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Data Scientist or Data Analyst?

Differences and how to choose what fits you best.

Photo by Benjamin Balázs on Unsplash
Photo by Benjamin Balázs on Unsplash

One of the confusing questions that you need to answer before you get into any type of job that requires dealing with data is, which career path should I choose? Which one will fit my personality and aspiration most?

Answering these questions is difficult because some terms are not easy to distinguish from others, so if you don’t know the difference, how can you make a decision? In my opinion, the most difficult roles to distinguish are a data scientist and Data Analyst.

For the longest time, back when I started my journey in data science, I thought they were the same thing but told differently. The fact that data science is a vague, broad term didn’t help with my confusion. After tons of reading and research, I could finally grasp the subtle difference between data science and data analytics.

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The truth is, data science and Data Analytics are interconnected terms; there is a lot of overlap between the two terms. Nevertheless, each path requires a somewhat different learning path and will provide different results.

To help you avoid any further confusion, I decided to write this article, clearing out the differences between the two terms, in definition, required skills, and role responsibilities. With no further ado, let’s get into it…

Data Science

Data Science is not just one role, and it is, in fact, an umbrella term covering different terms and sub-branches, like natural language processing, computer vision, machine learning, deep learning, etc.

However, if we want to put what a data scientist does in words, it will be something close to; a data scientist is a person with a curious mind that loves to ask questions in order to solve some problem. They rely on data to design algorithms, develop code and build models to reach actionable insights from this raw data.

The main goal of any data science project is to explore data, find patterns and trends, and using this information to predict future patterns and trends using different tools and techniques that the core of is often machine learning algorithms.

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Skills needed

Since data science is an interdisciplinary field, in order for you to be a successful data scientist, you will need to master several technical and soft skills. But mastery requires a long time; you can kickstart your career if you are comfortable with the fundamental knowledge needed to build any project. These fundamental skills are:

  1. Maths and statistical knowledge.
  2. Programming and software development.
  3. Data collection, cleaning, and exploration.
  4. Data visualization and storytelling.
  5. Familiarity with the core algorithms of machine learning.
  6. A basic understanding of business models and how they are developed.

Job responsibilities

As a data scientist or an expert in any of its subfields, you will be expected to solve complex problems using collected data to analyze, clean, explore, model and test. Your job role will mainly be to use different algorithms or design new ones to solve the problem at hand efficiently and quickly.

The insights collected from your model will be used to enhance or build new business models. So, your job will be critical for the success of some companies and how much profit they may obtain.

Data Analytics

Similar to data science, the term data analytics also covers different subfields, such as databases analyst, business analysis, sales analysis, pricing analyst, market research analyst, etc.

As a data analyst, your main goal will be to use the data provided to you to answer different business questions, like, what product sold best and why? If there was a drop in revenue, why did it happen and how can the company overcome it, and so on.

To reach an answer for these questions, the data analyst needs to be able to statistically analyze datasets, create tools to collect data and organize it and extract similar information from it in the future. In short, a data analyst’s job is to answer questions with unknown answers based on the current state of data and drive immediate actions.

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Skill needed

In order to succeed as a data analyst, you will need to be familiar with different types of knowledge. You don’t need to have an official degree of any type to become a data analyst but, having a background in the following skills is a must if you choose to pursue data analytics:

  1. Fundamental knowledge of maths and statistics.
  2. Data mining and data warehouse skills.
  3. Familiarity with data modeling techniques.
  4. Basic programming knowledge in R, SAS, and SQL.
  5. Database design and management.
  6. The basis of statistical analysis.

Job responsibilities

If you decide to pursue a career path in data analytics, your job responsibilities will center around either designing and managing efficient databases, use existing statistical tools to analyze and interpret some datasets, and answer some business-related questions based on your findings.

Another data analyst’s essential role responsibility is rot you to be able to report your finding in an efficient way that delivers your message in the simplest approach, so having some science communication skills can go a long way.

Takeaways

Getting into data science is a challenging task, but it gets even more challenging when you don’t know what the different terms in the field mean, not just within the field but also the name of the field itself. Perhaps the top of the list of the most confusing terms is data science and data analytics.

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Those two terms are so intercorrelated that they confuse any newcomer as well as people within the field itself. Because of the correlation between the two terms, reaching a concise, clear distinction between them is quite difficult.

But, despite the similarities, the two terms are quite different; they have different job responsibilities as well as different skill sets that one needs in order to get the job. In this article, we went through these differences, and hopefully, made things a little bit clearer, so when you decide on a career path, you know exactly what you need to succeed.


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