Data Analyst vs Data Scientist: Key Differences

Suppose you are interested in jobs related to big data. In that case, there are 2 career paths that you can choose, namely data scientists and data analysts.

The two have a lot in common, so it’s sometimes hard to tell them apart. Let’s discuss these two professions to find out the differences between data analysts and data scientists!

Data Analyst vs Data Scientist: Job Overview

These two jobs are on the list of high-demand jobs and will be high-paying jobs in 2021.

In addition, according to the World Economic Forum Future’s Jobs Report 2020, they also rank high in increasing demand throughout the industry. From these facts, they have an essential role in the industry.

Before knowing the differences between these two professions, we must first understand the basic meaning. An analyst is a professional whose job is to collect basic information and identify trends, which can assist in determining business strategy.

In addition, they are more focused on providing statistical analysis that can solve problems.

Although almost similar, the data scientist profession is more concerned with designing algorithms and predicting models.

Therefore, his work is more time-consuming to create tools and automation systems and find methods to extract company information to solve complex problems.

Data Scientist

Data Scientist is a professional in analyzing, processing, and designing data models and algorithms to be interpreted into company plans or actions.

Data Scientists often deal with unstructured raw data or even intangible business problems, so their work requires tools and methods from Statistics and machine learning to streamline data.

Data Scientists need to be able to automate their own machine-learning models and algorithms that can handle unstructured data.

Data Analyst

A data analyst collects and interprets data to solve specific problems through data cleaning, transformation, and data modeling.

Data Analysts typically work with structured data to solve real business problems using tools such as SQL, R, or Python programming languages, data visualization software, and statistical analysis.

Data Analyst vs Data Scientist

At the previous point, we know these two professions’ basic understanding. To continue the discussion, let’s pay close attention to the explanation below regarding the differences between data analysts and data scientists:

1. Roles and Responsibilities

These two jobs have differences in roles and responsibilities. Data analysts are involved in finding reasons why something can happen, while data scientists are more concerned with what will and can occur in the future.

An analyst is responsible for drawing conclusions from various sources to find the best solutions and decisions in business.

They will present these conclusions in easy-to-understand reports such as graphs and descriptions. In addition, they also play a role in designing dashboards using Business Intelligence Software.

On the other hand, a scientist in this field is responsible for making future predictions and dealing with more complex problems.

They can handle both structured and unstructured raw information. A scientist also plays a role in analyzing the accuracy of information. Therefore, we can call them an advanced version of an analyst.

2. Skills

Being a data scientist and data analyst requires similar skills, but they have slight differences. Analyst jobs are more inclined to the business sector.

So it is necessary to have knowledge about business and the ability to make decisions in business strategy.

An analyst must have the ability to describe information and good communication skills to submit reports. Knowledge of statistics and mathematics is also needed by analysts in carrying out their work.

In addition, analysts often use tools such as Python, Excel, and Business Intelligence Software. In contrast to analysts, even though data scientists will also use Python. However, they will heavily use Spark, MySQL, TensorFlow, and Hadoop in their work.

A scientist in this field requires the ability to machine learning algorithms. There is a fact that his work can have a significant impact on search engines using algorithms. The algorithm itself requires sufficient resources to operate accurately. Therefore this work involves accuracy. This profession requires us to be experts in mathematics, statistics, and programming.

Skills Data Scientist Requires

  • Some must-have Data Scientist skills:
  • Good understanding of machine learning and algorithms such as k-NN, Naive Bayes, SVM, Decision Forests, and others
  • Have experience with toolkits like R, Weka, NumPy, MatLab, and others
  • Proficient in SQL, Hive, Pig
  • Experience in databases like SQL, Hive, Pig
  • Proficient in using NoSQL databases, such as MongoDB, Cassandra, Hbase
  • Have good applied statistical skills such as distribution, statistical testing, regression, and others
  • Have scripting and programming skills
  • Data orientation
  • Understanding of business
  • Good communication skills
  • Good leadership and teamwork skills

Skills required by Data Analyst

Meanwhile, generally, a Data Analyst needs to have several skills, such as:

  • Have the good mathematical ability
  • Have the ability to process data
  • Good analytical skills
  • Good communication skills (oral and written)
  • Critical thinking skills
  • Attention to detail

3. Education

We can see the difference between data scientists and data analysts from the education they take. Data scientists usually have taken master’s or doctoral programs, or researchers have experience of 5 years or more. They typically take data science, information technology, mathematics, and statistics programs.

Another case is with an analyst with a bachelor’s degree in finance, computer science, statistics, and mathematics. However, this job does not require you to come from that field. By taking the professional certification, you can still become an analyst.

4. Career Development and Domain

Data analyst vs data scientist have little difference in their career development. An analyst will start his career by holding a role in reporting and designing dashboards. Furthermore, they will be responsible for looking for strategies or things related to more profound analysis techniques.

When they have more than 9 years of experience, those with managerial interest will become analytics managers. At the same time, analysts continue their education and become scientists. We will find an analyst more often in the healthcare and hospitality industry.

The data scientist ladder is more open, as there are more positions they can fill within a company. In fact, the demand for this profession is more significant if we compare it with the available workforce.

Leave a Comment