How do people manage to transfer their skills over?

[flibbage0]

As a senior Pharmacist with extra qualifications and experience in private outfits, retail pharmacy, and the NHS including mental health work alongside psychiatrists and psychologists, I am currently seeking to transition out of the pharmacy field due to a ceiling in terms of pay and progression (~£55k).

I have observed people from science backgrounds, such as biochemistry or chemistry, transition from hospital work to data analysis roles, including positions in insurance companies, or even completely changing careers to become IT sales engineers or similar roles. However, I am curious about how they manage to make such transitions with little experience in those fields without starting from the bottom again. Is it through nepotism or the gift of gab?

For example, whilst I have previously secured a job as a 1st line helpdesk support worker, I have also applied for roles such as a junior data analyst within the NHS, which I thought would be an easier transition due to my prior experience working for the NHS and knowledge of frontline issues and cost-saving initiatives (based on projects and audits that I have undertaken). However they don't seem to be interested and on LinkedIn all I receive are recruiters messaging me non-stop asking if I could work as a retail pharmacist (Boots, Lloyds etc), which I have no interest in doing as I have already done it.

Any advice you can provide would be greatly appreciated.

 

[dowie]

For example, whilst I have previously secured a job as a 1st line helpdesk support worker, I have also applied for roles such as a junior data analyst within the NHS[...]

Any advice you can provide would be greatly appreciated.

I'm not sure why you'd apply for an IT helpdesk role if you're already a pharmacist and want a data analyst role.

The thing that does seem to be missing here is what you're offering beyond your domain knowledge? All the big pharma companies will employ data analysts/data scientists so there are various roles out there where your existing domain knowledge may well be useful.

"Data Analyst" is quite a broad job title but can often require some knowledge of statistics, some programming ability and some familiarity with SQL and data visualisation packages.

"Data Science" roles can overlap with that but typically require a masters or PhD and a deeper knowledge of statistics + machine learning.

So perhaps get in an online course (see for example udacity https://www.udacity.com/course/data-analyst-nanodegree--nd002 ) and apply for data analyst roles.

For data science roles you could perhaps work as a data analyst while taking an MSc in statistics, data science, machine learning or similar across say 2 years online or part-time.

Something like this would be idea:

Machine Learning and Data Science (Online) MSc | Study | Imperial College London

www.imperial.ac.uk

Your undergrad may not be suitable for a serious quantitative MSc so one option would be to take a grad certificate in mathematics/statistics - for example:

Statistics for Data Science (Graduate Certificate)

Our Graduate Certificate in Statistics for Data Science introduces you to methods for the practical implementation of various types of statistical analysis.

www.bbk.ac.uk

^^^ that's designed for MSc applicants who lack the required undergrad, could also perhaps be useful in and of itself for help landing a data analyst course. Alternatively, you could perhaps take some relevant OU courses (some combo of them can result in a certificate too: https://www.open.ac.uk/courses/stat...ate-in-theoretical-statistics-probability-s04 you'd probably want to self study some other undergrad maths/stats if you attempted that one as it's basically two big 3rd year modules)

Another alternative pre-MSc course:

GradDip Mathematics

Understand the principles of advanced mathematics and prepare for a career in statistics, accounting and related sectors.

www.london.ac.uk

You don't need to go through an entire degree again but you are likely lacking the quantitative skills that you'd need for a masters and there are short undergrad-level courses resulting in certificates or diplomas that can solve that.

Other alternatives - there are lots of "conversion" MSc courses in Computer Science, these are basically core undergrad modules aimed at people like you who already have degrees, this might not necessarily get you into a data science role but it could perhaps help get you into other tech roles.

Likewise, if you fancy becoming a Business Analyst or Systems Analyst (can sometimes have overlap too with Data Analysts) then here's another short undergrad-level course:

Undergraduate Advanced Diploma in IT Systems Analysis and Design (Online)

A one-year part-time course for those wishing to develop their understanding of modern systems analysis, development and management. Delivered entirely online, the course is taught at third-year undergraduate level.

www.conted.ox.ac.uk

(This probably wouldn't help with a stats/ML/data science MSc though).

 

[Lopéz]

Here is the approach I use.

I take the competencies/requirements of my current/past job in order to identify my transferable skills.
Really think about what they involved, how I applied them, how I demonstrated them.
Keep a very wide lens on this, some people are incredibly poor at looking at their job role from a wider lens.

I'll give an example:

"As a TNT call centre agent I tracked parcels for customers" - no, this is not going to get you anywhere unless you want to do the same job at DPD.

