Introduction

Data is the base of all information. It is a collection of raw facts and figures collected from varied sources, that, after careful analysis expound to decision making. More than 4.6 billion people in the world actively use the internet, creating quintillion bytes of data on a daily basis. Data Analysts and Data Scientists sift through this goldmine to help companies drive swift business decisions efficiently. 

Both data science and data analysis deals with data, but the difference lies in what they do with it. While making a comparison of Data Analysis vs Data Science, it can be noted that data analysis deals with large data sets to identify trends, develop charts and create visually appealing presentations to help businesses in making strategically viable decisions. Data Science on the other hand deals with designing and building new processes for data modeling and production using prototypes, custom analysis, and forecasting tools using algorithms. 

Differentiating Data Analysis vs Data Science in simpler terms – Data Science can be referred to as an umbrella term, more comprehensive in its approach and used to prepare questions around the datasets, while Data Analysis processes and responds to these pre-prepared and asked questions. Data Analytics, therefore, can be considered a part of a larger process called Data Science.

  1. Data Analysis
  2. Data Science
  3. Difference Between Data Analysis and Data Science

1. Data Analysis

Data Analysis is a subset of a broader field viz. Data Analytics, the sole focus of which is to analyze existing datasets in a statistical manner and present it in a visually appealing manner. A person who is well-versed in the field of Data Analytics and can visualize data, conduct analytical data analysis, and communicate data points for conclusions is called a Data Analyst. 

The question that now arises is How to learn Data Analytics?  A knack for numbers, tinkering with computer programming, curiosity, a keen sense of emotional intelligence, and great interpersonal and communication skills are key ingredients for learning Data Analytics. A Master’s Degree in Data Analytics is not a prerequisite to doing well in the field of data analytics but a knowledge of SQL, MS Excel, and visualization tools like Tableau or Google Data Studio is a must-have.

All in all, a data analyst has many functions, main responsibilities being:

  • Meet with stakeholders and define the problem
  • Pull data using tools like SQL
  • Use tools like Excel and Tableau for EDA (Exploratory Data Analysis), trend analysis, and visualizations
  • Present findings and insights to the stakeholders in an easily understood, visually appealing manner

2. Data Science

Data Science is the vast field used to tackle big data using processes like data cleansing, its preparation, and analysis. Data is gathered from multiple sources and this undefined data is arranged using heavy coding and multiple tools, to build automation systems and frameworks. Data Scientists ask questions, write algorithms, and build complex mathematical and statistical models to determine patterns in data for predictions.  Top skills that a data scientist should possess include a thorough knowledge of programming languages like Python, Scala, or R, command over SQL, data wrangling and data exploration techniques, Jupyter Notebook, algorithms, and overall knowledge of machine learning. 

What is Data Science used for? Data Scientists not only deal with refining and defining data but they:

  • Define problems along with the stakeholders
  • Use SQL to pull data
  • Do EDA, feature engineering, model building, and make predictions using Python, Jupyter notebook, and algorithms.
  • Compile code for production

Data science, therefore,  is used to obtain answers, incorporating computer science, predictive analytics, statistics, and machine learning to parse through massive datasets in an effort to establish solutions to problems that haven’t been thought of yet.  

3. Difference Between Data Analysis and Data Science

It is easily perceivable that both data science and data analysis deal with big data and its processing to present it in a way that the outcome is well understood by the stakeholders. Due to the use of the common term ‘data’, it is often assumed that these fields are similar and interchangeable. But in reality, data science vs analytics could not be more different. 

Data science in simple words means to discern data – structured or unstructured, and gain insight or valuable information from it.  Data science uses various types and techniques of data analysis, which might or might not require the use of a computer and is closely related to the mathematical branch of Statistics, which is used in the collection, organization, analysis, and presentation of data. Data analysis, in contrast, is to draw conclusions from datasets by processing and extracting information. It follows a workflow of Data Ingestion, Data Cleansing, and Transformation, Dimensionality Reduction, Data Analysis, and Visualisation.

The main difference between the two fields is their scope. Data Science is an umbrella term for the group of fields used to mine large amounts of data.  Data analytics can be considered a part of this elaborate field. So data science has a macroscope in contrast to the microscope of data analysis. The goal of data science is to ask the right questions and for data analysis is to find actionable data based on those questions. Another difference is the area of exploration. Data science is not concerned with specific parts of data but data analysis is a more focused approach towards answering specific queries based on the data in hand.

Data Science is used for data sourcing, data cleansing, statistical modeling, result evaluation, testing, and deployment. Data analysis is used for data querying, data wrangling, statistical modeling, data analysis, and data visualization.

Major fields that use data science are machine learning, Artificial Intelligence, search engine engineering, corporate analytics, etc. Data Analysis is used in the healthcare industry, gaming, the travel industry, and industries with immediate data needs.

Conclusion

The two fields of Data Science and Data Analysis are highly interconnected, owing to their dependence on the sifting of data to make it useful. They can, therefore, be perceived as parts of a whole that are interdependent and interconnected to come up with vital information by processing big data. They are nothing but two sides of the same coin, that work in tandem to analyze, review, and understand datasets to create insights and future trends for all stakeholders.

Data science is responsible for bringing structure to big data, searching for compelling patterns, and advising decision-makers to bring in the changes effectively to suit the business needs of all stakeholders. Data analysis is one of the many tools and processes that data science uses. Data science asks important questions by looking at the data that did not even exist earlier. Data analysis can turn those hard to answer questions into actionable insights with practical application using a visually stimulating medium that is easy to interpret and understand.

If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional. 

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