Data Science Vs Data Analytics

Data Science Vs Data Analytics
Data Science Vs Data Analytics

With the advancement in processing large-scale data, the term “Data Science’ and “Data Analytics” has been coined among technocrats such that they started defining a system that can optimize their work most efficiently. However, working over such a huge dataset requires the proper skill set of tools to be used.

To give a better gist about large-scale data, Data Science and Data analytics are the two different bifurcations of data technology that accelerate business growth. At very first glance, they both look the same but give two different insights and pursue different approaches as well. Sometimes, the term ‘Data Science’ and ‘Data analytics’ are used interchangeably as both operate on large-scale data.

Watch the video : What is Data Science?

So the question is ‘What exactly is Data Science?

  • Data Science is the bigger shell that holds one of the components called Data Analytics.
  • Data Science is used with structured and unstructured data to demystify questions about the data.
  • Data Science uses predictive modeling and Machine Learning to parse through Big data and tells us the insights that were not encountered yet.
  • Data Science is a combination of different disciplines like Statistics, Machine Learning, Data Visualization, and Mathematics.
  • Data Science helps gain the actionable insights that can drive business innovation.

And, What exactly is ‘Data Analytics?’

  • Data Analytics primarily identifies any trends and concludes the data.
  • Data Analyst creates charts and visual representations of the current data and answers the predefined question they had.
  • They mainly deal with the statistical analysis of the current data.
  • With the help of data analytics, efficient business decisions can be made.
  • A data analyst uses various BI tools like Tableau, Power BI, and QlikView to get statistical inferences from the data which is not possible to do manually with such large-scale data.

Refer the video: Learn Data Analysis with the Help of Python

What is the main Difference between Data Science & Data Analytics?

As stated earlier, Data Science is the main set under which various disciplines come. Data Analytics for sure is one of the disciplines which work under the hood of Data Science but has its significance when dealing with large-scale industry.

So, when we say data science it generally means extracting data for the modeling of algorithms and to some extent deployment as well. Data science helps us create Machine learning models which are used for forecasting future events or predicting unseen data. On the other hand, Data analytics gives us valuable insights using statistical knowledge about the data in hand with the help of various BI tools (Tableau, Power BI, etc.)

Another main difference between Data Analytics and Data Science is that the former knows what are the answers to the predefined questions and the latter dig deeper to find what the undiscovered questions are.

Data Science can fit into structured and unstructured data whereas data analysis of unstructured data is only possible when converted into the structured form using some heuristics.

Roles and Responsibilities of Data Scientists and Data Analysts:

Data Scientists: –

  • To extract data from various sources
  • To check the integrity of data by doing primary checks
  • To perform Exploratory data analysis to gain insights into the various features present in the data
  • To create best-fitted machine learning models like Naïve Bayes, Logistic regression, XGBoost, etc.
  • To write codes for Machine Learning Libraries

Data Analysts: –

  • To extract and interpret the data
  • Good SQL knowledge for querying databases
  • Good knowledge of data visualization tools like Power BI, IBM Cognos
  • Good knowledge of different charts and graphs
  • Derive meaningful insights and solve problems

So, whenever we solve any business task, The three major components of Data Science i.e. Statistics, Data Visualization, and Machine Learning come into the picture, and with the combination of all these three, we build a strong predictive solution for the company. The Data visualization part though can be given to a specific team called Data analysts. They not only create insights about the data which will help the team of Data Scientists to create optimized machine learning models but also, creates solutions for business problem statements that will be useful for the company in taking certain decisions.

The scope of data science is Macro as it acts as the bigger umbrella under which smaller parts like Data analytics resides, hence it is having a micro scope. Data science professionals are generally highly paid in the industry compared to Data Analytics as they serve macro purposes. So in any industry, the group of Data Scientists and Data Analysts working together gives two different aspects of data-driven decisions.

For further reference :

Linear Regression Algorithms

Support Vector Machine Algorithm (SVM) – Understanding Kernel Trick

Logistic Regression with Example

Check out the videos

What is Exploratory Data Analysis (EDA) – EDA Using Python.

What is HR analytics? – HR analytics using Python