INSIGHTS

Data Scientist Vs Data Analyst. 10 things you should know.

Data Scientist The key factors that contribute majorly to the world we live in today are technology and collaboration. Technology offers us the means to get where we want, be it individually or as an organization, while collaboration enables us to get there faster.

In a world that is changing its pace every minute of the day, trendspotting and Parsing Data become a significant part of every industry. 

Isn’t it a bit puzzling when you think of Data Analyst and Data Scientist, what main tasks they perform, what are their key functions?

Here is an easy break-down for your perusal;

Data Analysis:

By definition, Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social domains. 
In today’s business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively. Processes like Measuring, recording, and tracking performance metrics allow the upper management to set new goals.

Data analysts are the people who are always on the look-out for the new sets of data to analyze, as mentioned in the article by Insaid, ‘Data analysts are the Detectives with a magnifying glass in their hand, always searching for that right set of data which will reveal the insights.

Main functional areas include:

  • Designing, coding, project management, and computer programming.
  • Implementation and troubleshooting of computer networks.
  • Research about the existing problems and plan solutions for the problem.

Data Science:

In a nutshell – Data Science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Data science is the same concept as data mining and big data: “Use the most powerful hardware, the most powerful programming systems, and the most efficient algorithms to solve complex problems” (Source: Wikipedia- https://en.wikipedia.org/wiki/Data_science)

On the other hand, Data scientists are someone who programs, develops algorithms based on data acquired and complexity factor of the problem. Also, did you know that the highly popular term of ‘ Data Scientist’ was coined by DJ Patil and Jeff Hammerbacher?In today’s business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively. Processes like Measuring, recording, and tracking performance metrics allow the upper management to set new goals.
Data analysts are the people who are always on the look-out for the new sets of data to analyze, as mentioned in the article by Insaid, ‘Data analysts are the Detectives with a magnifying glass in their hand, always searching for that right set of data which will reveal the insights.

Why is there a demand for Data Scientists, you may ask?

Three main factors that are responsible for making Data Scientists’ role major in any industry are,

i) Humongous amounts of data in the present days. – Digital data available for companies or even Government

ii) Quality of Data- Availability of millions of images and different type of data available on the internet has to go through a quality check. Not everything availed from the internet has utterly good quality. Data Scientists mine the data and cleanse them to prepare the final report of the high-quality Data

iii) Advanced Algorithms – In the previous 10 years, there is a huge surge in the Algorithms that are used in Big Data,etc.

Data Scientists are part mathematicians, computer scientists, and part trend-spotters as they need to know what the problem is and what is the most efficient way to solve such a complex problem

Main functions include:

  • Extract knowledge and insights from structured and unstructured data.
  • Evaluation of statistical models to judge how valid is the analysis of the models.
  • Developing improved predictive algorithms using machine learning.
  • Apply queries in the databases to gather complex data.
  • Recognize the business requirements and devise measurement plans like instrumentation, data collection, etc.
  • Apply improvements in internal data processing; automate manual procedures; improvise delivery and presentation of the data.
  • Proficient in working with the analytic tools and languages like Python and R to put forth actionable insights into important business metrics like revenue, customer acquisition, product enhancement, etc.
  • Constantly testing and enhancing the competence of machine learning models.
  • Creating the data visualizations to make a summary of an advanced analysis.

If you are you looking to dive into a Data Science career or if you are interested to know how a Data-driven program can help transition your career, check some of the advantageous skills for you here –  Data Science skills

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