Data analytics and data science are two terms that are commonly used in the ever-changing fields of business and technology. Despite their close relationship, these disciplines have distinct objectives and skill sets. We’ll explain the key distinctions between Data Science vs Data Analytics in this post so you can decide which is best for your requirements or professional objectives.
Data Science vs Data Analytics
What is Data Science?
The field of data science is concerned with taking structured and unstructured data and using different algorithms, preprocessing techniques, and scientific methods to extract meaningful information and insights. This area of study, which is connected to artificial intelligence, is one of the most in-demand right now. Data science uses computations, statistics, programming, mathematics, and other sciences to extract valuable information from massive amounts of data presented in a variety of formats.
What is Data Analytics?
By analyzing the raw data, data analytics is used to conclude. It is beneficial to a variety of businesses since it enables them to base their decisions on the data’s conclusions. In summary, data analytics facilitates the translation of large numbers of data figures into plain English, or conclusions, which are then useful for making more in-depth decisions.
Data Science Process
- Goal Definition: The data scientist collaborates with business stakeholders to determine the analysis’s goals and objectives. These goals can be more broad, such as improving overall production efficiency, or more specific, such as optimizing an advertising campaign.
- Data Collection: The data scientist creates a methodical procedure to gather and store source data if there aren’t already systems in place for doing so.
- Data Integration & management: “Unclean data is converted into information that is ready for the data scientist to analyze by using best practices for data integration. Distinct data types are combined into standardized formats for storage in a data lake or warehouse using steps in the data integration and management process, such as replication, ingestion, and data transformation.”
- Data Investigation & Exploration: The data scientist conducts exploratory data analysis and preliminary data investigation in this step. Usually, a business intelligence tool or data analytics platform is used for this research and exploration.
- Model Development: The data scientist selects one or more potential analytical models and algorithms based on the business goal and the data exploration. The data scientist then uses data science techniques, including AutoML, machine learning, statistical modeling, and artificial intelligence, to build these models using languages like SQL, R, or Python. After that, the models are “trained” through repeated testing until they perform as needed.
Data Analytics Process
- Project Requirements & Data Collection: Choose the question or questions you want to address and make sure you have gathered all the necessary source data.
- Data Integration & Management: Convert unclean, unsuitable for business use data. To combine various data types into standardized formats that are kept in a repository like a data warehouse or data lake and governed by a set of particular rules, this step involves data replication and ingestion.
- Data Analysis, Collaboration, and Sharing: Data analytics software allows you to explore your data and collaborate with others to generate insights. Subsequently, distribute your research findings throughout the organization in the form of engaging interactive dashboards and reports. Some modern tools support self-service analytics, which allows users to explore and analyze data using natural language without having to write code. These capabilities will enable more people to work with and benefit from their data, increasing data literacy.
Data Scientist Skills
- Oversee and enhance the data infrastructure of the company.
- Effectively communicate and work together within the organization
- Possess the curiosity to investigate and identify patterns and trends in data
- Make use of software tools, appropriate statistical methods, and programming languages.
- Assist in defining goals and interpreting outcomes using knowledge of the business domain
Data Analyst Skills
In addition to having the required technical knowledge, the ideal data analyst can collaborate and efficiently communicate with all stakeholders. Providing KPI examples and aiding in goal definition are examples of business skills. Technical expertise includes knowledge of statistical analysis, reporting, R or SAS programming, data modeling, data integration and management, and data analytics. These skills are typically derived from a background in statistics and mathematics, and occasionally include an analytics master’s degree.
Difference Between Data Science and Data Analytics
| Features | Data Science | Data Analytics |
| Coding Language | Python is the most commonly used language for data science along with the use of other languages such as C++, Java, Perl, etc. | Proficiency in Python and R Language is imperative for the field of Data Analytics. |
| Programming Skills | A thorough understanding of programming is necessary for data science. | Proficiency in basic programming is required for data analytics. |
| Use of Machine Learning | Machine learning algorithms are used in data science to obtain insights. | Machine learning is not used in data analytics to extract insights from data. |
| Other Skills | Data Science makes use of Data mining activities to get meaningful insights. | Hadoop-based analysis is used for getting conclusions from raw data. |
| Scope | The scope of data science is large. | The Scope of data analysis is micro i.e., small. |
| Goals | Data science deals with explorations and innovations. | Data Analysis makes use of existing resources. |
| Data Type | Data Science mostly deals with unstructured data. | Data Analytics deals with structured data. |
| Statistical Skills | Statistical skills are necessary for the field of Data Science. | The statistical skills are of minimal or no use in data analytics. |
FAQ’s of Data Science vs Data Analytics
Q1. Which is superior, data analytics or data science?
Ans. Depending on the kind of career we wish to pursue For individuals who wish to pursue a career in analytics, data science is a better option than data analytics if they want to use machine learning to create sophisticated models.
Q2. Which should I study first, data science or data analytics?
Ans. A beginner with no programming experience may find that starting with data analytics tools such as Microsoft Excel and Power BI is a good idea.
Q3. Which is more lucrative, data analytics or data science?
Ans. Data scientists typically make more money than data analysts do. However, the salary in both roles is determined by our level of expertise and understanding of the relevant field.