Data Science and Analytics: Similarities and differences

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The world is using data like never, and the terms Data Science and Data Analysis are used almost interchangeably today. In fact, many people think that a Data Scientist is just a fancy name for a Data Analyst. However, while they do sound similar and both deal with big data sets, they are inherently different. Let us first define each one and then look a little deeper into the similarities and differences between Data Science and Data Analytics.

To put data generation in today’s world into perspective, let us look at the following graph, which depicts how much data was generated every minute in 2018.

Source: domo.com

Data Science

Data Science is a multidisciplinary field which has a much broader scope when dealing with data, where several techniques and tools are used to extract insights from data. In most cases, Data Science is used to scope out the right questions from the data set. It works at the raw level of data (structured, unstructured, or a combination of both) to build data models, to create more efficient machine learning algorithms, make predictions, and identify patterns and trends.

Some of the tools and techniques involved are clustering analysis, anomaly detection, association analysis, regression analysis, and classification analysis. Data Science works in the realm of the unknown, trying to find new insights and relationships in big data.

Data Analysis

Data Analysis is a subset of Data Science. It can be defined as the process of applying statistical, logical, and analytical techniques to data sets to discover information that helps in making informed decisions. A data analyst can use several tools like visualizations, Business Intelligence (BI), data mining, and textual data analysis.

The information gleaned from data analysis is highly dependent on the quality of the data. Data analysis merely curates’ meaningful insights from past data but is generally not used for predictions. It is typically driven by business goals.

Similarities between Data Science and Data Analytics

Both work with big data to get better outcomes for business or society.

Both require a background Mathematics, statistical and programming skills (HadoopR, SAS, SQL, and Python). A Data Scientist should also be well versed with the Business.

Differences between Data Science and Data Analysis

SLNo.  Data ScienceData Analysis

1

 Data Science is used to formulate the right (unknown) questions, likely to be beneficial   to the business. Data analysis is used to solve questions that come from a business perspective.

2

 A Data Scientist is required to have business acumen and the ability to create a story   from the data. A Data Analyst will likely be required to find straightforward answers to questions posed by the business.

3

 Data Science is used to prepare the data for analysis by Cleansing, Processing, Massaging and Organizing the data. Data Analysis is used to mine data to discover correlations and identify patterns.

4

 Data Science uses data from different data sets to solve real world problems. Data Analysis identifies data quality issues and generally uses a single data set.

5

 Typical uses include Fraud Detection, Personalized Marketing, Social & Customer Analysis, Gaming, Weather Prediction, Dynamic Pricing, Mental Health Research, etc. Typical uses include recommendation engines, loyalty programs, on-the-fly detection of trends, targeted advertising, etc.

6

 Data Scientists use tools like Python, SAS, R, SPSS, Matlab, Scala, Hadoop, PIG and Hive. Data Analysts use programming languages like Python, R, SQL, HTML, and JavaScript. Data visualization tools like Tableau, Spotfire, and QlikView are also used.

 

Conclusion

While differences do exist between data science and data analysis, together they form the future of our data driven world. Be it for business, personal, social, medical, or naturally occurring phenomena, embracing these technologies will make a significant difference in our lives. Their contributions have already started being felt in our daily lives and further advancement in areas like machine learning and artificial intelligence should truly prove to be of great use.