"As a TNT call centre agent I handled inbound enquiries, using a range of logical funneled questions to identify problem consignments and retrieve them successfully from a database, made fact-based decisions using the system data available to me, translated internal industry jargon into digestible customer friendly statements, and presented that information to customers in the form of accurate and timely updates delivered in a confident and clear manner"

Do this exercise for the various subtasks you've performed in your role over the years.

Then map that to requirements that are shared, similar to, or have other parallels with the requirements of your new job.

If you can't think about your experiences for yourself through this broad lens, you will have a good deal of difficulty "selling" them as transferrable skills.

You might think of this as having "the gift of the gab " so to speak, but it really isn't, it's evidencing your competencies in a generic enough fashion to allow a potential employer to see the value you could bring to their organisation.

Hope that helps a bit.

I worked with someone once who scoped and specified an entire quote to order process for a jewellery retailer, including presenting items in a digital storefront, selecting an item using search criteria, checking out, stock checking, order fulfillment and so on, yet when asked about her industry experience she claimed she knew nothing about either retail or the jewellery industry.

Absolute nonsense - she just couldn't identify her transferable knowledge and experiences in the first place.

 

[Deleted member 77746]

Documentation.

Other than that people should already have the small skill sets for the post. The rest should be in plain text ready for handover.

 

[flibbage0]

Here is the approach I use.

I take the competencies/requirements of my current/past job in order to identify my transferable skills.
Really think about what they involved, how I applied them, how I demonstrated them.
Keep a very wide lens on this, some people are incredibly poor at looking at their job role from a wider lens.

I'll give an example:

"As a TNT call centre agent I tracked parcels for customers" - no, this is not going to get you anywhere unless you want to do the same job at DPD.

"As a TNT call centre agent I handled inbound enquiries, using a range of logical funneled questions to identify problem consignments and retrieve them successfully from a database, made fact-based decisions using the system data available to me, translated internal industry jargon into digestible customer friendly statements, and presented that information to customers in the form of accurate and timely updates delivered in a confident and clear manner"

Do this exercise for the various subtasks you've performed in your role over the years.

Then map that to requirements that are shared, similar to, or have other parallels with the requirements of your new job.

If you can't think about your experiences for yourself through this broad lens, you will have a good deal of difficulty "selling" them as transferrable skills.

You might think of this as having "the gift of the gab " so to speak, but it really isn't, it's evidencing your competencies in a generic enough fashion to allow a potential employer to see the value you could bring to their organisation.

Hope that helps a bit.

I worked with someone once who scoped and specified an entire quote to order process for a jewellery retailer, including presenting items in a digital storefront, selecting an item using search criteria, checking out, stock checking, order fulfillment and so on, yet when asked about her industry experience she claimed she knew nothing about either retail or the jewellery industry.

Absolute nonsense - she just couldn't identify her transferable knowledge and experiences in the first place.

I definitely think this is probably something I could improve on. I like completing the task as quickly and efficiently as possible and moving onto the next task/project however I don't realise how much work I have actually done to get that far. Pretty much a blur. The problem is that I'm used to being ABC, 123, bullet point type maybe due to time constraints working in the NHS?

 

[HangTime]

I saw some discussion of Data Science above. Anecdotally, it is an ageist field that can be tough to break into as an experienced candidate. A former employee of mine (with considerable experience in Data) undertook a postgraduate qualification in data science as he was interested in pivoting into that field, but then found he couldn't get a job in Data Science. He was great, very smart (96th percentile for critical thinking), hard working, enthusiastic, decent people skills, very good at digging into detail etc. Probably the best person I ever hired, so I'm pretty sure it wasn't an aptitude problem, more that someone in their late-40s didn't really fit the profile of what was expected in a junior/mid-level DS role.

 

[OspreyO]

We have a data Science analysis. Almost impossible to get into it unless you have list qualifications and certifications that would fill the boot of your car.

In addition our HR department filter out anyone without a whole load of other qualifications. I think HR have been told to give new hires (younger people) a preference.

 

[flibbage0]

I should have clarified that I'm under 30, senior in level not age (although I sometimes feel like it)

 

[dowie]

We have a data Science analysis. Almost impossible to get into it unless you have list qualifications and certifications that would fill the boot of your car.

BSc + MSc or BSc (+ MSc, maybe) + PhD is the usual requirement... so that's 2 or 3 qualifications.

