Data Science and Data Analytics: Learn what makes each type of analysis different
The rise of Big Data has spawned two new industry buzzwords: Data Science and Data Analytics. Today, the entire globe significantly contributes to tremendous data growth, hence the term “Big Data.”
Texts, emails, tweets, user queries (on search engines), social media chatter, data created by IoT and linked devices -everything we do online – are all Big Data. The data produced by the digital world every day is so enormous and complicated that standard data processing and analysis technologies cannot handle it. Data Science and Data Analytics come into play. We frequently use Data Science and Data Analytics interchangeably since Big Data, Data Science, and Data Analytics are still developing technologies. The fact that both data scientists and data analysts deal with big data is the leading cause of the misconception. The substantial differences between data analysts and data scientists fuel the argument between data science and data analytics.
Big Data is dealt with by data science and data analytics, each using a different strategy. Data Analytics falls under the category of data science. Mathematics, statistics, computer science, information science, machine learning, and artificial intelligence are all combined in data science. This results in differences between data science and data analytics. Data mining, data inference, predictive modeling, and the creation of ML algorithms are all included in this process, which aims to discover patterns from large datasets and turn them into practical business strategies. However, statistics, mathematics, and statistical analysis play a significant role in data analytics. Data science looks for novel and original issues that might spur commercial innovation. On the other hand, data analysis seeks answers to these issues and determines how to use them inside an organization to promote data-driven innovation.
Data are used differently by data scientists and analysts. Data scientists clean, analyze, and evaluate data to derive insights using a combination of mathematical, statistical, and machine-learning approaches. ML algorithms, predictive models, custom analyses, and prototypes are used to create sophisticated data modeling procedures. Data analysts gather vast amounts of data, arrange it, and analyze it to find pertinent patterns, whereas data analysts evaluate data sets to detect trends and develop conclusions. Following the analytical phase, they try to display their findings using techniques for data visualization, such as charts and graphs. To make detailed results understandable to a company’s technical and non-technical people, data analysts translate them into business-savvy language. Another distinction between data analysis and data science is this. To provide valuable insights for data-driven decision-making, both jobs execute varied degrees of data gathering, cleansing, and analysis. Therefore, data scientists’ and analysts’ duties frequently overlap, leaving people to wonder, Are data analytics and data science the same?
Data scientists’ responsibilities
- to prepare, purge, and verify data accuracy.
- using massive datasets to undertake exploratory data analysis.
- to create ETL pipelines for data mining.
- to do statistical analysis utilizing ML methods such as decision trees, random forests, logistic regression, and KNN.
- to create helpful ML libraries and automate code.
- employing machine learning techniques and algorithms to get business insights.
- to find new trends in data and anticipate business outcomes.
The duties of data analysts
- to gather and analyze data.
- to find essential trends in a dataset.
- to carry out SQL data querying.
- to experiment with various analytical techniques, including descriptive, diagnostic, prescriptive, and predictive analytics.
- to show the gathered data using data visualization tools like Tableau, IBM Cognos Analytics, etc.
Data scientists need to be experts in programming (Python, R, SQL), predictive modeling, and machine learning, as well as in mathematics and statistics. Data analysts must be knowledgeable in database administration and visualization, data mining, data modeling, data warehousing, and data analysis. Data scientists and analysts need to have a strong sense of logic and problem-solving skills. Another distinction between data analytics and data science is this.
An analyst of data must be:
- well-versed in SQL databases and Excel.
- competent with various technologies, including SAS, Tableau, and Power BI.
- programming skills in R or Python.
- adept at visualizing data.
To be a data scientist, one must:
- well-versed in multivariate calculus, linear algebra, probability, and statistics.
- proficient in R, Python, Java, Scala, Julia, SQL, and MATLAB programming.
- adept at managing databases, handling data, and using machine learning.
- knowledgeable about using extensive data systems like Hadoop and Apache Spark.
Data analysts have a statistical and analytical approach, but data scientists are often far more technical and demand a mathematical mentality. A Data Analyst’s employment is more of an entry-level one from a professional standpoint. Data Analyst positions in businesses are open to those with a good experience in statistics and programming. Recruiters often favor applicants with 2–5 years of industry experience when recruiting Data Analysts. On the other hand, data scientists are seasoned professionals with more than ten years of experience. Regarding remuneration, both data science and data analytics are well compensated. In India, the average compensation for a data scientist is between Rs. 8,13,500 and Rs. 9,00,000, while the average salary for a data analyst is between Rs. 4,24,400 and Rs. 5,04,000. The most excellent aspect of pursuing a career in data science or analytics is that these fields have a good, steadily ascending job trajectory.
