We are in a technology-driven age and are ever managing the growing needs of the companies and consumers with regards to the same. In such a scenario the role of data analysts becomes very perilous to manage the demands. A data analyst is someone who is in charge of collecting and analyzing the data, responsible for performing statistical analysis on the data. It is not essential that the skills of a data analyst are as evolved as a data scientist, a data analyst can or cannot create algorithms. Although they share the same goal of discovering insights from the data and strategically use them to create solutions.
Usually, data scientist works with the IT teams, data scientist or the management, to define organizational goals, data mining, identifying new trends and opportunities, designing and creating databases. Now, these skills come handy when considered as a base to progress in diverse directions in the analytics field.
There are various professional possibilities that can be easily handled by a data analytics professional. If you are a newcomer in this filed, or are trying to explore the field of data analytics, and are wondering about the future options for either career progression, or any alternatives to the analytics job, then this article will help you gauge the opportunities in a Data Analytics field.
Affiliated with the role of a database administrator, this role is a possibility but has nothing in common with the data analyst role. One does not need proficiency in programming languages like R or Python. SQL orientation is, however, a plus. This is an IT role, where the person manages data and the infrastructure that manages IT.
While as a data management professional you will manage data infrastructure, as a data engineer, you will design and implement the data infrastructure. A step up in complexity from the data management professional, a data engineer is a non-analytical big data career opportunity. You cannot say one of the two is superior, it is your knowledge, skill, and preference that should be the deciding factor. Both these roles are similar in the technologies and skills to an extent. However, the application and complexity of the same are different.
If you thrive when working with big data frameworks, analysis and presentation, creating dashboards, querying of databases is your forte, then this is the perfect career opportunity for you. The above two options will help you manage data and designing data, the role of a business analyst will be extracting information from the data other than what it already says superficially. There are unique skills requirements which can be learned if you wish to pursue in this field.
Investigating data is the base of a role as a practitioner in machine learning, in addition to this capability you will also need to be hands-on with proficiency in statistics, writing machine learning algorithms, etc…, this is where big data becomes sophisticated, insightful, where tools and experience are used together to leverage data. Therefore, statistics and programming both become essential assets for a machine learning professional, if those are your interests then go for it as machine learning integration in technologies is going to be huge over the next couple of years.
This term means nothing specific in general but uses all the roles and technologies listed above. From fluency in programming languages to querying and statistical capabilities, to extracting, managing and designing, and conducting initial exploratory analysis, and deciding which machine learning algorithm to use to perform predictive analysis, from visualizing the results to giving the presentation to the management with the end result, all comes under the job responsibilities of this role in addition to having the domain knowledge.
The options mentioned above are only a few of the possibilities but will serve as a good starting point for anyone exploring to understand options available to a data analyst.