Unlocking the Data Vault for You: Discover Hidden Gems in the Data Landscape

by Atinuke Naomi
5 mins read

One major problem people seem to have these days is to choose a career path and stick to their decisions. Why? In a world where opportunities may come in abundance, it is easy to get confused and may not know which to go for. This is tedious for people who are vast in many areas. This article aims to touch on the different areas in the data world for easy streamlining of career paths.

Career Pathways in Data

There are many different career pathways in data, each with its unique focus and responsibilities.

Data Analysis

Data analysis involves the collection, cleaning, and analysis of data to extract meaningful insights. They use their findings to help businesses make better decisions about everything from product development to marketing campaigns.

Data analysis

What it involves
  1. Data Collection: Data analysts can collect data from a variety of sources, such as internal databases, customer surveys, and social media. They may also purchase data from third-party data providers.
  2. Data cleaning: Once data has been collected, it needs to be cleaned and prepared for analysis. This may involve removing errors, formatting data consistently, and filling in missing values.
  3. Data analysis: Data analysts use a variety of statistical and machine-learning techniques to analyze data. They may also use data visualization tools to create charts and graphs that make it easier to understand the data.
  4. Interpret and communicate findings to stakeholders clearly and concisely. This may involve writing reports, creating presentations, or developing data dashboards.

Data Science

Data science is the use of knowledge of statistics, machine learning, and computer science to develop and build predictive models. They use these models to help businesses understand their customers better, identify trends, and make predictions about the future.

What it involves
  1. Identifying business problems that can be solved with data.
  2. Working with business stakeholders to understand the challenges they are facing and to identify opportunities to use data to improve their performance.
  3. Data Collection and Preparation.
  4. Development and training of machine learning models. This is for them to be able to make predictions.
  5. Evaluation and deployment of models: Once a model has been trained, data scientists need to evaluate its performance and deploy it to production. This may involve integrating the model into a software application or making it available to users through a web service.
  6. Monitoring and maintenance of models: Once a model has been deployed, data scientists need to monitor its performance and make adjustments as needed. This may involve retraining the model with new data or updating the algorithm.

Machine Learning Engineering

It involves building and deployment of machine learning models at scale. Machine Learning Engineers (MLEs) work closely with data scientists to understand the business problems that need to be solved, and then they design and implement scalable solutions.

Machine Learning involving data

MLEs need to have a strong understanding of machine learning algorithms, software engineering principles, and cloud computing platforms. They also need to be able to communicate and collaborate effectively with other members of the data science team.

What it involves
  1. Designing and implementation of machine learning systems: MLEs work with data scientists to design and implement machine learning systems that can solve real-world problems. This may involve developing new algorithms, optimizing existing algorithms, or integrating machine learning models into software applications.
  2. Deployment and maintenance of machine learning models.
  3. Building and maintaining machine learning infrastructure: MLEs are responsible for building and maintaining the infrastructure that supports machine learning development and deployment. This may involve managing cloud computing resources, developing data pipelines, and building tools and libraries for machine learning engineers and data scientists.

Data Engineering

Data engineering involves building and maintaining the systems and infrastructure that support data collection, processing, and analysis. Data engineers work closely with data scientists and data analysts to ensure that data is accessible and reliable.

Data engineers need to have a strong understanding of database systems, data processing technologies, and cloud computing platforms.

What it involves
  1. Designing and building data pipelines: This is to collect, process, and store data. This may involve developing custom scripts, using data processing frameworks such as Apache Spark, or integrating with cloud-based data services.
  2. Managing databases and data warehouses: Majorly to store and manage data. This may involve setting up and configuring databases, optimizing performance, and ensuring data security.
  3. Development and maintenance of data quality tools and processes: This is to ensure that data is accurate, complete, and consistent. This may involve developing custom scripts, using data quality tools, or implementing data governance policies.
  4. Working with data scientists and data analysts to support data analysis.

Business Intelligence (BI) Analysis

BI analysts use data to create reports and dashboards that help businesses track their performance and make informed decisions. They work closely with business stakeholders to understand their needs and develop data-driven solutions.

BI analysts need to have a strong understanding of data analysis tools and techniques. They should also be able to communicate and collaborate effectively with business stakeholders.

What it involves
  1. Collection and preparation of data.
  2. Analyzing data and identifying trends: Using different statistical and data mining techniques to analyze data and identify trends. Data visualization tools can also be used to create charts and graphs that make it easier to understand the data.
  3. Creating reports and dashboards: This is to communicate findings to business stakeholders. Reports may include tables, charts, and graphs that illustrate key performance indicators (KPIs) and trends. Dashboards are interactive dashboards that allow business stakeholders to drill down into the data and explore it in more detail.
  4. Presenting findings to business stakeholders: BI analysts present their findings to business stakeholders clearly and concisely. They may do this through presentations, reports, or meetings.





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