Data Science Careers - 3 Top Benefits of a Data Science Career

Why Build a Career in Data Science or Analytics? 3 Top Benefits of a Data Science Career

In the last ten years, the requirement for data science abilities and data-oriented choices has increased drastically, along with the availability of data. The impact of the COVID-19 pandemic on businesses and customers has increased the prominence of data analytics and science, making them necessary for guiding decision-making in various sectors and roles.

“Data science is a 21st century job skill that everybody should have.”

Eric Van Dusen, the curriculum coordinator for data science education at UC Berkeley, believes that data science is a necessity for anyone in the 21st century to have in their skillset. “Every field. I instruct pupils that it is necessary for them to emerge with this collection of abilities. You will be much more influential in whatever profession you choose.

Data science is a field that is receiving a lot of attention, and it provides generous wages and great chances for success.

1. Earn a High Salary

Robert Half’s information reveals that the average salary for the beginning of a data scientist’s career is $95,000, which is almost two times more than the median salary for the United States. The typical wage for a data analyst, which is a lower-level job, is over $70,000 – much more than the median salary in the United States.

A Burtch Works study revealed that the most significant element impacting data science salaries is prior work experience. Data scientists who have been working in the field for seven or more years typically bring in an average of $129,000 per year. Data scientists with many years of experience and a leadership position can make more than $250,000. It is also crucial to take into consideration the level of education, size of the business, and industry when deciding on data science wages.

2. Solve Complex Problems

If you take pleasure in tackling intricate, practical issues, you will never be uninterested as a data science expert. Your main duty in this role is to uncover solutions and comprehension by studying and dealing with large amounts of unprocessed information. Here are some instances of difficulties in the business world that you will experience.

“Being able to extract information from data is actually a very powerful position to be in.”

Philippe Rigollet, an associate professor in the MIT mathematics department and Statistics and Data Science Center, commented on the renowned John Tukey’s words: “Being a statistician provides the benefit of being able to explore in all sorts of different areas.” I guarantee that for whatever area you find intriguing, data science can be utilized to enhance it. Gaining insight from data is a very advantageous position to be in, since data is being gathered in all aspects of life, from marketing to medicine and even to sports and amusement.

3. Avoid Job Automation

Data science roles, particularly data analysts, are at very low risk for automation for a few reasons:

1. The need for data science positions is increasing at a rate of 50% on average.

2. Very few platforms can produce sophisticated analyses.

3. Data scientists are the primary individuals responsible for automating processes.

Growing Your Data Science Career: From Analyst to Data Scientist

You can use your data science skills to advance your career in two main ways: become a data science expert, such as a data analyst, database developer, or data scientist, or shift into a job that uses analytics, such as a functional business analyst or data-driven manager. Both jobs in the field of data analytics involve having a strong grasp of fundamentals such as programming, data handling, extracting useful information, and displaying it graphically.

Despite the two distinct paths that are available, the ever-changing nature of this relatively new area of work allows for a lot of flexibility when it comes to job opportunities. Data analysts can progress into either data science or data system developer positions if they enhance their knowledge and skills. By increasing expertise in AI, statistics, data management and big data analytics, a data analyst can successfully move into a data scientist position. A data analyst can take their knowledge of Python, relational databases, and machine learning and use it to transition into a data system developer. A lot of these abilities can be obtained either by gaining work experience or independently through attending data analytics boot camps or taking online data science classes. The main emphasis of this guide is on positions related to data science.

Data Scientists vs. Data Analysts: What’s the Difference?

The abilities and duties associated with starting-level data science positions and data analysts tend to be similar. It is necessary that one possesses both statistical awareness and programming aptitude for either of the positions. However, there is a clear difference in the focus.

What Does a Data Scientist Do?

Data scientists use data to investigate and answer questions related to the business. They use data to develop new product components and typically engage in more modeling and exploratory research. They will invest a significant amount of effort in tidying the data so that it can be employed successfully for the models and AI programs. When you view a tailored selection of series on Netflix, that is the consequence of data science and machine learning algorithms.

Furthermore, a part of data science is devoted to predictive analysis. Rafael Lopes, a Partner Solutions Architect at Amazon Web Services and an instructor for Getting Started with Data Analytics on AWS, discussed the complexity of predictive data analysis. According to him, this type of analysis involves looking into the future by examining data from the past, or by combining information from several different datasets and sources. Essentially, it attempts to forecast what will happen next based on prior events. Neural networks, regression, and decision trees are frequently utilized in diagnostic assessment.

Job Roles in Data Science

1. Data Analyst

Analysts who work with data have a range of duties, such as creating visuals, rearranging and formatting large sets of information, and manipulating data. They are required to do searches in the databases occasionally. An essential capability for a data analyst is maximizing efficiency. This is due to the requirement of constructing and adjusting programs that are able to extract data from some of the most extensive data banks without damaging the information.

Few Important Roles and Responsibilities of a Data Analyst include:

Extracting data from primary and secondary sources using automated tools
Developing and maintaining databases
Performing data analysis and making reports with recommendations
Analyzing data and forecasting trends that impact the organization/project
Working with other team members to improve data collection and quality processes

How to Become a Data Analyst? Some of the most in-demand skills for data analysis include SQL, R, SAS, and Python. Getting officially certified in these areas can greatly improve the chances of your job applications being accepted. You should also have good problem-solving qualities.

