Common job Roles comes under data science.

 

Below is a general description of a few main job roles in the Data science field.

1. Data Scientist

Being a data scientist may be intellectually demanding, and analytically fulfilling, and it can put you at the cutting edge of new technological developments. As big data continues to be more crucial to how businesses make choices, data scientists are becoming more prevalent and in demand.

Data scientists decide what questions their team should be asking and then work out how to use data to respond to those questions. For forecasting and reasoning, they frequently create predictive models.

Daily duties for a data scientist could include the following:

• Analyze databases for patterns and trends to gain new insights.
• To predict outcomes, create algorithms and data models.
• Utilize machine learning methods to raise the caliber of data or product offerings.
• Inform senior staff and other teams of your recommendations.
• Use data analysis software like Python, R, SAS, or SQL.
• Keep up with advancements in the field of data science.

***According to Glassdoor, the average compensation for a data scientist in the United States is $122,499 asof April 2022.

 2. Data Analyst

To find the solution to a problem or provide an answer to a question, a data analyst gathers, purifies, and analyzes data sets. They work in a variety of fields, including as government, business, finance, law enforcement, and science.

The practice of extracting information from data to guide better business decisions is known as data analysis. Five iterative phases typically comprise the data analysis process:

Choose the data you want to examine:

• Collect the data
• Clean the data in preparation for analysis
• Analyze the data
• Interpret the results of the analysis

When it comes to what Data Analyst hat actually do, Here’s what many data analysts do on a day-to-day basis:

1. Gather data: Analysts frequently do their own data collection. This can entail completing surveys, monitoring website visitor demographics, or purchasing datasets from data collection experts.
2. Clean data: Raw data may include outliers, duplicates, or errors. In order to prevent inaccurate or distorted interpretations, cleaning the data refers to maintaining the quality of data in a spreadsheet or through a programming language.
3. Model data: This requires developing and planning a database's structural elements. You may decide which data kinds to save and gather, how to tie different data categories to one another, and how the data will actually look.
4. Interpret data: Finding patterns or trends in the data will enable you to interpret it and use it to support your interpretation of the question at hand.

***The average base pay for a data analyst in the United States in December 2022 is $62,382, according to job listing site Glassdoor

3. Data Engineer

Data engineering is the practice of developing large-scale data collection, storage, and analysis systems. It covers a wide range of topics and has uses in almost every business. Massive volumes of data can be gathered by organizations, but to make sure that it is in a highly useable shape by the time it reaches data scientists and analysts, they need the right personnel and the right technology.

The following are some of the most typical duties of a data engineer:

• Architecture development, construction, testing, and maintenance
• Align the architecture with the needs of the business
• data gathering
• Create data set procedures.
• Utilize tools and programming languages
• Determine how to increase data quality, efficiency, and reliability.
• Make inquiries about your industry and business through research
• Utilize vast data sets to solve business problems
• Utilize high-end analytics software, machine learning, and statistical techniques.
• gather information for predictive and prescriptive modelling
• Utilize data to find hidden patterns.
• Find tasks that can be automated using data.
• based on analytics, provide stakeholders with updates

***The average salary for a Data Engineer in the US is $120,880.

4. Machine Engineer

Machine learning engineers are in high demand today. However, the job profile has some difficulties. Machine learning engineers are expected to perform A/B testing, design data pipelines, and implement popular machine learning algorithms like classification, clustering, etc., apart from having a deep understanding of some of the most powerful technologies like SQL, and REST API. , etc.

A few important roles and responsibilities of a machine learning engineer include:

• Design and development of machine learning systems
• Exploring machine learning algorithms
• Testing machine learning systems
• Application/product development based on client requirements
• Extending existing machine learning frameworks and libraries
• Exploring and visualizing data for a better understanding
• Training and retraining systems
• Know the importance of statistics in machine learning

 

***According to Glassdoor the average salary for a Machine Learning Engineer is $107270 per year in US.

 5. Data Architect

A data architect creates data management plans so that databases can be easily integrated, centralized and protected with the best security measures. They also ensure that data engineers have the best tools and systems to work with.

A few important roles and responsibilities of a data architect include:

• Development and implementation of an overall data strategy aligned with the business/organization
• Identification of data collection sources in accordance with the data strategy
• Collaborate with cross-functional teams and stakeholders for smooth operation of database systems
• End-to-end data architecture planning and management
• Maintaining database systems/architecture with efficiency and security in mind
• Regularly audit the performance of the data management system and make changes to improve the systems accordingly.

 

***According to the Galssdoor The average salary for a Data Architect is $117729 per year in US