 

[XeNoNF50]

As a senior Pharmacist with extra qualifications and experience in private outfits, retail pharmacy, and the NHS including mental health work alongside psychiatrists and psychologists, I am currently seeking to transition out of the pharmacy field due to a ceiling in terms of pay and progression (~£55k).

I have observed people from science backgrounds, such as biochemistry or chemistry, transition from hospital work to data analysis roles, including positions in insurance companies, or even completely changing careers to become IT sales engineers or similar roles. However, I am curious about how they manage to make such transitions with little experience in those fields without starting from the bottom again. Is it through nepotism or the gift of gab?

For example, whilst I have previously secured a job as a 1st line helpdesk support worker, I have also applied for roles such as a junior data analyst within the NHS, which I thought would be an easier transition due to my prior experience working for the NHS and knowledge of frontline issues and cost-saving initiatives (based on projects and audits that I have undertaken). However they don't seem to be interested and on LinkedIn all I receive are recruiters messaging me non-stop asking if I could work as a retail pharmacist (Boots, Lloyds etc), which I have no interest in doing as I have already done it.

Any advice you can provide would be greatly appreciated.

I would suggest learn SQL. Get familiar with it. Enough to put it onto your CV. Plenty of resources online to get you there. Remember you now have chatgpt / bing chat and other AI learning applications as well as stack overflow to assist you.

Data visualisation / dashboards is a great skill to have too. Really easy to get going with that, launch Google data studio, connect up to data sets as simple as a Google sheet and have a play, find some public data sets maybe.

SQL is not overly difficult to learn. It is a valuable skill, the one I predominantly look for when hiring for analysts within my team.

Too often, particularly on the junior end of the scale, analysts are MS Excel based and don't have a second clue how to write SQL. Not ideal when their job will be working with tens of billions of rows of data.

Even if you don't use this in your day to day right now and can't show professional experience in using it, there is great value in knowing how to do these things, getting it onto your CV, be able to hold a conversation on it and show enthusiasm for it.

I wouldn't worry too much about getting into data science specifically. Data requires both the technical side and soft skills side and data science is particularly technical. It is not a necessary path for earning more and progression.

Particularly as you become more managerial, you will likely do less technical analysis and more translating analysis in a format much suited for communicating with executives. I've barely written a query this year whereas a few years back I'd be knee deep writing triple digit lines of code in a single query.

 

[Nemiyen]

SQL is not overly difficult to learn. It is a valuable skill, the one I predominantly look for when hiring for analysts within my team.

That was a ninja edit for what the S stands for

I'm a bit delayed in my reply as my parents just popped round as I was quoting you and missed it.

 

[XeNoNF50]

That was a ninja edit for what the S stands for

I'm a bit delayed in my reply as my parents just popped round as I was quoting you and missed it.

Haha it is just what I say to my most junior analysts even though it isn't true then realised someone on the Internet might jump on me ha.

 

[OspreyO]

Look before you leap. Data analysis is perfect for ai automation going forward.

 

[jon86]

I would suggest learn SQL. Get familiar with it. Enough to put it onto your CV. Plenty of resources online to get you there. Remember you now have chatgpt / bing chat and other AI learning applications as well as stack overflow to assist you.


SQL is not overly difficult to learn. It is a valuable skill, the one I predominantly look for when hiring for analysts within my team.

I would say that like any programming language, there's levels of knowledge that you can attain through reading and tinkering, and levels of knowledge that you only gain through experience. It's true that it's not overly difficult to learn a moderate level of knowledge of SQL, and familiarity with concepts such as; SELECT, JOINs, views, aliases, functions, stored procedures, etc, but it takes time and opportunity to really understand a lot of the much deeper, more complex areas, and when/why you need to use them, such as: triggers, CTEs, indexes, locks, materialised views, rules, etc.

My tl;dr is to be honest about your level of skill and experience with any programming language, whether that be with a recruiter, interviewer, or hiring manager. As a hiring manager myself, I'd rather someone say to me that they have read some books, experimented with some toy datasets, and are familiar with some of the concepts, than to claim that to claim that they're an expert in a language, only to be found out that they're not.

 

[XeNoNF50]

I would say that like any programming language, there's levels of knowledge that you can attain through reading and tinkering, and levels of knowledge that you only gain through experience. It's true that it's not overly difficult to learn a moderate level of knowledge of SQL, and familiarity with concepts such as; SELECT, JOINs, views, aliases, functions, stored procedures, etc, but it takes time and opportunity to really understand a lot of the much deeper, more complex areas, and when/why you need to use them, such as: triggers, CTEs, indexes, locks, materialised views, rules, etc.