2. Data Engineers

Engineers who specialize in data create and evaluate expansive Big Data settings for organizations so that data scientists may execute their computations on systems that are in peak condition and highly advanced. Data engineers replace the present systems with more up-to-date or modernized variants of the current technologies in order to enhance the productivity of the databases.

Few Important Roles and Responsibilities of a Data Engineer include:

Design and maintain data management systems
Data collection/acquisition and management
Conducting primary and secondary research
Finding hidden patterns and forecasting trends using data
Collaborating with other teams to perceive organizational goals
Make reports and update stakeholders based on analytics

How to Become a Data Engineer? If you’re aiming for a job as a data engineer, then you should be familiar with Hive, NoSQL, R, Ruby, Java, C++, and Matlab, as these are all technologies that require hands-on practice. It would be beneficial if you possess the capability to cooperate with widely used data APIs and ETL tools, etc.

3. Database Administrator

A database administrator’s role is clear: they are in charge of making sure all databases in a business are functioning properly, and providing or revoking the service to staff based on their needs. They are also responsible for database backups and recoveries. Few Important Roles and Responsibilities of a Database Administrator include:

Working on database software to store and manage data
Working on database design and development
Implementing security measures for database
Preparing reports, documentation, and operating manuals
Data archiving
Working closely with programmers, project managers, and other team members

How to Become a Database Administrator? A database administrator must possess certain necessary skills and abilities, such as backing up and restoring databases, protecting data, creating data models and designs, etc. Being able to handle crises effectively is an added advantage.

4. Machine Learning Engineer

Machine learning engineers are in high demand today. However, the job profile comes with its challenges. In addition to possessing an extensive understanding of some of the most powerful technologies such as SQL and REST APIs, machine learning engineers are expected to conduct A/B testing, construct data pipelines, and apply common machine learning algorithms including classification and clustering.

Few Important Roles and Responsibilities of a Machine Learning Engineer include:

Designing and developing Machine Learning systems
Researching Machine Learning Algorithms
Testing Machine Learning systems
Developing apps/products basis client requirements
Extending existing Machine Learning frameworks and libraries
Exploring and visualizing data for a better understanding
Training and retraining systems
Know the importance of statistics in machine learning

How to Become a Machine Learning Engineer? Initially, it is essential that you possess a profound understanding of some of the technologies such as Java, Python, JS, etc. Additionally, you need to have a firm comprehension of mathematics and statistics. Once you have acquired proficiency in both areas, it is much simpler to succeed in a job interview.

5. Data Scientist

Data scientists must be aware of the difficulties that businesses face and provide the most excellent answers by means of data analysis and data handling. For example, it is anticipated that they will do predictive analysis and carefully examine “disorganized/unstructured” data to provide useful information. They can also accomplish this by recognizing tendencies and models that can aid the organizations in making better choices.

Few Important Roles and Responsibilities of a Data Scientist include:

Identifying data collection sources for business needs
Processing, cleansing, and integrating data
Automation data collection and management process
Using Data Science techniques/tools to improve processes
Analyzing large amounts of data to forecast trends and provide reports with recommendations
Collaborating with business, engineering, and product teams

How to Become a Data Scientist? To achieve success in the field of data science, one must have proficiency in the use of R, MatLab, SQL, Python, and other related technologies. A more advanced qualification in mathematics or computer engineering, etc. can be beneficial.

6. Data Architect

A data architect is responsible for designing the framework for data management so that databases can be efficiently interconnected, consolidated, and safeguarded with the strongest security protocols. They make sure that the data engineers have the most effective and efficient tools and systems to utilize.

Few Important Roles and Responsibilities of a Data Architect include:

Developing and implementing overall data strategy in line with business/organization
Identifying data collection sources in line with data strategy
Collaborating with cross-functional teams and stakeholders for smooth functioning of database systems
Planning and managing end-to-end data architecture
Maintaining database systems/architecture considering efficiency and security
Regular auditing of data management system performance and making changes to improve systems accordingly.

How to Become a Data Architect? To have a successful career in data architecture, one must have a good understanding of data warehousing, data modelling, and ETL. Additionally, it is important to possess knowledge of Hive, Pig, and Spark.

Read Also: 15 Most Common Data Science Interview Questions

7. Statistician

A statistician is someone who is knowledgeable in the area of statistical concepts and data organization, as suggested by the name. They not only draw out and present beneficial knowledge from the data collections, but they also aid in the development of new techniques for the engineers to put into practice.

Few Important Roles and Responsibilities of a Statistician include:

Collecting, analyzing, and interpreting data
Analyzing data, assessing results, and predicting trends/relationships using statistical methodologies/tools
Designing data collection processes
Communicating findings to stakeholders
Advising/consulting on organizational and business strategy basis dat
Coordinating with cross-functional teams

How to Become a Statistician? A statistician has to have a passion for logic. They demonstrate proficiency with multiple database technologies, including SQL, data extraction, and the various artificial intelligence tools.