In the information age, data is power.

Besides using big data to make better decisions and solve business problems, organizations also use it to generate business intelligence, make informed decisions, and measure performance.

While data can be easily accessed, it needs to be processed, organized, and presented in a relevant and meaningful context. This is an area where Data Scientists and Data Analysts add value.

The rising need for organizations to gather and use data to fuel decisions has made data science roles one of the fastest-growing careers.

That means that the number of roles in the data space is exploding. However, the responsibilities of these job titles are often confused.

In this article, we’ll discuss in detail some key responsibilities that differentiate a Data Analyst from a Data Scientist.

We will also explore the job descriptions, required skills, and goals of each of these data-related careers. Lastly, we are going to examine what both roles have in common and the salary range of each of these roles.

Data Analytics and Data Science – An Overview

If you are planning to start a career in a data-focused field, you may have considered working as a Data Analyst or a Data Scientist.

Although each organization has a unique way of defining these roles, I think it is important that you know the difference.

The mechanics of Data Science and Data Analytics are significantly different. Yet, both Data Science and Analytics roles use similar programming languages and tools in their daily tasks.

Data Analytics involves sorting through massive amounts of data using statistical tools to analyze data, and ultimately provide findings to stakeholders.

A Data Analyst uses data visualization tools to present insights and eliminate guesswork from the company’s decision-making process.

Data Science is mainly centered on developing algorithms and predictive models for extrapolating data and creating data processes.

A Data Scientist uses advanced programming to create new methods by which data can be captured and analyzed.

Now let’s dig into the details of both of these roles.

Data Analysts: How Do They Add Value?

Data Analysts are generally in charge of collecting, cleaning, and organizing data which is used in drafting reports.

These reports reveal patterns and trends which guide stakeholders in making business decisions.

Data Analysts also design databases and explain data sets with advanced statistical tools.

In short, Data Analysts use various tools to analyze concise sets of data to answer important questions.

For example, a Data Analyst working in an e-commerce firm should be able to provide answers to why sales have plummeted in the last month.

Here are some of the tasks a Data Analyst will carry out on a day to day basis:

• Collect, clean, and organize raw data
• Use SQL to query and gather information across databases
• Process data and derive insights from massive datasets
• Use data visualization tools to create dashboards, and
• Liaise and collaborate with multi-disciplinary teams within the organization

What Skills Should a Data Analyst Have?

Generally, a Data Analyst should have an educational background in STEM (Science, Technology, Engineering and Math), with a master’s degree in analytics.

However, many employers do not rule out candidates without an advanced degree for this role.

In addition to the qualifications previously mentioned, here are some skills Data Analysts must have to drive efficient analytics in an organization:

• Data Mining and Data Warehousing
• Database Management
• Knowledge of analytics tools such as R, Python, and SQL
• Knowledge of Machine Learning and Artificial Intelligence
• Statistical Analysis, and
• Data Modeling

Data Analyst Goals Within an Organization

The core objective of data analytics is to provide answers to important business questions.

With the skills of a Data Analyst, companies can make their operations data-driven.

A data-driven organization can track its performance and maximize its areas of strength.

For instance, a Data Analyst can produce reports that reveal the reasons behind the success of a product recently launched by a company.

Data Scientists: How Do They Add Value

Besides performing the duties of Data Analysts, Data Scientists perform more in-depth tasks such as optimizing machine learning models.

Data Scientists focus on the “big picture” by designing experiments and training machine learning (ML) models to produce more accurate predictions in the future.

By leveraging their Data Analytics and Software Engineering skills, Data Scientists can carry out data projects from start to finish.

Here are some tasks Data Scientists perform within an organization:

• Use SQL to gather and query databases and gather complex data sets
• Develop data collection, data modelling, and metrics creation programs
• Evaluate the efficiency of data-mining techniques and the precision of data sources, and
• Communicate trends, patterns and metrics to stakeholders in a relatable manner

What Skills Should a Data Scientist Have?

In addition to an advanced degree in Data Science, employers seek candidates who are already experts in analytics with vast knowledge of the latest tools and technologies in Data Science.

Here are specific qualifications that employers look for when hiring Data Scientists.

• Analytical and strong problem-solving skills
• Knowledge of Machine Learning and Artificial Intelligence
• Strong Written and Oral Communication skills
• Knowledge of programming languages like Python, SQL, SAS, Javascript, and Software Development
• Experience using data computing tools such as Hadoop, Gurobi, and more
• Data visualization and presentation skills, and
• Statistical Modeling and Predictive Analysis

Data Scientist Goals Within an Organization

Data Scientists add value to an organization by examining data from various sources and creating means for deriving insights that can optimize business processes.

With the right tools, a Data Scientist can manipulate data to provide useful intelligence that can point the organization in the right direction.

How These Job Roles Vary

Both Data Analysts and Data Scientists are required to have analytical and statistical skills to gather, analyze, and translate large data sets.

However, having programming skills is a must for Data Scientists, as they often need to write new algorithms and data models to apply to data sets.

Also, Data Scientists work on undefined data from various sources while Data Analysts focus on defined data from only one source.

Although both Data Scientists and Data Analysts work with data, the objective of each role is very different.

Data Analysts are concerned with getting valuable information from raw data, while Data Scientists are constantly working on improving the process of and getting accurate insights from data.

Data Analyst and Data Scientist: Common Responsibilities and Skills

A career in Data Analytics or Data Science requires an educational background in any STEM field or statistics.

However, you need an advanced degree in Data Science to give the best chance to earn a job as a Data Scientist.

In most cases, Data Scientists have worked as Data Analysts at some point in their careers.

In terms of skills, the requirements for both roles are also very much similar.

Both Data Analysts and Data Scientists have the responsibility of mining and analyzing data. They are also both skilled in data warehousing, SQL, math, statistics, and data visualization.

How Much Do Data Analysts and Data Scientists Earn?

Data Scientist and Data Analyst roles are two of the best paying roles in tech.

Glassdoor estimates the average salary of a Data Scientist to be $79,232 US dollars per year.

A Data Analyst typically earns lower than this with an average salary of $57,328 per annum.

This is not surprising as the Data Analyst position is more of an entry-level role that does not mandate years of experience.

Wrapping Up…

A lot of organizations are leveraging data for their overall success.

This has made data-focused roles some of the most rewarding jobs in the market.

Before you choose a role in the Data Analytics or Data Science field, you need to define your career path.

Your interests, educational background, skills and long-term goals, can help determine which role suits you best.

What I have explained so far shows that you can choose to start your career in Data Analytics and aim for the role of a Data Scientist.

However, to advance, you have to acquire the skills that are required for a Data Scientist role.