What’s the difference between a data scientist and a data science analyst? The discussion is a hot topic in today’s digital world. Students and those new to the field are often confused by the two positions. That’s why it’s essential to examine the job description, profile, and requirements of the two roles.

If anyone wants to pursue a career in one of these areas, elaborating on the difference helps push them to the right decision. While there are some similarities between data analysts and data scientists, there are many ways in which they differ.

As the name suggests, a data analyst analyzes and reports on datasets. A data scientist conducts research and examines every aspect of data to understand it to its core.

Let’s examine the skill sets, roles, and responsibilities of data scientists and data analysts to understand their similarities and differences.

## What is a Data Scientist?

Data Science is the hottest job of the decade and is the most widely used technique among machine learning and artificial intelligence. Data science is a combination of machine learning, artificial intelligence, mathematics, and statistics. The job consists of mining a large amount of structured and unstructured data to solve complex problems and identify patterns, insights, and correlations within the datasets.

### Roles and Responsibilities

The central role of a data scientist starts where the responsibility of a data analyst ends. Day to day roles and key responsibilities of a data scientist are:

- Cleansing and pre-processing the data.
- Identifying new features that can add value to data.
- Analyzing the requirements of the business and identifying ways to reach their goals.
- Utilizing machine learning algorithms to identify patterns or trends while predicting future events.
- Optimization of predictive models and hyper-tuning them appropriately.
- Creating stories out of data and producing interactive visualizations to translate findings with your team and stakeholders.

### Skillset and Tools

There are many skills required to become a data scientist. From programming to linear algebra, statistics to ML algorithms, deep and vast knowledge are necessary to fulfill the role of a data scientist.

Let’s explore the skill set and tools required to become a data scientist.

**Programming skills**

Programming skills are a must to become a data scientist. Python and R are two languages that are in demand for this role. But experienced data scientists often say that businesses and experts favor Python. There are many free sources like Kaggle and DataCamp, where you can quickly learn Python and R.

**SQL**

SQL, or structured query language, is an interface to access databases. Software stores data in databases, so accessing these interfaces is a necessary skill to learn. Refer to practical and straightforward platforms like w3schools or Kaggle to learn SQL skills.

**Mathematics and statistics**

There are areas of mathematics that data scientists need to be proficient. Hands-on experience in linear algebra, matrices, probability, and statistics is a must. Look to your 11th, 12th, graduate, and post-graduate mathematics books to practice and understand these concepts.

**Machine learning algorithms**

Many machine learning algorithms like random forest, logistic regression, support vector machine, linear regression, and more are used by data scientists daily. They also use ML algorithms for modeling and predictions of unseen data. Machine learning algorithms can be self-taught using platforms like Coursera, Kaggle, and DataCamp.

**Deep learning**

Neural networks, NLP (natural language processing), and computer vision are advanced methods of deep learning implemented by data scientists. Advanced libraries of neural networks make modeling and predictions easy and fast.

For example, BERT is a pre-trained open-source machine launched by Google AI in 2018. It is widely used for advanced NLP problems. More recently, Google unveiled a 2020 machine learning model for Indian languages named MuRIL.

## What is a Data Analyst?

A data analyst’s job is to analyze and visualize data. Data analysts create data reports that are to be understood by non-technical workers in layman language. Companies of various backgrounds, from healthcare providers to retail brands, can benefit from the work of data analysts.

Data analysts spend their time gathering data, preprocessing data, and converting their findings into beautiful insights that help businesses improve.

### Roles and Responsibilities

A data analyst plays a vital role in a company. They sometimes wear the hat of business analysts as well. Core responsibilities of a data analyst are:

- Querying data from databases using SQL.
- Data cleansing, formatting, and processing.
- Exploratory data analysis and interpretation of data using statistical techniques.
- Developing actionable insights in the form of visualizations and reports.
- Working with teams and stakeholders to define business goals and requirements.

### Skillset and Tools

Hands-on reporting and visualization tools are the trademarks of data analysts. Basic knowledge of programming languages like R and statistics knowledge are valuable additions. Let’s examine the skill set and tools required to work as a data analyst.

**SQL**

Data analysts’ first job is to gather data from multiple databases and translate it into a structured format. Data collection is achievable using SQL. However, data analysts have more advanced knowledge of SQL than the intermediate-level knowledge possessed by data scientists.

**Statistics**

Knowledge of statistics is a must if you are working as a data analyst. Statistical analysis is critical in understanding data, finding relationships within data, and developing hypotheses and theories. When forecasting sales and segmenting a potential customer base, statistics skills come in handy.

**Excel**

Microsoft Excel is a potent tool used by data and business analysts. It might feel old school, but its capabilities are vast. Users need to invest some time to master the software. Excel is also an excellent tool for creating data visuals.

**Visualization tools**

Experience with tools like Tableau, Google Data Studio, or any other visualization tool is a pre-requisite in the data analyst profile. They are platforms that can paint pictures of the insights developed from data analysis.

There are a variety of visualization formats like bar plots, pie-charts, and scatterplots that are used by analysts to showcase data in a meaningful manner. To learn more about data visualization and different visualization tools, click here.

## Similarities Between Data Analysts and Data Scientists

We discussed the skills and responsibilities of data analysts and data scientists in the last section. Now let’s examine the similarities between the two.

Skills like statistics, SQL, data visualizations, analyzing data meanings, and deriving data insights are common among data scientists and data science analysts. Both of these roles aim to find insights from data that are beneficial to business goals.

## Key Differences Between Data Analysts and Data Scientists

Predictive modeling, advanced tools, and programming are the difference between data scientists and data analysts. Data analysts work on past and current data, while data scientists work on predicting scenarios for the future. Here’s a summary of each role:

Features | Data Scientist | Data Analyst |

Description | A data scientist’s job is to predict future trends and patterns from data. | A data analyst’s job is to analyze and discover patterns from data. |

Scope | Works on big data | Works on small data |

Type of Data | Works on both structured and unstructured data | Works on structured data |

Skills | Mathematics, statistics, programming skills, machine learning algorithms | Basic statistics and mathematics, basic programming, analytics |

Tools | Python or R, SQL, Tensorflow and other libraries, Neural Networks | Tableau, Excel, SQL, basics of Python or R |

## Salary Difference

The salary range of a data scientist vs. a data analyst is a significant point to consider when choosing your career path. For a future aspirant, skillset and salary go hand in hand.

Glassdoor reports that the average salary for a data scientist is $122,875 per year in the United States, while the average salary for a data analyst is $75,549 per year.

Let’s compare the average salary positions with the help of a bar plot:

## Understanding Data Scientists and Data Analysts

By now, you should be able to distinguish between these two roles that work with data. Although data scientists and data analysts share some commonalities, their functions, skill sets, methodologies, and business insights make them different.

Data analytics and data science are growing, interrelated fields. Develop and assess your skillset to end up in the role that’s right for you.