Data Science Vs Data Analytics
Given the vast volumes of data created today, data science is a crucial aspect of many sectors, and it is one of the most hotly disputed topics in IT circles. Its popularity has expanded over time, and businesses have begun to use data science and data analytics approaches to expand their efficiency and enhance customer happiness. Let’s see difference between data science and data analytics in detail in this blog.
What is Data Science?
Data science is a system that includes data purification, preparation, and analysis and is used to deal with the massive data. A data scientist collects information from a variety of sources and applies machine learning, predictive analytics, and sentiment analysis to extract meaningful information from it. They can provide accurate predictions and insights that may be used to fuel crucial business decisions as they understand data from a business perspective.
The Data Science Lifecycle
The data science lifecycle is divided into six stages:
- Understand the problem statement
- Collection of Data
- Data Cleaning
- Data Explore
- Data Modelling
- Collect Insight
Data Science: Core
Database administration, data wrangling, and machine learning are all skills you'll need expert in Probability & Statistics, Multivariate Calculus & Linear Algebra, and R, Python, Java, Scala, Julia, SQL, and MATLAB programming. Knowledge of big data platforms such as Apache Spark, Hadoop, and others.
Job roles of Data Scientist
- To clean, process, and confirm
- Exploratory Data Analysis on big
- Create ETL pipelines to perform
- Conduct statistical analysis with
machine learning algorithms such as logistic regression, KNN, Random Forest, Decision
Trees, and others.
- Write automate programmers write
- To use machine learning tools and algorithms to really get business insights.
- To explore different data patterns in order to make business predictions.
What is Data Analyst?
A data analyst is someone who can do basic descriptive statistics, display the data, and interpret sets of data in order to reach conclusions. They must have a fundamental grasp of statistics, a thorough understanding of databases, the capacity to design different perspectives, and the ability to interpret data. The required degree of data science is known as data analytics.
The Data Analyst Life Cycle
The data analyst lifecycle is divided into six stages:
- Understanding the data
- Data cleaning
- Data Enhancement
- Data Analytics
- Data Visualization
Data Analytics: Core Skills
- Excel and SQL
database knowledge is required.
- Expertise in tools like Tableau, Power BI, Google Data Studio, is must for making meaningful insight.
- Programming skills in language like R or Python is necessary.
Job roles of Data Analyst
- To gather and analyze data.
- To find interesting patterns in a
- To use SQL to run data
- To try out various analytical techniques, such as predictive analytics, prescriptive analytics, descriptive analytics, and diagnostic analytics.
- To represent the extracted data using data visualization tools like as Tableau, IBM Cognos Analytics, and others.
I hope you got a clear idea about the difference between Data Analyst and Data Scientist.
Happy Learning! Stay tuned!
- Avani Popat
- Mar, 10 2022