My tl;dr is to be honest about your level of skill and experience with any programming language, whether that be with a recruiter, interviewer, or hiring manager. As a hiring manager myself, I'd rather someone say to me that they have read some books, experimented with some toy datasets, and are familiar with some of the concepts, than to claim that to claim that they're an expert in a language, only to be found out that they're not.

Pretty much what I was getting at. Trying to jump career to this cold from doing nothing relevant is likely not to work. But showing a desire and interest and having played and tinkered with it enough to put something on your CV and be able to hold a conversation on it will likely yield better results in terms of getting started.

 

[Mrwong]

Before you take the step of switching, I have pointed out a broad range, what you need to learn.

Data science is not excel. Data science is a joining of all areas into one.
Multi disciplinary area.

Statistics, data base query languages (relational data and graph data), r and python languages including common libraries. This does not end there, you need to know the mechanism of statistics in order to clean data, such as replacing missing values with fillers as not to alter the data.

Then you need to know how to design visualisations and how to visualise the data without overcomplicating the graphs/ dashboards.

Then need to know NLP, ML as a min, you need to understand categorisation methods etc and advance statistics that go with it and understanding training models (such as reading training lines from the graphical output).

Abstract maths, domain knowledge architecture/ engineering.

Then you need a basic understanding information governance.

Understand

Logic Programming - Introduction

Logic Programming for AI Topics. Group 11: Simran Cashyap (suc05), Max Sheremet (ms1005) and Charence Wong (cclw05).

www.doc.ic.ac.uk

You can see the list of skills you need from graphical design, statistics, computer studies to mathematical Philosophy and some others. I have covered a good range, however there are a few more depending on area of industry.

 

[flibbage0]

Before you take the step of switching, I have pointed out a broad range, what you need to learn.

Data science is not excel. Data science is a joining of all areas into one.
Multi disciplinary area.

Statistics, data base query languages (relational data and graph data), r and python languages including common libraries. This does not end there, you need to know the mechanism of statistics in order to clean data, such as replacing missing values with fillers as not to alter the data.

Then you need to know how to design visualisations and how to visualise the data without overcomplicating the graphs/ dashboards.

Then need to know NLP, ML as a min, you need to understand categorisation methods etc and advance statistics that go with it and understanding training models (such as reading training lines from the graphical output).

Abstract maths, domain knowledge architecture/ engineering.

Then you need a basic understanding information governance.

Understand

Logic Programming - Introduction

Logic Programming for AI Topics. Group 11: Simran Cashyap (suc05), Max Sheremet (ms1005) and Charence Wong (cclw05).

www.doc.ic.ac.uk

You can see the list of skills you need from graphical design, statistics, computer studies to mathematical Philosophy and some others. I have covered a good range, however there are a few more depending on area of industry.

Thanks I will have a look at that website, would you say it's one of those things you either have a knack for or don't? For instance in the healthcare field we were always told you either understand the concepts of cardiology really easily or you don't get it at all.

The only thing I can compare it to is the old adage of Marmite you either love it or hate it.

 

[Mrwong]

Thanks I will have a look at that website, would you say it's one of those things you either have a knack for or don't? For instance in the healthcare field we were always told you either understand the concepts of cardiology really easily or you don't get it at all.

The only thing I can compare it to is the old adage of Marmite you either love it or hate it.

Your best bet start with Stats then move to R and python.

Download anaconda.
Run python, Start with 1 data set, (make a data set is very easy) play around with it. learn the difference of each of these {} [] (), then create 2 or more datasets play around with them.
Learn how to populate sets with empty brackets in python.
There are lots of steps involved.
Once you are comfortable with above, move onto libraries pandas numpy, matplotlib, eventually learn to import excel data and manipulate data.
Once you have some understanding of the above, move onto databases, and how to query databases.

The logic programing should come after you understand the above as it is a very specific field.

 

[dowie]

Thanks I will have a look at that website

I think learning Prolog is fairly low down on your list of things to look at at least w.r.t data science in industry tbh... it was popular in CS departments in the 90s tho I gather, before ML moved towards statistical methods in the 00s/early 10s and then later (and now) neural nets made a comeback.

 

[Mrwong]

I think learning Prolog is fairly low down on your list of things to look at at least w.r.t data science in industry tbh... it was popular in CS departments in the 90s tho I gather, before ML moved towards statistical methods in the 00s/early 10s and then later (and now) neural nets made a comeback.

Data science moving into A.I, or A.I into data science.