Looking to Move into Analytics From a Different Domain?
Originally posted by our Co-founder, Sarita Digumarti on LinkedIn.
Sarita shares how people from different domains can find suitable jobs in the field of analytics. In the article she goes on to explain the wide variety of analytics roles across domains and how one can identify the roles that best fit their skillset.
One of the most frequent questions that we receive regularly is about what sort of analytics roles are available and suitable for someone that has experience in other functions. Many people understand the need to constantly keep learning and adding skills for enabling career growth, but at the same time face difficulty in figuring out how to make sure they are choosing the right area to upgrade skills and how that will fit into their overall career trajectory.
If you have identified analytics as an area of interest, and are currently in a job that does not involve analytics or data mining, here are some pointers on what sort of job roles within analytics and data mining could be suitable, and how to identify these roles.
Understand the types of job roles available within the analytics function:
Analytics and data science are very broad terms, and are used to describe a wide range of activities and roles, with of course a common theme being data handling. Within this function, there are a wide variety of roles including
A: Primarily BI focused: Business Intelligence is the backbone of data driven decision making, and is the analysis of historical data to assess “what happened” and to some extent understand “why it happened”. BI roles require knowledge of data analysis techniques and an understanding of business context specific to the domain that the analyst is working in. BI roles also require knowledge of specialized BI tools and visualization skills
B. Primarily Predictive Modeling focused: Data Science roles include roles that require knowledge of advanced analytics and machine learning algorithms that not only help companies understand “what happened”, and “why it happened”, but also “what will happen next?”. These roles require advanced statistics and modelling skills, and advanced analytics application knowledge, along with data analysis and visualization skills and of course business context knowledge.
C. Primarily Data Processing and Preparation driven: Some roles in data science and analytics may require advanced knowledge of data storage, handling, and preprocessing, especially when dealing with large volumes of data from multiple sources. While in some instances a data scientist role may require knowledge of both data preprocessing and modelling skills, in many companies, especially with large analytics teams, there tends to be more specialization, and therefore job roles that are more focused on data handling and processing. These roles will require strong knowledge of databases and database querying.
D. Primarily Application Development focused: For many clients, an analytics solution cannot just be a statistical model, but has to be developed into implementable strategy. And very often that means that an application needs to be developed that allows the business users to use the outcome of data analytics. So within analytics teams there will be job roles that require knowledge of specialized data science tools that allow application development. In these roles, programming skills are very important, along with knowledge of analytics techniques and application
E. Primarily R&D focused: These roles are highly technical, super specialized roles that require advanced (Ph.D) level of knowledge of statistics and computational techniques, in order to build and develop new algorithms and solutions to complex business problems.
F. Big Data Focused: Increasingly, as the volume and the variety of data being collected and processed is increasing, there are many highly specialized and technical tools being used to store, manage, and analyse these Big Data sets. Within Big Data roles again there may be roles that are more specific to Big Data Processing, and Big Data Analytics. Knowledge of Big Data technologies is a must, along with strong programming skills and statistical skills and tools.
G. Sales roles: Many companies will also have some people that are part of a pre-sales/sales team that can meet clients to pitch analytics and data science solutions. Typically these roles require people with a mix of both analytics knowledge and sales experience
H: Managerial roles: Finally, there is always a need for people that can manage and deliver data science projects. These roles require prior experience with team and project management, and a good understanding of analytics tools, algorithms and applications.
This was a broad list of job roles within data science teams. Once you have a good understanding of these, the next step is to:
Identify which of these roles is most suitable or appropriate given your past experience:
For example, if you primarily come from an IT application development background, a good role for you could be either something that requires Big Data skills, or application development role within a data science team. If on the other hands, let’s say your prior job experience has been in the financial services sector, perhaps in insurance, or retail banking, then it may be a good idea to aim for a BI or data analyst role where knowledge of business process and strategy within a particular domain is a key skill set
Once you identify what role is the most appropriate to target, the next step then is to:
Make a list of skill sets and concepts that you will need to learn in order to be able to apply to those job roles:
This is where you will be able to focus on the skillsets that you require for a specific role, rather than blindly deciding on them based on “popularity”. This enables you to spend time building up knowledge on topics that will help you make an intelligent career move!
Here is a link to an example of someone that was able to successfully make the switch from a unrelated domain into analytics
If you would like to pick up your skills in analytics but not sure where to start, try Jigsaw Academy’s course Selector to help you build your career path
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