with the responsibility of running the organization’s entire data science operations and therefore requires the right mix of skills and experience.

Ideally, they have vast experience managing teams and steering large data analysis projects from start to end. They are aware of and can be depended upon to implement the latest technologies, make strategic decisions based on insights from data. In simple terms, their input directly determines the success or failure of the business or the direction that the business operations will take.

Conclusion

The demand for refined data scientists across levels is huge if companies’ reliance on data-driven decisions is anything to go by. However, as a professional, packaging yourself for the role you intend to advance to should be a continuous process as you go along. Work on acquiring the right mix of skills, gaining experience, and also mentoring others through their career path. Data science is a very rewarding career.

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