Thinking of Making Money as a Data professional: All You Need to know, Careers in Data Explained.

1.     Data Strategist – Understands how to leverage data to drive business value.

Roles: develop and implement data strategies aligned with organizational goals, ensuring data quality, privacy, and compliance.

Skills: Business acumen, data governance leadership, and communication.

2.   2.  Data Architects – Uses it to design blueprints for organization data structure

Roles: Design and manage the data architecture, including databases and data warehouses, to support data storage and retrieval needs.

Skills: Data modelling, database management, ETL(Extract, Transform and Load) process, and technical expertise.  

3.     3. Data Engineer – Actualize the data architects blueprints/sets out data path plans for data analyst and Data scientist to extract, transforms and load)

Roles: Build and maintain data pipelines, ETL processes, and data infrastructure to enable data analytics and reporting.

Skills: Programming (Python, Java), big data technologies (Hadoop, Spark), database systems, and data integration.

4.   4. Data Analyst – Uses this process to extract meaningful insights or trends that can bring Value to business

Roles: Analyze data to provide actionable insights, create reports, and support decision-making within an organization.

Skills: Data analysis, data visualization, SQL, and proficiency in tools like Excel, tableau, or Power BI

5.   5. Business Intelligence Analyst – Takes on a reporting role in an organization, they assess and presents on performance of an organization using dashboards and or key performance indicators into actionable results

Roles: Focus on creating interactive dashboards and reports to visualize and communicate data-driven insights.

Skills: Data visualization, BI tools (tableau, Power BI), data interpretation, and business understanding.

6.  6. Data Scientist – Is a professional who investigates and interprets to solve real-world problems and supports business objectives, more technical (tools, advanced tools and techniques such as statistical modelling and machine learning. They primary use of programming languages framework when working with data

Roles: Apply advanced statistical and machine learning techniques to analyze data, extract patterns, and build predictive models.

Skills: Statistics, Machine Learning, Programming (Python, R), Data preprocessing, and domain knowledge.

7.  7. Machine learning Engineer – Responsible for building and deploying machine learning Models, that learns from data that helps in making predictions and supporting decisions of organizations. Primarily use programming languages and framework to perform their duties.  

Roles: Specializes in deploying and maintaining machine learning models in production systems, often working closely with data scientists.

Skills: Software engineering, Machine Learning frameworks (TensorFlow, PyTorch), and model deployment.

Each of the career play a vital role in the data ecosystem and they can work in any organization even in the most places you never thought of, just dive into it if you think this is for you. just know you can do anything if you set your mind to it. You can start with my skillup link to access free classes on any of the subject listed. 

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