Fri. Jan 28th, 2022

Companies use data science aggressively to become market leaders. Data is transmitted from different sources such as the web, social media, customer reviews, internal databases, and government data sets. But simply having that data stored will not help companies in any way use the data one needs to analyze it. Analyzing data is not an easy job as the trends are hidden.

The data science industry is drawing income from all industries both national and international. Revenue of $ 1.27 billion is in the last year alone and is projected to reach $ 20 billion in 2025. This sudden growth is because big data is proving to be of great value to the business. Some of the uses are:

  • Helping to understand market demand.

  • Help in the innovation of new products and services.

  • Helps with customer retention and satisfaction.

  • Helps communicate the brand to customers.

  • Help in digital marketing and social networks.

  • It helps in real-time experimentation and monitors business performance.


Data scientists are data manipulators who seek meaning in collected data. A data professional has many roles in your data for daily activities. Since the entire data process is a pipeline of many linked steps, a data scientist can do them all together or separate experts are appointed to complete the process. Some of the roles they play are:

  • Conduct research and pose a problem that is relevant to the market.

  • Collect data from various internal and external sources such as the web, internal databases, data sets available on the Internet, or customer reviews on social media platforms.

  • Clean and clean the data of all inconsistencies like gaps and incorrectly entered figures, time zone differences, etc.

  • Explore the data from all directions to find any kind of behavior patterns or hidden trends in them. For this, many tools are used that are programmed for exploratory data analysis.

  • Use statistical and mathematical tools and models to gain insight into your data and prepare it for predictive decision making.

  • Create new algorithms that are also called machine learning, where data is used to automate work.

  • Communicate the inferences learned in the use of data visualization tools and present them in a way that management can understand.

  • Proper understanding will lead to actionable decision making and finding solutions that can be applied in a practical way.

Different companies have different tasks lined up for their data analysis, but most of the activities remain similar.


Data scientists must have several skills up their sleeves. But the most important of these is having a curious mind and an analytical mindset. Searching for a question and then, like a detective, sniffing out answers from a large amount of data is no joke. Core traits like patience, curiosity, and contextual understanding can help one be successful. The rest of the knowledge is technical and can be learned and practiced. Some of the necessary skills are:

  • Mathematics, statistics and probability.

  • Programming and coding.

  • Cloud Computing (Amazon S3)

  • Modeling and machine learning

  • Database managment.

  • Tools like Python, Apache Spark and Flink, Hadoop, Pig & Hive.

  • SQL, Java, C / C ++

  • Knowledge of the industry.

  • Presentation and communication skills.

  • Decision-making skills.

Industry of all sizes and influences demands these skills of their experts, and to be a successful data scientist these are mandatory requirements.

By admin

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