Data has been a key to the growth of companies in recent years. There are various job profiles such as data analyst, data engineer, data scientist, business analyst, etc that help gain useful insights from raw data by contributing in one way or another. In this article, we will discuss the job responsibilities, salaries, and skills of data analysts and data scientists. Additionally, we will also have a discussion on data analyst vs data scientist to compare the perks of choosing a career out of these two.

Who is a Data Analyst?

A data analyst is a professional who takes a dataset and analyzes it using various statistical and machine learning techniques to produce useful insights. The data analyst is also responsible for describing the insights to the management with the managerial implications of the outputs.
With statistical analysis, the data analyst tries to help find answers to certain questions to solve problems in the business. Normally, the data analyst is part of an interdisciplinary team in an organization that focuses on collecting, preprocessing, and analyzing data for the purpose of gaining insights.

Who is a Data Scientist?

A data scientist is a person who is typically involved in creating data analysis tools. Data scientists design data modeling processes, formulate algorithms, and create data analysis models.
A data scientist focuses on designing tools to analyze data, and creating data frameworks and automation systems to facilitate better data analysis. Thus, you can say that a data scientist creates tools for data analysis that are then used by data analysts.

Data Analyst vs Data Scientist: Education

Data analysts and data scientists both need a sound knowledge of statistics, mathematics, and programming concepts.
To become a data analyst, you need to have a bachelor’s degree in computer science, mathematics, or statistics. You can also become a data analyst without having a degree in these domains. However, you will need the knowledge of these domains for sure to excel in a data analysis career.
A data scientist is more qualified than a data analyst. To become a data scientist, you need to have a sound knowledge of data modeling, machine learning, and other skills along with statistics and mathematics. To excel as a data scientist, you need to have a master’s degree or Ph.D. in one of these fields. Without this, you are very unlikely to enjoy being a data scientist.

Data Analyst vs Data Scientist: Skills

Both data scientists and data analysts need similar skills. You need to be good at problem identification, problem-solving, pattern identification, strong communication skills and data intuition. Apart from these, you need the following skills to excel as a data scientist or data analyst.

Statistical analysis and Probability

Data analysts today use statistical processes, and algorithms to extract useful information from a large source of data. This useful information is used to make predictions, and estimations using probability. Thus, you need to know about the statistical tools to apply them correctly.
As a data scientist, you need to design data analysis processes and algorithms to analyze the data. Therefore, you need a much better understanding of statistical concepts so that you can build algorithms that work well with different kinds of data.

Machine Learning

The majority of machine learning applications are built on models using statistical analysis concepts with machine learning algorithms . Calculus and algebra are the core of learning machine mathematics. This mainly includes processes like K-nearest neighbors, Random Forests, Naive Bayes, Regression Models, etc.
As a data analyst, you only need to know how to use the machine learning algorithms for data analysis. However, if you are a data scientist, you need to understand the implementation and working of the machine learning algorithms to design better data analysis tools.

Programming

After the information has been extracted from data, you need to create insights to be able to assist in decision making. Programming is the way to do that. To use the machine learning algorithms for creating data analysis tools, you need to learn to code.
One can say that Data science is essentially programming combined with statistics and machine learning. Although there is no hard and fast rule of which language is better, Python takes its leading place in being the most convenient. The other languages that are also used by data scientists are R, SQL, Julia, etc. To start with, you can learn python if you want to be a data scientist. Python provides us with different libraries and frameworks that will make your life easier.
If you want to be a data analyst, you may opt to not learn to code. There are many other tools to analyze data that don’t need the knowledge of coding. However, I would suggest you learn at least one programming language for better career prospects.

Database Management

When we are talking about data, it becomes completely mandatory for the candidate to be aware of database management as it helps in storing, manipulating, and retrieving data at any time.

Data Cleaning

Data cleaning skills are used to prepare raw data for analysis and create insights after being cleaned of irrelevant information. This makes it easy for the data analyst to focus on the correct data without wasting much processing time, money, and effort.
If you are a data scientist, you also need to learn data preprocessing to gain the ability to create better data models.

Data Visualization

A good data analyst is a person who knows how to craft a beautiful story out of the data. Data may not always be in a form that is understandable by everyone. This skill makes the data scientist a wizard who can create understandable, visual information for the decision-makers of the company. Some of the data visualization tools that you should be familiar with are Tableau, PowerBI, Google Analytics, MS Excel, Fusion Charts, and SPSS.
As a data scientist, you also need to have a knowledge of data visualization tools so that you can explain the insights to management if needed.

Data Analyst vs Data Scientist: Responsibilities

The responsibilities of a data analyst include the following tasks.

• Querying data from the database using SQL.
• Performing data analysis and producing results for a given problem. Generally, you will use different descriptive, predictive, prescriptive, or diagnostic tools to analyze data.
• Using the results obtained from data analysis to create dashboards to explain data to other stakeholders.
• Explaining the results to the management using the dashboards.

Typical job responsibilities of a data scientist include the following tasks.

• Building ETL pipelines.
• Data mining.
• Data cleaning
• Data scrubbing
• Statistical analysis of data.
• Creating data analysis tools that automate the process of data analysis.
• Developing big data infrastructures with tools such as Hadoop and Apache Spark.

Data Analyst vs Data Scientist: Salary

The average base pay of data analysts in the US is around $70,000 per year. It may vary from $45,000 per year to $110,000 per year based on the location and experience.

Data scientists, due to their increasing job responsibilities, earn more than data analysts. The average base pay for a data scientist in the US is $117,000 per year. It may vary from $82,000 to $210,000 per year based on your job location and experience.

Data Analyst vs Data Scientist: What Should You Become?

A data scientist has more responsibilities than a data analyst. If you want to be a data scientist, you also need to learn a lot more skills compared to a data analyst. However, the perks of becoming a data scientist are great when compared to a data analyst. The average salary of a data scientist is the highest salary of a data analyst. Thus, becoming a data scientist should be your choice if you want to earn more.

Initially, you can start with learning the skills related to data analysis. After becoming a data analyst, you can choose to learn more skills in order to become a data scientist.

Conclusion

In this article, we had a discussion on data analyst vs data scientist. We compared the salaries, education, job responsibilities, and skills of both professions.
I hope you enjoyed reading this article. To learn more about data science and analytics, you can read this article on regression in machine learning. You might also like this article on how does coding work if you aren’t very much into programming.
Stay tuned for more informative articles.
Happy Learning!

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