Data Scientist vs Data Analyst – The Hot Debate for a Promising Career
World’s 90% of the data is generated in the last past 2 years. So now you can think how rapidly data is being generated. In fact, more than 2.7 zettabytes of data exists in the World today. It is projected to grow to 180 zettabytes in 2025.
To play with such huge amount of data there are responsible persons such as data scientists, data analysts, data engineers, etc.
In this Data Science vs Data Analytics Tutorial, we will learn what is Data Science and Data Analytics. Also, we will check the major difference between their roles this means Data Scientist vs Data Analyst.
This blog also contains the responsibilities, skills, and salaries for both data scientist and data analyst. This information will help you to select the perfect one for your career.
Without wasting time, let’s start exploring the difference between Data Science and Data Analytics.
1. What is Data Science?
Data Science is a field that encompasses operations that are related to data cleansing, preparation, and analysis. Data science is an umbrella term in which many scientific methods apply.
For example, data scientists apply concepts from mathematics, statistics, programming, and various other tools to perform data-operations.
With the help of Data Science, we analyze Big Data. We extract information and meaningful insights from this data. First, the Data scientist gathers datasets from multi-disciplines and compiles it together.
After that, he/she applies machine learning, predictive and sentimental analysis. The data is then sharpened to a point where some meaning can be derived out of it. At last, useful information is derived from the data.
Data scientist understands the data from a business point of view. His work is to give the most accurate predictions. A Data Scientist fosters decision-making in the company. Based on the prediction, a data scientist contributes to calculated data-driven business decisions.
In artificial intelligence and machine learning, Data scientist has a great role to play. For a Data scientist, knowledge of machine learning is a must. Machine learning is the most impressive technology in the world.
A Data Scientist needs to be well versed with machine learning algorithm and must be able to assess situations in order to apply these algorithms. And finally, a data scientist must know the in-depth working of the algorithm in order to apply it.
Want to explore everything about Data Science? Check Latest Data Science Tutorial
After getting in-depth knowledge of the Data Science now, let’s read about Data Analytics.
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2. What is Data Analytics?
Most people think that data science and data analytics are similar. But there are several differences between them. In order to understand their differences, we will have to assess them descriptively. Data analytics is the basic level of data science.
Data Analytics is carried out using Excel, SQL and in rare cases, even R. They mostly have business and computer science degree.
Its methodologies are mainly used in commercial industries. Data Analysts usually deal with static data and perform descriptive analysis as well as inferential analysis. They are responsible for testing and rejecting models and hypotheses.
It is the science of drawing insights from sources of raw information. It discloses trends and metrics. Otherwise, data may lose in the mass of information. They use the information to increase the efficiency of a business system.
To verify and disprove existing theories or models, Data Analytics is used. It is also used in many industries to enable organizations to make better decisions.
3. Data Scientist vs Data Analyst – Key Differences
Data Science and Data Analytics may stem from the common field of statistics, but their roles and backgrounds are very different. Following are some of the key differences between a data scientist and a data analyst.
- The first key difference between Data Scientist and Data Analyst is that while data analyst deals with solving problems, a data scientist identifies the problems and then solves them. Data Analysts are hired by the companies in order to solve their business problems. The role of a data analyst is to find trends in sales or usage of summary statistics for description of customer transactions. On the other hand, a data scientist does not only solves problems but also identifies problems in the first place.
- Data Analysts do not require communication skills and business acumen. Data Analysts are limited to the boundaries of analyzing data. It is not required by them to communicate the results with the team and help them in making data-driven decisions. However, a data scientist is required to have a strong story-telling and management skills in order to translate findings into business strategies. Therefore, a data scientist enjoys an active role in the decision-making process of the company.
- Another important difference between a data scientist and data analyst is the difference in handling data. Data Analysts make use of SQL queries to retrieve and manage structured data. On the contrary, Data Scientists also use NoSQL for unstructured data. Data Scientists, therefore, are responsible for managing both unstructured and structured data.
- Data Analysts do not deal with the development of predictive modeling or statistical tools for data forecast. However, Data Scientists requires the knowledge of machine learning to build powerful predictive models. These predictive models are regression and classification based models. While the role of a data analyst is only limited to statistical analysis and experimentation of data, data scientists also perform the forecasting of future events. These events can be forecasting sales, segmenting potential customer base etc.
- Data Scientists require tuning of data models and making the data products better. It also requires optimization of the performance of data-fueled products and machine learning models. This is not required by data analysts. Therefore, the role of a data scientist does not only involve building models but also tuning and maintaining them.
Now that you have decided to play with data, you must know the trending comparison – Big Data vs Data Science
4. Feature-wise Difference between Data Scientist and Data Analyst
Here is the best difference between Data Scientist and Data Analyst, check all the responsibilities, roles, and skills below –
4.1 Data Scientist vs Data Analyst – Responsibilities
Major responsibilities of a data scientist are –
- Data Transformation as well as data cleansing. A Data Scientist is also required to pre-process the data.
- Utilizing machine learning for forecasting and classification of patterns.
- Performing the optimization of predicting models and tuning them appropriately.
- Analyzing the requirements of the company and formulating questions for solving them further.
- Performing interactive visualizations for communication results with the team.
Major responsibilities of a data analyst are –
- Performing analysis and interpretation of data using statistical techniques.
- Extracting data and storing it in databases.
- Performing data cleaning and data filtration.
- Using exploratory data analysis for visual communication of data.
- Working with the teams to analyze business requirements.
4.2 Data Scientist vs Data Analyst – Skills
Approximately more than 40% of data scientist positions need an advanced degree. Such as an MS, or Ph.D. More than 80% of Data scientist have master’s degrees. More than 45% have PhDs.
Skills required for Data Scientist –
- In-depth knowledge of Python coding. It is the most common language including Perl, Ruby etc.
- Sound knowledge of SAS/R.
- A Data Scientist must be able to work with unstructured data. Whether it is coming from videos, social media etc.
- Sound skill in SQL database coding.
- Data Scientist should have a good understanding of various analytical functions. For example rank, median etc.
- In-depth knowledge of Machine learning requires.
- A Data scientist should have familiarity with Hive, Mahout, Bayesian networks, etc.
Skills required for Data Analyst –
- Sound knowledge of Excel, SQL, R, and Python.
- Communication and Data visualization skills.
- In-depth knowledge of Data wrangling skills.
- Mathematics and Statistical skills.
4.3 Data Scientist vs Data Analyst roles based on skill sets
Roles of data scientists according to their skill sets –
- Data Scientists
- Data Science Managers
- Big Data Architects
- Data Engineers
- Decision Scientists
Data Analyst roles according to their skill sets –
- Database Administrators
- Data Analysts
4.4 Data Scientist vs Data Analyst – Salary
Below statistics shows the salary of Data Scientist vs Data Analyst-
Data Scientist –$117,345
Data Analyst – $67,377
In the following visualization, we observe a distribution in the salary of data scientists. Breaking down the distribution, we see that the salary of an entry-level data scientist is $79,423. This is less than the average of $99,558.
The salary of data scientists beyond this average is based on their experience. Their salary ranges from $115,530 to $136,752 based on their years of experience.
Furthermore, let us see the salary comparison between Data Scientist and Data Analyst as follows –
We have studied about the Data Science vs Data Analytics in detail. Hence it is now easy to choose the best career option among the Data Analytics and Data Science.
If you are still in confusion, we recommend you to must check the Data Science vs Data Analytics difference through the infographic. It will give you a clearer insight.
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