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Are you a Data Scientist or Data Analyst ??

Updated: Jun 24, 2020

Myth Busters: Data Science and Data Analytics are similar.

The buzz word of 2020 is “Data Science”.

All right, I am supposed to kick start with a interesting introduction that would act as anchor, that you stick around till the end.

But, I am going to make it short and simple.

Everyone claims that they are doing it, but nobody knows how correctly they are doing it. The entire confusion starts with different roles and naming conventions used by people.

People toss around word like “Data Analyst”, “Data Warehousing”, “Data Analytics”, “BIG Data”, “Data Analysis”, “Data Mining” and “Data Science”. And often get confused that “Data Analytics” and “Data Science” are one on the same.

But there is one fact that they all work with DATA.

Data Analytics Vs Data Science:

As earlier said, both data analysts and data scientist work with data, the main difference is the data analysts work with huge volumes of data, examine the data sets provided and develop data visualization, chart, reports and present it to business which help business to take strategic decisions. On the other hand, data scientists work on the historical data, and create new work flows, apply several algorithms, produce few prototype modelling, predictive models for further analysis.

Responsibilities of Data Analyst:

Data analyst works in different industries on different functional areas. They work on Master Data Management, Customer relationship management, Sales and Distribution, Marketing, Finance and Controlling, Trade Promotion Management and many more. However, their main goal is to use the huge volumes of transnational or master data to filter meaningful insights which help business to solve problems.

For instance:

Why the cost price of raw materials increased during #Pandemic ?

Why the sales dipped during the #COVID ?

Why there is delay in deliveries ?

The crux of a Data Analyst job includes >> >> >>

  • Data Designing, maintain data and databases. Fix errors and other data related issues

  • Data Mining, transport data from initial persistent area to other secondary or further level of data base.

  • Data Organizing, format the data, convert to readable format and organize in different layered scale structure.

  • Data Visualization, preparing reports, charts and dashboard which effectively communicate the business about the data trends for decision making.

  • Data governance, identify process improvements, prepare documentation, recommend data bases, data conversions, and develop data governance.

Again, there are different types of Data Analytics:

  • Descriptive Analytics: Looks at the data set for stipulated amount of time, like yearly sales, monthly manufacturing costs, hits on a website and so on to spot the trends.

  • Prescriptive Analytics: Tries to spot what business decision can take to mitigate the challenges. Data driven decisions have always made wonders for a success in any business.

Tools used by Data Analysts:

There are a bunch of tools in market by many providers to work with. A few of them are SAP BW, SQL, HANA, Big Data, Terra Data, Power BI, Design Studio and Tableau.

Responsibilities of Data Scientist:

Data Scientists, generally work close with Business to analyze the goals, vision and mission and ascertain how data can be used to achieve those short term or long-term goals. They analyze the data, create data flows, create algorithms, produce different predictive data models and share their insights with business to take a concrete decisions.

Basically, data scientist work with different stake holders to gather the requirement in below fashion >> >> >>

  • Collect data

  • Process and cleanse the data

  • Protect and store the data

  • Explore the data for further analysis

  • Choose several algorithms based on the data and goal

  • Apply statistical modelling and machine learning algorithms.

  • Split the data into train and test data

  • Train the data model

  • Measure the results and repeat the step for accuracy

  • Present the outcome to Business Stake holders.

Tools used by Data Scientists:

There are many tools, but the most popular ones are Python, R, Machine Learning.

Important skills of Data Science:

  • Statistical Learning

  • Machine Learning

  • Programming

  • Presentation

Although there are many differences in the job roles and responsibilities of both Data Analyst and Data Scientists, there is always a huge demand for both in businesses. However, Data science weighs more as the monetary benefits are on the higher end.

You have better ideas or corrections to make?

Put them on the comments now, yes noww!

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