What is data scientist have to do?

Data scientists have become indispensable assets in practically every firm during the last decade. These professionals are well-rounded, data-driven individuals with advanced technical capabilities who can construct complicated quantitative algorithms to organise and synthesise vast amounts of data in order to answer questions and drive strategy in their company. This is combined with the communication and leadership skills required to provide tangible results to numerous stakeholders throughout a company or organisation.
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Data scientists must be inquisitive and results-driven, with great industry-specific expertise and communication abilities that enable them to convey highly technical outcomes to non-technical colleagues. They have a solid quantitative background in statistics and linear algebra, as well as programming skills with a focus on data warehousing, mining, and data visualisation.

Data scientists work closely with business stakeholders to understand their goals and determine how data can be used to achieve those goals. They design data modeling processes, create algorithms and predictive models to extract the data the business needs, and help analyze the data and share insights with peers. While each project is different, the process for gathering and analyzing data generally follows the below path:

  1. Ask the right questions to begin the discovery process
  2. Acquire data
  3. Process and clean the data
  4. Integrate and store data
  5. Initial data investigation and exploratory data analysis
  6. Choose one or more potential models and algorithms
  7. Apply data science techniques, such as machine learning, statistical modeling, and artificial intelligence
  8. Measure and improve results

A Data Scientist’s specific tasks vary greatly depending on the industry they’re in and the company they work for. Generally speaking, though, a Data Scientist can expect to encounter some or all of the following tasks and responsibilities.

  • Researching an industry and company to identify pain points, opportunities for growth, and areas for improvement in efficiency and productivity (among other things).
  • Defining which data sets are relevant and useful, then collecting or extracting that data from various sources.
  • Cleaning data to remove anything unusable, and testing it to confirm that what remains is accurate and uniform.
  • Creating and applying algorithms used to implement automation tools.
  • Modeling and analyzing data to identify latent patterns and trends.
  • Visualizing data or organizing it into dashboards that other members of the organization can consult.
  • Presenting findings and making recommendations to other members of the organization.

Data scientists examine which questions need answering and where to find the related data . They have business acumen and analytical skills as well as the ability to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data.

In simple terms, a data scientist’s job is to analyze data for actionable insights.

Specific tasks include:

  • 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

A Data Scientist is a professional who collects large amounts of data using analytical, statistical, and programmable skills . It is their responsibility to use data to develop solutions tailored to meet the organisation’s unique needs.

The work of a data scientist can vary greatly but at the core a data scientist uses statistical and mathematical methods to analyze data to in order to understand “the story” the data is “telling” or indicating. Often they work to prove or validate a hypothesis.

They often are dealing with large amounts of data collected over month or years. This data may be combined with other data sets (i.e. car performance data combined with car purchasing data to determine if car performance influences customers to buy more of the high-performing cars). So a data scientist must be versed at 1) processing or “cleaning/preparing” the data, 2) analyzing the data using statistical methodologies, 3) interpreting the outcomes of the analysis, and 4) creating a summary of the findings to be shared with other members of their team. Some data scientists perform all of these tasks, while others may specialize in only one of these areas and then work with others to complete the work.