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When you think of a data carrier, which job titles are you in the first mind? Data analyst, of course data scientist? It’s about it. Even the data engineer or machine learning engineer seems to have a selection of some left field.
If most of you think the same way, it is not surprising that data scientists are so difficult to do.
Today, we will look at some of the career alternative routes. They can give you a better job of employment and even give you a more interesting career than the defeated tracks.
1. Data Product Manager
This role is a moment between business, engineering, and data teams. They describe data product LATA data requirements. Whether or not this position may face a customer, it all depends on the product you work on.
For example, customer -facing data products will have data API, machine learning model interface, or client interface and dashboards. In your work, you will focus on the device’s leases, scales and reliability. In short, user experience.
In the role of non -customer, you can work on internal dashboards, internal self -serving analtics tools, data pipelines, or machine learning model output. The focus here is to deal with cross -function needs, get quick insights, and keep reliable data.
This is a character to clarify the image, this is a character where to deal with your needs: like:
- We need a filter through coat in this dashboard.
- This API needs pages and access control.
- The drug prediction model needs to be explained for the customer’s success team.
Skills Required:
1. SQL and Data Analytics
2. Stakeholder communications
3. Product Management
4. Basic UX for dashboards
How to learn in free:
2. Data Journalist
Data journalists tell a story with data using their data analysis and image. They may be themselves “regular” journalists or analysts who work with journalists to find samples in public data, confirm the claim with evidence and digest information by viewing data.
He can work in press and electronic media newsrooms, investigating units (such as, propbika, ICIJ), non -profit organizations and think tanks.
Project data can include reporters to unveil the corruption records, analyzing government spending records, creating interactive electoral concepts, reporting on climate change, etc.
Skills Required:
1. Cleaning data
2. Data concept
3. Telling the story and writing
- To indicate angle or statement in dataset
- Headwaters and leads that attract attention
- To describe stats in simple language
- The story with reference to experts or members of the community for humanity
4. Finding data
How to learn in free:
3. Analytical engineer
Data engineers handle raw data and storage, while analysts run questions and look for data insights. Then what are the analytical engineers? They turn raw data into a data stack analysis and clean datases ready for their analytical layer.
For analytics engineers, designing and maintaining DBT model for data change in normal tasks, the specification of matrix and business logic, and the construction of data marts and cement layers. They also cooperate with data engineers (up streams) and analysts/product managers (Bahau)
In a way, analytical engineers are data analytics software engineers.
Skills Required:
1. Advanced SQL (for change logic)
2. Data Blood Tool (DBT) (for Analytics Engineering)
- I write the model DBT
- Dependent and the formation of reef () chains
- Model Directory construction and maintaining (Staging -> Intermediate -> Marts)
- Test writing (unique, not_null, accepted_ale)
3. Got and version control
- Use gut to press/stretch the code and manage branches
- Commit to the messages
- Open the bridge requests to review the code review
- To resolve the integration conflict
4. Data Warehouse
- Improve the questions
- Managing datases, Permissions, and Schemes
- Additional models and substances
- Big Curi
- Asnophilic
- Redshift
5. Bonus skills:
How to learn in free:
4. Operations analyst
Operations analyze analysis workflows (eg, supply chain, staff, customer service), identify subupatimal performance, identify lost resources and barriers, and recommend solutions.
In short, they use data to improve business tasks. Some common examples include delivery correction, cost reduction analysis, and manpower planning.
In his work, Operations analysts will submit reports about the KPI, scenario models (such as if the company has reduced shifts), real -time operation monitoring, and reports about automated works.
Skills Required:
1. Excel and SQL
- Construction of axis tables and summary reports
- Database pulling data from the database
- Cleaning and analyzing data
2. Data Visual Tolls
- Types of charts
- Dashboards with filters and drill downs
- Connect with data sources and automate updating schedules
- Table
- Power bi
- Watching studio
3. Prediction and scenario modeling
4. The process automation
How to learn in free:
5. Data Ethics/AI policy analysts
In this work, you will ensure that the algorithm and data system be used in a fair and accountable manner according to human rights and social values. This role focuses on the moral aspects of data -powered technology development, deployment and regulation.
These experts usually work through governments, educational institutions, think tanks and non -governmental organizations. You can also get jobs in private companies that focus on ethics, not just profit.
Common tasks include prejudice or various effects of Machine Machine Learning Model, consulting products and legal teams about compliance with data privacy regulations (eg, GDPR), and contributing to AI policy suggestions, model documents, or audit framework. Explanation and Model to promote transparency. You will also work with Data Scientists
Skills Required:
1. The basic understanding of algorithm and model bias
2. Legal and moral framework
3. Communication and policy writing
- Model documentation and writing evaluation
- Translate the dangers of the technical model into simple language
- Draft Ethics Leaderships, Policy, or Position Papers
How to learn in free:
Conclusion
If you want to work with data, do not restrict yourself to just a few career options. Not everyone is required Be a data scientist. It has become so hype, you think it’s the only option. No, it’s not. The five alternatives we have mentioned here show how diverse the data carrier can be. These alternatives allow you to use your technical knowledge with concrete results, and even help to create a better society.
Net Razii A data is in a scientist and product strategy. He is also an affiliated professor of Teaching Analytics, and is the founder of Stratskrich, a platform that helps data scientists prepare for his interview with the real questions of high companies. The net carrier writes on the latest trends in the market, gives interview advice, sharing data science projects, and everything covers SQL.