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Difference between Data Science and Data Analytics
Data Science vs Data Analytics
Lets have a look on difference between Data Science and Data Analytics. Both the fields are closely related fields that deal with extracting insights and knowledge from data. While there is some overlap between the two, they have distinct focuses and approaches.
Table of content :
- What is Data Analytics?
- What is Data Science?
- Difference between Data Science and Data Analytics
- Salary of Data Scientist vs Data Analyst
- Career in Data Science vs Data Analyst
What is Data Analytics?
It involves examining, cleaning, transforming, and modelling data to discover useful information, draw conclusions, and support decision-making. It primarily deals with descriptive and diagnostic analysis, aiming to understand what happened, why it happened, and what actions can be taken based on historical data. Data analysts use statistical methods, data visualisation tools, and various data manipulation techniques to uncover patterns, trends, and correlations within the data. They often work with structured data sets and employ tools like SQL, Excel, and Power BI and Tableau.
To know more about Data Analyst, its roadmap and How to become a Data Analyst. Click the link below
What is Data Science?
It is a multidisciplinary field that combines elements of mathematics, statistics, computer science, and domain knowledge to extract insights and build predictive models using data. Data scientists focus on predictive and prescriptive analysis, aiming to forecast future trends, make recommendations, and optimize processes. They use advanced statistical and Machine learning techniques, Data mining, and programming languages like Python, R, SQL, etc. to extract meaningful information from complex and large-scale datasets. Data scientists often work with both structured and unstructured data, including text, images, and sensor data.
To know more about Data Science, its roadmap and How to become a Data Scientist. Click the link below
Difference between Data Science and Data Analytics
| Data Science | Data Analytics | |
|---|---|---|
| Objective | Forecasting, optimization, and recommendation | Understanding historical data and supporting decisions |
| Technique | Advanced statistical analysis, Machine learning, AI | Statistical methods, data visualization, data manipulation |
| Data Types | Structured and unstructured data | Primarily structured data |
| Tools/Language | Python, R, SQL, Seaborn, Spark, TensorFlow, etc. | Excel, Python, SQL, Power BI, Tableau |
| Process | End-to-end involvement in data analysis, model development | Focuses on specific analysis and reporting tasks |
| Domain Knowledge | Requires deep understanding of the problem domain | Domain-specific knowledge often required |
Salary of Data Scientist vs Data Analyst
| Data Science | Data Analytics | |
|---|---|---|
| Fresher | 3.5 – 4 LPA | 3 – 3.5 LPA |
| 1-3 years of experience | 5 – 6 LPA | 4 – 4.5 LPA |
| 3-5 years of experience | 6 – 7 LPA | 5 – 6 LPA |
| Experienced | 9 – 15 LPA | 8 – 12 LPA |
Career and Scope in Data Science vs Data Analytics
Both fields offer promising career paths with a wide scope for growth and opportunities.
Data Science :
- Career Opportunities: Data Scientist, Machine Learning Engineer, Data Engineer, AI Specialist, Data Analyst (with advanced skills), Research Scientist.
- Scope: Data science has been rapidly expanding across industries as organizations recognize the value of leveraging data for decision-making and gaining a competitive edge. The demand for skilled data scientists is high in sectors such as finance, healthcare, e-commerce, manufacturing, and technology. Data scientists work on solving complex problems, building predictive models, developing AI systems, and extracting insights from large-scale datasets.
Data Analytics :
- Career Opportunities: Data Analyst, Business Analyst, Business Intelligence Analyst, Data Visualization Specialist, Data Engineer (with analytics skills).
- Scope: Data analytics have been a critical component of business intelligence for a long time and continue to be in high demand. Organisations across various sectors require skilled data analysts to interpret and communicate data insights, support decision-making, and optimise processes. Data analysts work with structured data, perform statistical analysis, create visualisations, and generate reports.
FAQ
Which field is better Data Science or Data Analytics?
Data science, or data analytics, depends on individual interests, skills, career goals, and the specific context. Both fields offer unique opportunities and have their own merits.
Which field has higher salary?
On average, data science roles tend to have higher salaries compared to data analytics roles. Data science positions often require more specialised skills than data analytics positions.
What to learn as a beginner for both the profile?
As a beginner, one should learn mathematics, statistics, Excel for data analytics, and programming languages like R, Python, and SQL for data science.
Which field requires more programming skills data science or data analytics?
Data science typically requires stronger programming skills compared to data analytics. Data scientists often work with programming languages like Python or R for data manipulation, statistical analysis, and machine learning tasks. Data analytics also involves programming, but the level of programming expertise needed is generally lower.
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