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What types of jobs will big data and data science support in 2022?

 

Big Data - Data Science - Jobs in 2022



In today's world, data science is one of the most exciting and forward-thinking domains of technical application.



Meanwhile, this new area is transforming both technology and industries. The job market and the skills required are influenced as industries across all verticals grow more data-driven. The culture we live in, our everyday lives, and the nation's economy become more reliant on data as we uncover new data touchpoints and ways to analyze them. This is also why big data and data science enable a diverse range of work prospects.
While data scientist is hailed as the most exciting job of the twenty-first century, data architect and data analyst roles have gained prominence in recent years. Let's take a look at some of the most frequent data science and big data career roles.

Data Statistician

A data statistician uses statistical theories and methods to gather, analyze, and comprehend qualitative and quantitative data.

Skills: SQL and NoSQL database systems, distributed computing, data editing, and cleaning, analysis tools (MatLab, R), statistical software (SAS, SPSS).

Business Analyst

A business analyst is skilled at connecting data insights to actionable business insights and can communicate the message throughout the enterprise using storytelling tactics.
Skills: SQL, MS Office, Storytelling, Conscious Listening, and Power BI.

Data Engineer

A Data Engineer is responsible for the design, implementation, administration, and optimization of data pipelines and infrastructure that transform and send data to data scientists for querying.
Skills: Programming languages (Java, Scala), NoSQL databases (MongoDB, Cassandra DB), and frameworks (Apache Hadoop).

Data Administrator

Data administrators must ensure that the database is available to all stakeholders in the company, that it is operating properly, and that the required security measures are in place to keep the data safe and secure from hackers.

Skills: ERP, data modeling and design, Java, HTML, and database management system (Oracle 11g, Microsoft SQL Server, IBM DB2, Sybase, and MySQL). Knowledge of operating systems (Windows, Linux, and UNIX), and Information Systems Management (MIS).

Data Architect

Data Architects are responsible for planning, developing, implementing, and managing an organization's data architecture. To combine, centralize, and sustain data sources, they must create blueprints for data management frameworks.
Skills: Relational database management systems (RDBMSs) or fundamental databases, programming languages (Python, Java, C++), Hadoop technologies such as MapReduce, Hive, and Pig, ETL (extract, transform, load), data modeling (Impala, Oozie, Mahout, Flume).

Data Analyst

A Data Analyst processes and analyzes existing datasets, primarily through statistical analysis. A data analyst is also responsible for data querying and forecasting in order to find links, patterns, and trends in data.

Skills: Data wrangling, data visualization (Tableau, Qlik), spreadsheet tools like Microsoft Excel or Google Sheets, programming skills (SAS, R, Python), statistical and mathematical skills, data visualization (Tableau, Qlik), spreadsheet tools like Microsoft Excel or Google Sheets.

Data Scientist

Understanding an organization's goals and deciding how data may be used to attain those goals is what a Data Scientist does. A data scientist also creates predictive models, which frequently include machine learning and deep learning algorithms, for use in prediction, data cleaning, and data analysis, among other things.

Skills: Programming (SAS, R, Python), Statistical and Mathematical Skills, Storytelling and Data Visualization, Hadoop, SQL, Machine Learning, and Predictive Modeling are some of the skills required.











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