Data Analyst vs. Data Scientist: Main Difference

Data Analyst vs. Data Scientist. How are these two job roles different from each other? If I am extracting data from a large dataset, am I a data scientist or a data analyst? If I am trying to make an estimate of likely sales of a particular type of candles for the upcoming festive season, am I working as a data scientist or a data analyst?

A lot of industries are beginning to understand the value of data. According to the U.S. Bureau of Labor Statistics (BLS), jobs related to data, including data science and data analytics are projected to grow at 36% by the next ten years. So, if you are planning to pursue a data-driven career, the time is just right for you.

To start a career in data science candidates can consider pursuing a course that can equip them with the right skills. Interview Kickstart, a global leader in career upleveling, offers courses in data science and data engineering.

One can also consider opting for a course in data analytics to succeed in tech-centric interviews with top IT companies in the world.

Interview Kickstart also offers courses in Machine Learning for career aspirants to specialize in a subdomain of data science.

Also read: Data Analyst vs Business Analyst

So, data analyst vs data scientist, which career is the right fit for you?

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Data Analyst vs Data Scientist. Who is a Data Scientist?‍

A data scientist is a professional who performs activities like data mining, data forecasting and anomaly detection to derive actionable insights for the stakeholders to make informed decisions.
Data scientists perform a host of tasks from finding patterns in large data sets, training machine learning models and deploying AI applications.

With a lot of industries starting to take a data driven approach in business decision making, data science is considered to be a highly in-demand profession with immense opportunities and lucrative compensation.

Data Analyst vs Data Scientist. Who is a Data Analyst?

Data analysts focus on querying, analyzing, and visualizing data sets. With the use of various tools (Tableau, Power BI, etc.) and programming languages (SQL, Python and R) they derive meaningful insights and reports for the stakeholders to make informed business decisions.

Many consider data analysis as a specialized task under the data science umbrella. Data analysts transform raw data into actionable intelligence, thus playing an important role in problem solving and decision making for organizations.

Data Analyst vs Data Scientist: Job Responsibilities of a Data Scientist</h2>

Data scientists perform experiments to see if the desired outcome can be achieved using the data they have. To derive these outcomes, they follow a seven phase process called the Data Science Life Cycle:

Phase 1: Problem Identification: Identify a problem or opportunity that needs to be addressed‍

Phase 2: Data Mining: Extract information relevant to that problem or opportunity from the large data sets.‍

Phase 3: Data Cleaning or Data Preprocessing: Fixing of errors, inconsistencies and redundancies in the data sets.

Phase 4: Data Exploration: Analyze the cleaned data set to gain a deeper understanding of its characteristics through descriptive statistics, data visualization, and exploratory data analysis (EDA) techniques.‍

Phase 5: Featuring Engineering: Transform existing features of a data set by performing procedures like addition, deletion, and mutation to improve the training of machine learning models.  ‍

Phase 6: Predictive Modeling: Use the transformed data to predict future outcomes by performing procedures like data selection, data training, data evaluation, and data optimization. ‍

Phase 7: Data Visualization: Represent devised insights in the form of charts, tables and graphics to make recommendations to stakeholders.

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Data Analyst vs Data Scientist: Job Responsibilities of Data Analyst

Data analysts translate available data into meaningful insights that create business impact. To achieve this objective, they follow the below-mentioned four-step process:

Data Collection and Interpretation: Data analysts first collect raw but relevant data from various sources such as websites, social media channels, and business applications. Subsequently, they work closely with other departments to understand their requirements.

Data Cleaning, Organization and Validation: Cleaning the data is essential before its analysis can be performed. Data cleaning involves fixing incomplete, duplicate and inaccurate data, structuring organized data elements and resolution of data quality issues.

Data Mining for Actionable Insights: Data analysts uncover insights within the cleaned data with the use of various data modeling and data visualization tools. These techniques include regression analysis, ANOVA tests and decision trees to name a few.

Communication of Findings: After the insights are unearthed, the data analyst’s next job is to convey these findings to stakeholders. They present the findings with the help of data visualization tools like Tableau, Power BI and Google Studio.

Data Analyst vs Data Scientist: Key Differences

Both data analysts and data scientists perform distinct yet important roles in an organization. Now let’s identify the key differences between the responsibilities and skills of data analyst vs data scientist.

Data Analyst vs Data Scientist: Comparison of responsibilities

The responsibilities of a data scientist are more inclined towards helping in making predictive decisions for the growth of the company. The analyst works on data to solve the current issues a company is facing.

Data Analyst vs Data Scientist: Comparison of Proficiency Levels of Skills

Whereas the skill sets of a data analyst and a data scientist are similar, proficiency levels vary significantly. For instance, a data analyst works more with the data visualization tools and a data scientist on data modeling and algorithms. ‍

Source: Datacamp.com

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FAQs: Data Analyst vs Data Scientist‍

Data analyst vs. data scientist, which one is better?‍

The job role of a data scientist is of a more complex nature and requires a higher level of expertise than the job role of a data analyst. Hence, a data scientist gets a higher pay than a data analyst. In small organizations, the two job roles are used interchangeably as the same employee(s) performs all the functions of a data scientist and a data analyst. In larger organizations, their roles are more clearly defined and demarcated.

Can a data scientist apply for a data analyst job?

Transitioning from the role of a data scientist to a data analyst is easier. A data scientist generally can perform all the duties of a data analyst but a data analyst can’t perform all the duties of a data scientist.

Is data analytics a difficult career to pursue?|
Data analysts are under constant pressure to deliver actionable insights to other stakeholders. The high volume of data that is required to process can be overwhelming, which can lead to higher stress among data analysts.

Are data analysts required to code as well?

Data analysts are required to have knowledge about programming languages like Python, R and Structured Query Language (SQL). So, as and when required they are required to code as well.

Also read:

‍Data Scientist vs. Machine Learning Engineer‍

What is the R Language? What Makes it Essential for Data Scientists?

‍What Is Data Engineering: A Complete Guide in 2024

‍Data Engineer vs. Data Scientist — Everything You Need to Know