What Is Data Science, and What Does a Data Scientist Do?


In this dynamic world 'We have lots of data – now what?'

Data science is a multidisciplinary blend of data inference, algorithmm development, and technology in order to solve analytically complex problems.

At the core is data. Troves of raw information, streaming in and stored in enterprise data warehouses. Much to learn by mining it. Advanced capabilities we can build with it. Data science is ultimately about using this data in creative ways to generate business value:

This aspect of data science is all about uncovering findings from data. Diving in at a granular level to mine and understand complex behaviors, trends, and inferences. It's about surfacing hidden insight that can help enable companies to make smarter business decisions.


PROIBA Data Science Program


Data scientists are responsible for discovering insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals. The data scientist role is becoming increasingly important as businesses rely more heavily on data analytics to drive decision-making and lean on automation and machine learning as core components of their IT strategies.

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PROIBA Data Science Program


Data scientists are responsible for discovering insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals. The data scientist role is becoming increasingly important as businesses rely more heavily on data analytics to drive decision-making and lean on automation and machine learning as core components of their IT strategies.

Few Questions that comes to your mind

  • What Do Data Scientists Do?
    • Identifying the data-analytics problems that offer the greatest opportunities to the organization
    • Determining the correct data sets and variables
    • Collecting large sets of structured and unstructured data from disparate sources
    • Cleaning and validating the data to ensure accuracy, completeness, and uniformity
    • Devising and applying models and algorithms to mine the stores of big data
    • Analyzing the data to identify patterns and trends
    • Interpreting the data to discover solutions and opportunities
    • Communicating findings to stakeholders using visualization and other means
  • How to solve a problem in Data Science?
    • Problems in Data Science are solved using Algorithms. But, the biggest thing to judge is which algorithm to use and when to use it?
  • What is Machine Learning?
    • It is a type of Artificial Intelligence that makes the computers capable of learning on their own i.e without explicitly being programmed. With machine learning, machines can update their own code, whenever they come across a new situation.
  • What is the difference between an analyst and a data scientist?
    • Analyst is somewhat of an ambiguous job title that can represent many different types of roles (data analyst, marketing analyst, operations analyst, financial analyst, etc). What does this mean in comparison to data scientist? Data Scientist: Specialty role with abilities in math, technology, and business acumen. Data scientists work at the raw database level to derive insights and build data product. Analyst: This can mean a lot of things. Common thread is that analysts look at data to try to gain insights. Analysts may interact with data at both the database level or the summarized report level. Thus, analyst and data scientist is not exactly synonymous, but also not mutually exclusive.
  • What is Data Munging?
    • Raw data can be unstructured and messy, with information coming from disparate data sources, mismatched or missing records, and a slew of other tricky issues. Data munging is a term to describe the data wrangling to bring together data into cohesive views, as well as the janitorial work of cleaning up data so that it is polished and ready for downstream usage. This requires good pattern-recognition sense and clever hacking skills to merge and transform masses of database-level information. If not properly done, dirty data can obfuscate the 'truth' hidden in the data set and completely mislead results. Thus, any data scientist must be skillful and nimble at data munging in order to have accurate, usable data before applying more sophisticated analytical tactics.
  • Data scientist requirements?
    • Each industry has its own big data profile for a data scientist to analyze. Here are some of the more common forms of big data in each industry, as well as the kinds of analysis a data scientist will likely be required to perform, according to the BLS.
    • Business
    • E-commerce
    • Finance
    • Government
    • Science
    • Social networking
    • Healthcare
    • Telecommunications
    • Lot More
  • How can I register for course?
    • We have made it ease for you! To enlist for a course visit 'Admission' selection in the homepage. We have finite seats for each Batch. Save your seat by enlisting to avoid disenchantment at the last minute.Or You can 'click here' to redirect to our admission page.
  • Tell me, what if I am unable to attend after Registering?
    • We assure you about your money. You can join/attend for the upcoming batches as per your availability. However, If you are unable to attend you must inform to our helpdesk team as there may be awaiting participants list and another student may be able to take your place.
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