Four Data Science Job Roles You Should Consider

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Looking for a job is stressful. You need to stand out among thousands of applicants in order to get a job. But, the ever-developing field of data science has numerous job opportunities for everyone. There are thousands of data science institutes that provide the right education to data science aspirants. Let us take a look at four significant, and high paying job roles that data science has to offer.

  1. Data Scientist

The job of a data scientist is to understand the challenges that a business goes through, and provide the quickest and the best cure using data analysis and data processing. For example, a data scientist is expected to execute a predictive analysis, and do an extremely detailed examination of a disorganized or unstructured data to produce legitimate insights. A data scientist is required to have the skill of identifying patterns and trends, which, companies need in order to make better choices.

To become a data scientist, one would have to be an expert in subjects, such as, Python, SQL, MatLab, R, and other current technologies. Pursuit of this job title will be easier if one has an advanced degree in either computer engineering or mathematics.

  1. Data Analyst

The job of a data analyst involves various tasks which include munging, visualisation, and data processing in large amounts. A data analyst also needs to investigate the databases regularly. However, the principal skill of a data analyst is optimization. They have to have this particular skill set, because they would create, and modify algorithms which are used to obtain information from some of the largest databases without making any kind of alterations to the data.

In order to become a data analyst, one must have the knowledge of SAS, R, SLQ, Python, and other complementary technologies. Applicants for this job role are also required to have adequate problem-solving qualities.

  1. Data Architect

The job of a data architect is to produce blueprints for data management. Once the blueprints are created, the integration, and centralization of databases become apparently easier. They also help protect the databases with the finest security measures. A data architect also makes sure that data engineers are provided with the best tools and mechanisms to work with.

A data architecture applicant requires proficiency in data modelling, data warehousing, extraction transformation and loan (ETL), and other domains. They are also expected to have knowledge of Pig, Spark, Hive, and others.

  1. Data Engineer

A data engineer builds upgradable Big Data ecosystems for businesses. The result helps data scientists to run their algorithms on stable and highly optimized data systems. Among other works, a data engineer is also required to update existing systems with latest or upgraded versions of contemporary technologies, so that the databases perform with high and improved efficiency.

A career in data engineering requires expertise in areas, such as, R, Java, C++, Ruby, Hive, MatLab, and NoSQL. Knowledge of popular data APIs and ETL tools is highly recommended for a data engineering applicant.

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