Which Is Better Data Science Or Data Analytics?

Which Is Better Data Science Or Data Analytics
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Data is valuable and is generated every second across systems, phones, stores, etc. Big data is gold in the tech world because data sciences and data analysis help get actionable insights for businesses to base their smart decisions on. The volume of data has grown so huge that the datasets now need professionals with the appropriate knowledge and tools to convert it, parse through it, make sense, and provide the right information. Big data comprehension and use is now a part of data analytics and sciences. So how are data science and data analysis different though complementary? Let’s examine these fields.

What Is Data Science?

The multidisciplinary data science field mainly focuses on discovering actionable insights from the extensive sets of structured and raw data available to them. The field is used for unearthing answers from the data to the things the data is about, and we still do not know. Experts in data science use techniques and technology to get these insights from predictive analysis of trends in data, computer science, machine learning, etc. Their process of study uses parsing of data and Big data databases and data sets to scour for answers. Rather than answer specific questions, data science experts focus on trend prediction, finding the right questions to ask the database, using disconnected and disparate data sources that may have raw, structured or semi-structured data sources, and finding better methods to information analysis. In other words, the data scientist helps clean and structure the data sets and databases, so the machines understand how to answer when asked questions of the database.

What Is Data Analytics?

Data analytics is focused on the ways and means of performing statistical operations, processing the Big databases and existing datasets, and more. Data Analysts create new methods to process, capture, and organize data in the quest for actionable insights to data presentation and current ways of predicting trends and predictions based on the provided data sets. Most importantly, the field of data analytics uses the data sets provided by data scientists who help the systems with cleaned data to understand how to predict trends. They are the ones tasked with finding solutions, trends, and answers from data. Data analytics also includes combining methods like statistical analysis, analytics, etc., combining data sets, and finding the right connection between the data sources.

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The Differences

Most people use the terms data scientist and data analysts interchangeably, which is wrong. They are both interrelated but unique fields. The fundamental difference between the two is the scope. Data science is an umbrella term for various methodologies and fields to mine databases and Big data sets. On the other hand, data analytics is the small part of data sciences that focuses on getting the actionable insights and predictions from the datasets and databases provided to them from the existing system queries.

The next important difference between the two is the exploration of the fields. While data sciences actually parse the data to provide actionable insights after cleaning raw and unstructured data formats, data analytics finds the answers or insights from the already cleaned data based on existing queries. Take a look at the table below to help understand the differences.

Areas Data Science Data Analytics
Scope Macroanalysis of data Microanalysis of data
Goal Asks and focuses on the right questions Finds actionable insights from the data and queries provided.
Major Fields ML or Machine learning, Artificial Intelligence (AI), corporate analytics, search engine engineering and more. Gaming, healthcare, travel, banking etc., with an immediate need of insights into the data.
Using Big Data Yes Yes

The two fields are really data-based and 2 sides of a coin. Hence the functions are interrelated. Data science parses, cleans and formats the big datasets into queries, future trends, initial observations, potential insights etc., that are used to train the software and algorithm. Their inputs are used in modelling, enhancing AI algorithms, improving machine learning, and presenting data in a structured and easy to use format for the software. Adding in a data analytics field means the database and algorithm can now be focused upon to provide the best of trends, predictions, insights etc., on which business decisions are made.

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Skills required:

1. Data Analytics

This field needs a good knowledge of problem-solving skills in

  • Intermediate level of Statistics and mathematics.
  • Being adept at SQL database and Excel to dice and slice the data.
  • Experience with reporting tools like Power BI and other BI tools.
  • Knowledge of languages and statistical tools in SAS, R, Python etc.

An engineering background is not needed to be a data analyst. However, you should be able to use your skills with statistical analysis, predictive analytics, modelling and databases taught in a data science course.

2. Data Science

This field will need a combination of skills in Advanced Statistics, mathematics like calculus, algebra etc., Machine Learning, Predictive Modelling, and Programming, along with

  • Dexterity in Big data tools like Spark and Hadoop.
  • Knowledge of No SQL, SQL, databases like MongoDB and Cassandra, among others.
  • Use of data visualization tools like D3.js, QlikView and Tableau.
  • Expertise in programming languages like R, Python, Scala, Java and more.

Job Roles

The Data Analyst’s job role includes –

  • Data Cleansing
  • Develop visualizations and KPI’s.
  • Exploratory data analysis
  • Discover new patterns using various statistical tools.

The basic qualifications required would be a Bachelor’s degree and a specialization like the MS in DS (NWU).

The Data Scientist’s job role includes –

  • Cleaning, processing, and integrity verification of data.
  • Discovering business insights using ML techniques and algorithms.
  • Exploratory Data Analysis.
  • Finding new data trends to make future predictions and drive data insights.

The qualifications required would be a Master’s degree in Mathematics, Statistics, Computer Engineering or Computer Sciences with expertise in ML, AI, Big data, Neural networks and more.

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Salary Packages

From a cursory glance at the above facts, you can easily see that Data Scientists are required to have a higher qualification level as their job is far more complex. On the other hand, data analysts have to prove themselves and grow into the role of a Data Scientist. For both roles, the demand is very high and far outstrips the demand. The average Indian salary for a fresh Data Scientist on Glassdoor is Rs 5.11 lakhs per annum which shoots up with experience. For Data Analysts, the pay packages start from 4 lakhs pa and again escalate with experience.

In Conclusion

Do you wish to be a Data Scientist or Data Analyst? Then your skills are what puts you in control of your career, and you should get specialization through the best data science programs from Great Learning. Their courses are typically fast-tracked and customized to give you a head start. Their placement and career fairs ensure you get placed with the industry leaders because the course design includes a range of industry-relevant projects to be completed. The mentored course also makes use of industry experts and industry experts lead the online weekend learning sessions. All in all, you are set to hit the field with exemplary practical skills and will probably get a far better pay package than the rest.

Data today is digital gold. With the increase in data generated by the second, the complexity of handling data sets and databases has also risen. The Data Scientist needs to have a higher level of understanding of these complexities and work with large or Big databases. On the other hand, businesses rely on Data Analysts to predict their trends, provide foresight, insights and predictions based on the data generated and used by their systems. Both roles are crucial to enterprises, and the demand for these two roles far outstrips the supply of data professionals.

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