Before the data can be used to extract the meaningful insights, it must run through a process that makes it usable to narrate a story. Data Analysis is the process of cleaning, identifying, transforming, and modelling data to discover meaningful insights and information.
Roles in Data Analysis
Telling a tale with data is like a journey that generally doesn’t start with you. The data must come from somewhere, and getting that data into the place which is usable takes a lot of time and effort and which is most likely out of your scope.
In today’s scenario, the applications and projects can be complex and large. This often involve skills and knowledge from several individuals.
Each individual brings a unique talent and expertise, sharing in the effort of working together and synchronize tasks and responsibilities to transform a project from a concept to production.
In recent years, roles such as business analyst and business intelligence analysts were the norms for data processing and understanding.
However, the rise of big data has lead these roles to evolve into more specialized set of skills that overhaul and streamline the processes involved in data analysis and engineering.
Top Roles in Data Analysis
While there are a lot of similarities exist between a business analyst and data analyst, the key difference between these two roles is what they do with the data. A business analyst possess the domain expertise and is very close to the business.
He is a specialist in interpreting the data which derives from the visualization. Often, a single person can take up the roles of both business analyst and data analyst in the process of data analysis.
The goal of a data analyst is to enable the businesses to maximize the value of their datasets through visualization and reporting tools. They are responsible for cleaning, profiling and transforming data based on the format of data analysis model.
They are responsible for designing and building effective and scalable data models for implementing the advanced analytics capabilities into reports.
Data Analyst’s are tasked with turning raw data into meaningful and relevant insights.
They also work with data engineers to determine and locate the proper data sources that meet the stakeholders requirements. Furthermore, they also work with the database administrator to make sure that the analyst has the appropriate access to the needed data sources.
Data engineer facilitates and sets up the data platform technologies that are in cloud and on-premises. A data engineer manages the flow of structured and un-structures data from various data sources.
A data engineer uses different data platforms which includes relational databases, non-relational databases, data streams and file stores. They also make sure that the all the data services are securely and seamlessly integrated across data services.
Data engineers are responsible for the use of cloud data services, on-premises, and tools for ingesting, egressing, and transforming data from multiple data sources.
They collaborate the with stakeholders to identify and meet the data requirements. They design and implement solutions for data analysis.
The work of a data engineer adds massive value to the business intelligence and data science projects.
Data engineer performs data wrangling, in which they bring all the data together. Because of this, the projects move faster as data scientists can focus on their own tasks.
A data analyst works closer with the data engineer in order to make sure that they can access the variety of structured and un-structured data, and they will provide help in optimizing the data models.
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The primary aim of a data scientist is to apply advanced analytics on data to extract value from it. Data scientists perform descriptive analytics and predictive analytics.
Descriptive analytics can be used to evaluate data through a process known as EDA(exploratory data analysis).
Predictive analytics is used in machine learning to apply modelling techniques that can detect anomaly and patterns.
Some data scientist might work in the dungeons of deep learning, performing looping experiments to solve complex problems using deep learning algorithms.
Database administrator implements and manages the operational aspects of cloud-native and hybrid data platform solution that are built on data services and database servers.
A database administrator is responsible for consistent performance, overall availability, and optimizations of the database.
They monitor the overall health of the database and the hardware it relies on.
A database administrator works with business stakeholders to identify and implement various policies, processes, and tools for data backups and data recovery.
They are also responsible for the security of the data, granting and revoking the user access, and managing privileges to the database based on the business needs and requirements.
Where to learn Data Analysis?
We did a lot of research to find the best resources to learn data analysis and compiled the best resources from the cluster. IBM is offering free data analysis courses through its Cognitive Platform.
You can find the resource through this link: Data Analysis With Python
What role are you planning to take on in the field of data analysis? Let us know in the comments below.