Difference Between Data Science vs Data Analytics

1. Objective
In this article, we will learn what is Data Science and Data Analytics. Skills needed to become a Data Analyst and Data Science. Then, we will also learn the key difference between Data Scientist vs Data Analyst. At the end, we will discuss the percentage of interested job seekers of Data scientist and Data Analyst.

So let’s start with the introduction of Data Science.

2. What is Data Science?

Data Science is a field that encompasses related to data cleansing, preparation, and analysis. Data science is an umbrella term in which many scientific methods apply. For example mathematics, statistics, and many other tools scientists apply to data sets. Scientist applies the tools to extract knowledge from data.
It is a tool to tackle Big Data. And then extract information from it. First Data scientist gathers data sets from multi disciplines and compiles it. After that, apply machine learning, predictive and sentimental analysis. Then sharpen it to a point where he can derive something. At last, he extracts the useful information from it.
Data scientist understands data from a business point of view.His work is to give the most accurate prediction. He takes charge of giving his predictions. The prediction of data scientist is very accurate. It prevents a businessman from future loss.
In artificial intelligence and machine learning, Data scientist has a great role to play. For Data scientist, knowledge of machine learning is the must. Machine learning is the most impressive development in the tech world. He requires knowing that which method of machine learning will exactly help him. And finally, how to apply that. He does not need to know how that method works.

2.1. Skills needed to become Data Scientist

Approximately more than 40% of data scientist positions need an advanced degree. Such as an MBA, or Ph.D. More than 80% of Data scientist have master’s degrees. More than 45% have PhDs. The following are the required data science skills-
  • In-depth knowledge of Python coding. It is the most common language including Perl, Ruby etc.
  • Sound knowledge of SAS/R
  • It is must that Data scientist 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 familiar with Hive, mahout, Bayesian networks, etc. In data science, knowledge of MySQL is just like an added advantage.
After getting in depth knowledge of the Data Science next, let’s read about Data Analytics.

3. What is Data Analytics?

Most people think that data science and data analytics are similar. But there is a minute difference between them. You will get the difference if you will see in a concentrated way. Data analytics is the basic level of data science. In data analytics computations made by using SAS/R. They mostly have business and computer science degree
Its methodologies are mainly used in commercial industries. To get more-informed business decisions by researchers and scientists. To check or reject the scientific models, hypotheses, and theories.
It is the science of drawing insights from sources of raw information. It discloses the 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.
Next, let’s see the skills needed to become Data Analyst

3.1. Skills needed to become Data Analyst

The following are the required data analyst skills-
  • Sound knowledge of R and Python
  • Communication and Data visualization skills.
  • In depth knowledge in Data wrangling skills
  • In depth knowledge of PIG, HIVE
  • Mathematics and Statistical skills

4. Feature wise Comparison Between Data Science vs Data Analytics

as we have discussed the introduction to Data Science and Data Analytics, Skill set for Data Science, Skill set for Data Analyst. Now in this section, we will cover the feature wise difference between Data Scientist vs Data Analyst.

4.1. Data Scientist vs Data Analyst according to Definition

  • A Data Scientist role is to predict future based on past patterns. While Data analyst finds meaningful information from data.
  • The role of Data scientist is to generate its own question. But Data analyst finds the answers to others sets of questions.
  • As Data scientists have the what ifs. But Data analysts are the ones who do the day-to-day analysis
  • Data scientist addresses business problems. It also gives an accurate prediction of the value of business once solved. Whereas Data Analyst only address business problems
  • Data scientist uses machine learning for extracting information. But Data Analyst uses an R / SAS tool for extracting information.
  • The role of Data scientist is to explore and examines information. He explores information from many disconnected sources. But Data Analyst explores and examines data from a single source.
  • The prediction of Data Scientist is very high. It can be accurate up to 90%. But, Data analysts don’t predict. They only solve the question given by the business.
  • A Data scientists will formulate questions. They formulate those questions whose solutions are likely to benefit the business. But Data Analyst only solves the questions given by business.
  • A Data scientist must have sound knowledge in statistical models and machine learning. Data Analyst needs sound knowledge in SAS/R

4.2. Data Analyst vs Data Scientist according to Responsibilities

 a) A Data Scientist Responsibilities
  • Data cleansing and processing.
  • Prediction of the business problem. His roles are to give future results of that business.
  • Develop machine learning models and analytical methods.
  • Find new business questions that can then add value to the business.
  • Data mining using state-of-the-art methods.
  • Presenting results in a clear manner and doing the ad-hoc analysis.
b) Data Analyst Responsibilities
  • Identify any data quality issues in data acquisition.
  • Solving business problems. By mapping and then tracing the data.
  • A Data analyst should coordinate with engineers to gather new data.
  • Perform statistical analysis of business data.
  • Documenting the types and structure of the business data.

4.3. Data Analyst vs Data Scientist roles based on skill sets

a) Data Scientist roles according to their skill sets
  • The Data creatives
  • Data Developers
  • Data Researchers
  • The Data Businesspeople
b) Data Analyst roles according to their skill sets
  • Database Administrators
  • Operations
  • The Data Architects
  • A Data Analysts

4.4. Data Scientist vs Data Analyst – Salary

Below statistics shows the salary of Data Scientist vs Data Analyst-
Difference of the Salary of Data Scientist vs Data Analyst
Next, let’s see the percentage of interested job seekers by indeed.com


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  2. This is a very informative article. I like that it is broken down into short sections making it easier for the reader.

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  4. well explained, thank u

  5. Learned a lot, Thanks for the article.

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