The Ultimate Guide to Choosing between Data Science and Data Analytics.
Data professionals are in high demand all over the globe due to the rise in big data. The roles of data scientists and data analysts cannot be over-emphasized as they are needed to support decision-making.
Students and individuals have a general misconception about both careers. This article will serve as an ultimate guide to choosing between Data Science and Data Analytics. At the end of this article, you will fully understand what it entails to be a data scientist or data analyst.
Before going into the main purpose of this article, what is data?
What do you mean by data?
Data is an exclusive collection of facts and statistics for reference and analysis.
- Data is a collection of discrete values that convey information, describing quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted. — Wikipedia
Data could be statistical, financial, scientific, cultural, geographical, transport, natural, or meteorological. It is collected through various techniques such as; surveys, observations, and measurements. Data collected is not useful until it is critically analyzed. Prior to analysis, the data has to be thoroughly cleaned to remove errors, outliers, duplicates, and other forms of irregularities.
Due to the spread of the web and smartphones, data is collated per second leading to the birth of big data. The rise of big data led to an increase in demand for data scientists and analysts as there is a need for the data to be worked on to support decision-making.
Some individuals are confused about the right path to choose between the two lucrative careers — Data Science and Data Analytics. This article will serve as an ultimate guide to choosing between these careers.
Data Science vs Data Analytics
Oftentimes, the role of a data scientist is confused with that of a data analyst. In reality, the nature of each role is different from the other. Let’s take a look at the responsibility of each role.
Responsibilities of a Data Scientist
A data scientist is a data professional with programming, analytical, and statistical skills to collect, analyze, and interpret data. The role of a data scientist also involves the use of advanced analytics techniques such as machine learning and predictive modeling. The major tasks of a data scientist are to gather and prepare data, use various types of analytical tools to detect patterns and trends and develop statistical and predictive models to predict future occurrences.
Data Scientists are often tasked with the responsibility of mining data for information to make inferences such as market trends, financial risks, cyber threats, and medical analysis.
Most times, they might not be provided with a specific business problem to solve. Hence, a data scientist should understand the data and the business critically well enough to formulate business problems to solve and deliver actionable insights.
Responsibilities of a Data Analyst
Data analysts, on the other hand, help businesses and organizations make data-driven decisions through their analytical skills. Their job is mainly to collect, process, analyze, and create detailed reports on data to meet business needs. They create reports, dashboards, and other data visualizations, providing actionable insights to business leaders to support decision-making efforts.
They are often provided with business questions to answer, theirs is to understand the questions and determine whether the data can answer these questions. They require basic programming skills in contrast to data scientists that require high expertise.
Now, let’s take a look at the technical requirements for each role.
Technical requirements for a Data Scientist
- High expertise in programming either in R or Python, or both.
- Familiarity with Databases; SQL for structured data, and NOSQL for unstructured data.
- Experience with machine learning frameworks for supervised and unsupervised learning.
- Experience with cloud platforms like; AWS, AZURE, etc.
- Experience with visualization tools like; Tableau and Power BI.
- Knowledge of big data platforms like; Hadoop and Apache Spark.
Technical requirements for a Data Analyst
- Familiarity with Databases
- Experience with analytical tools like; Microsoft Excel.
- High proficiency in visualization tools like; Tableau, Google Studio, and Power BI.
- Basic programming knowledge in R or Python.
Moving on, let’s take a look at the salary range for both roles.
Salary differences between Data Scientists and Data Analysts
According to Glassdoor, the average salary of a data scientist in the US is $108, 303 per year while that of a data analyst is $75, 692 per year.
Conclusion
Now, we have differentiated between Data Science and Data Analytics, it now depends on you to choose the career path best for you. Although both careers overlap somewhat, each is distinct in its way.
It is evident that both careers are of great importance in today’s business world as they support decision-making. It is now up to you to make a choice based on your interests.
