What is Data Science?

Data science is an interdisciplinary field that uses the scientific methods, algorithms, processes and system knowledge and interest from structured and unstructured data. Data science is related to data mining and big data. Data science is a concept to unify statistics, data analysis, machine learning, and their related methods in order to understand and analyze actual phenomenon with data. Now a day’s Data science continues to evolve as one of the most promising and in-demand carrier paths for skilled professionals. Today successful data professionals understand that they must advance past the traditional skills of analyzing a large amounts of data mining and programming skills. In order to discover useful Intelligence for their organization, data scientists must master the full scope of the data science life cycle and a level of flexible understanding to maximize the returns in all the stages of the process.

What is Data Analysis?

Data analysis is a process of collecting, inspecting, cleansing, transforming and modeling data with the goal of discovering useful information, informing conclusion 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 science domains. In today’s business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.

Data Analysis Process consists of the following phases:

  • Data Requirements Specification:
    The data required for analysis is based on a question or an experiment. Based on the requirements of those directing the analysis, the data necessary as inputs to the analysis is identified.
  • Data Collection:
    Data Collection is the process of gathering information as per the requirement on a topic or variable. The emphasis is on ensuring accurate and honest collection of data. Data Collection ensures that data gathered is accurate such that the related decisions are valid. Data Collection provides both a baseline to measure and a target to improve. Data is collected from various sources ranging from organizational databases to the information on a onlinesite. The collected data is required to be subjected to Data Processing and Data Cleaning.
  • Data Processing:
    The data that is collected must be processed or organized for analysis. This includes structuring the data as required for the relevant Analysis Tools. For example, the data might have to be placed into rows and columns in a table within a Spreadsheet or Statistical Application.
  • Data Scrubbing: Raw data may be collected in several different formats, with lots of junk values and clutter. The data is cleaned and converted so that data analysis tools can import it. It’s not a glamorous step but it’s very important.
  • Data Analysis: Data that is processed, organized and cleaned would be ready for the analysis. Various data analysis techniques are available to understand, interpret, and derive conclusions based on the requirements. Data Visualization may also be used to examine the data in different formates, to obtain additional insight regarding the messages within the data.

Types of Data Science Jobs:

  • Data Analyst
  • Data scientist
  • Data Engineer
  • Machine Learning Engineer
  • Data Science Generalist etc.