The promising career options for the youth of the 21st century are both Project Management and Data Science. Both options belong to different lines, so differentiation cannot be made based on the same lines. The skills and interests of a person determine their interest in the field. Specific factors like salary and growth prospects are significant and similar in both fields, and several industries employ qualified project managers and data scientists.
Project management should be favored by people who have good management skills as it involves work on a macro level. Besides, the work involves supervising tasks delegated to the team and completing the responsibilities assigned to each team based on skills and knowledge.
Increasing Demand of Data Science
Alternatively, Data Science has shown a growing demand for data scientists in the present and future periods. This field should be chosen by candidates who have an interest in working with figures and complex data as it is a technical field. It involves scrutinizing data, extracting inferences, and acknowledging the latest trend to enhance decision-making and strategy planning. The tasks included in this profession are data mining, data processing, data analysis, and data visualization.
Now, let us understand the role of both professions to make a clear distinction.
What are the Roles of a Project Manager?
A Project Manager ensures interaction with stakeholders with efficient completion of projects through initialization, planning, designing, controlling, detailed explanation of project steps and tools, and supervising teams, tasks, and processes to ensure delivery on schedule. The impact on the operation of an organization involving key risks can be controlled by Project Managers. In addition, a manager can continuously supervise, estimate, and eliminate these risks throughout the life of a project.
The Project Managers encourage leadership, teamwork, and accountability with sufficient opportunities for team members to collaborate and decide the efficiency of the results. They are proficient in specific areas like strong critical thinking, time management ability, and communication skills without ignoring the significance of being transparent with stakeholders while working towards the desired outcomes.
Under this profession, a variety of digital tools, techniques, and frameworks are used based on the preference of the client, budget size, type, or size of the project, and the core tasks working in the industry sector include the following:
- Liaising with stakeholders
- Planning activities and their sequence
- Time estimates
- Cost estimates/ preparation of budgets
- Management of resources
- Risk analysis/problem-solving
- Process optimization/testing
- Team management
- Change management
- Monitoring and reporting on project progress and performance
- Quality control
- Project deliverables
What are the Roles of a Data Analyst?
- A data analyst performs the role of collecting and interpreting data to generate meaningful information. The responsibilities of a researcher involve analyzing data and drawing inferences to bring changes in the business.
- The country’s growth in the IT and Healthcare field has created extensive demand for professional data scientists all across the globe. It is mainly because the sectors revolve around data analysis to improve the decision that discloses the consumer preference and plugs products to the right crowd.
- Another notable reason behind the huge demand in India is the modernization of the industry and service sector, thereby ranking the country’s second-largest data informative center.
- Candidates can apply for data science courses. It provides in-depth learning and verifies knowledge with a certificate to prepare data for analysis, conduct basic visualization of data and finally construct the desired data.
- This certification course is highly valuable for money not only because of reduced time and cost requirements, but also knowledge of soft skills like teamwork, communication, and leadership.
The scope of data science is increasingly rising which makes it a lucrative professional career. Data sets collected from various sources are the chief reason behind the growth of skilled data science professionals. The process of a data analytics project has the following steps:
- Identification of the needs of the customer
- Creation of an action plan, where information flow signifies the strength of communication among team members
- Collecting data from APIs, flat files, database backups, and other data sources.
- Cleaning data to draw inferences since the initial data is unorganized and unfiltered.
- Generation of reports and visualizations to solve the defined problem.
Reasons to Pursue a Career in Data Science and Project Management
- Work in a variety of industries
The role of data analysts and project managers is available in several industries. With the help of an analyst, a company can use complex and unfiltered data to draw conclusions and make decisions. The role of a project manager in a company is equally significant enough. Companies are inclined toward hiring data professionals with skills in project management.
It is because skills in both fields support each other and ensure effective and efficient work. It helps to solve business problems, implement solutions, and monitor outcomes.
The recruiters can choose from a lot of spheres like healthcare, education, manufacturing, construction, technology, and many other sectors.
- Impact on business with data and project management skills
Data analysts can bring a direct impact on the industry with the skills attained from online data science certification courses. For instance, a researcher can develop a new product with the compilation and analysis of data.
- In real life, data scientists revolutionize the healthcare business through image analysis and drug research. The client service is refined through insights from past data of the patient.
- In the airline industry, flight path and route analysis are conveniently operated with advance prediction of delays and casualties to ensure the safety of passengers along with crew members.
The skill of a project manager can bring growth in businesses and organizations through planning and managing the internal project before the deadline. For instance, the manager ensures the completion of a project of a company with a rise in budget and efficient customer service.
- Opportunities to Advance Career
The career opportunities in both Project management and data science are high and rewarding. In the data science field, the roles are data analyst, data science technician, healthcare data analyst, and many more.
- The advancement in career is done through data science certifications like Certified Analytics Professional (CAP), SAS Certified Data Scientist, Open Certified Data Scientist (Open CDS), IBM Data Scientist Professional Certificate, Google Professional Data Analytics Certification, and Tensorflow Data Scientist Certificate.
- In the Project Management field, the roles are project manager assistant or junior project manager.
- Solid salary availability
The salary and employee benefits offered to data analysts and project managers are good enough to live a high standard of living. According to the Bureau of Labour Statistics, the average salary in data science and machine learning reaches approximately USD 176,213 for data scientists and USD 166,992 for data engineers.
Additionally, India has around 50,000 job vacancies in the data science field at an average salary pay range of INR 800,000. For an entry-level scientist, the salary starts from INR 500,000 and skilled professionals can receive INR 20,00,000 salary.
- Peace of mind and Job Security
The increasing demand for data science makes it a lucrative professional career, so job security is ensured with a good salary package. The future scope of the data analyst is positive, there is a predicted increase in employment rate by 36% from 2021 to 2031 by the US Bureau of Labour Statistics.
Project Management Tools used for Data Science Projects
A data researcher is dependent on project management techniques and methodologies to achieve deliverables, optimize processes, and fast-track business and team performance over time.
The discrepancy is eliminated using advanced project management tools with an assured high return on investment. Some project management tools/frameworks used by data professionals are-
- Agile/Scrum:
This framework allows team members to give priority to the unpredictable nature of data science projects with the below steps:
Plan, build, test, evaluate, adapt, and repeat until the desired outcome is reached.
- RAID Log:
RAID means Risks, Assumptions, Issues, and Dependency. It ensures that the data science learners are up to date with the project data.
This is done by the below steps: making assumptions during data preparation, planning the next steps, identifying risks, liaising with stakeholders, implementing, and supervising cross-team deliverables, evaluating, and keeping track of project achievements.
- CRISP-DM
This stands for Cross-industry Standard Processes for Data Mining. This approach comprises six phases that are user-friendly and widely used for data science and analytics projects. It involves comprehending the business problem and the data, data preparation or cleansing, data modeling, evaluation, and deployment.
From the above article, we can conclude that the roles and responsibilities of project managers and data analysts are on different paths. Project managers supervise the team and ensure that the intended goal of the project is completed efficiently within the specified timeframe. The main aim is the successful completion of tasks delegated to the team members of a specific department.
On the other hand, the role of a data analyst involves engagement with large amounts of data followed by the conversion of information into insights. It reflects the problem-solving and leadership skills of the researcher with an improvement of the organization.