Below are questions from our audience ranging from career, salary, software, skills, and more. 

Audience question: What is the difference in job profile between a data scientist and data analyst? If there is a difference, what should a data scientist do in the data analytics field?

JP: One of the things that we wanted to do at the start of our data analytics course was to disambiguate between these two terms between data analyst and data scientist.

The truth of the matter is that they are fairly nebulous terms, but I’ll try and do what I can in terms of the best of my understanding.

Data science is generally a broader term than data analysis. We talked about the process of data analysis in terms of problem definition, all the way out between data cleaning, data managing and analysis, all the way to delivery of data communications.

Data science tends to be a little bit broad, broader (excuse me), and a lot of what data scientists do is things like develop models.  You know if you’re talking about people who develop state of the art models in image recognition. Sorry, I’m getting fairly technical. Like if you see those bots or a website where you’re putting (uploading) a picture and it tells you what it is.

Or, if you’re driving a Tesla and it’s able to drive around and figure out if they’re driving on a road or if they’re about to run into another car or follow lanes and stuff—these are all driven by AI models developed by data scientists

That’s probably how I understand data science and data analysis.

And in terms of what should data scientists do to get into a data analyst field, I think some of the skill sets are slightly different. Data analysts tend to work with things like SQL a little bit more. They’re dealing with kind of more predefined roles and analysis pipelines because they’re more often in a business role answering defined business questions with these tools.

Whereas data scientists’ roles, like building models so it tends to be a little bit different from that sense.

Audience question: We often hear from L&D managers, ‘look, our executive has said, our people need to be more data literate and make more data driven decisions across the board, where do we even start? People have varying levels of competency already, how do we even begin on this journey?

JP: There’s a couple of things that can do that can help, obviously, one thing is just kind of reducing that intimidation factor in dealing with data. I think a lot of that is going to be just training and continued development for those people and supporting them throughout.

In terms of how to figure out where they’re at in terms of their comfort with data and abilities, I think it’s a good idea to have conversations with people about how comfortable you are in dealing with these tools that might be useful on top of or in your day-to-day tasks.

And then you can ask them questions like, “how comfortable are you with Excel, formulas, pivot tables and VLOOKUP formulas and so on and so forth?”

It might be that you’re dealing with databases all the time. How much do you use SQL you know? It’s probably quite intimidating to be formally assessed in something, but I think a lot of training would be a really good way forward for many organisations and it can be a collaborative process to put them in groups and work on kind of relevant business problems together.

Audience question: Do data scientists need to have knowledge of statistics and what level of it do you need to become a data analyst?

JP: I guess by nature if you’re dealing with data science and data analytics types of fields, it is undoubtedly going to be computer heavy so levels of computer literacy is really helpful.

But I guess the question is, can someone without an IT background join the data world, I think the answer is yes, none of us are born with these skills, right? These are not innate skills that are encoded to our genes or anything so for sure it is something that we can learn.

And I’ve seen people do it in the Data Analytics: Transform because a lot of the students in our cohort that I’m mentoring come from a non-technical background, I would say, actually, most of them come from non-technical backgrounds. And a lot of them pick it up really well and they do fully admit that it is not easy, but it’s something that they pick up and they do really well so that’s great to see.

In terms of statistics, there’s two parts of this question I can see, so I’ll try to answer the first part about the knowledge of statistics. Depending on what you’re doing in data science. If you’re building models and using machine learning to do things like regression models which is when you predict things like numbers, so when you predict things like housing prices, based on where it is, how big the houses are, blah blah blah. Or if you’re using models at any level you want a fairly good intuition of statistics and maths.

But if you actually work on some of these fields, what you’ll see and what’s required isn’t a university graduate level of statistics or algebra or calculus. What you do need is basically a low level understanding of some statistics and some algebra and that’s kind of all you need.

If you of course, are doing some role that is fairly statistics heavy and that might be in the fields of experiments where you are required to do things like, “does this experiment tell us with a certain degree of confidence that this drug is going to work”–or something like that those fields are, of course, more statistics heavy and critical, so it really depends.

Audience question: Is it worth doing a master’s degree in this field or would the Elevate course or Transform course offered be enough to become a data analyst or data scientist?

JP:  That’s a really good question. I don’t feel particularly well placed enough to say whether masters is worth doing as that’s a fairly personal type question.

The Data Analytics: Elevate course is designed for primarily non-data professionals trying to upskill and sort of become more familiar in dealing with data and to empower them as we mentioned and to give them a flavour of what a professional data analyst might do.

That’s why we have optional modules and things like using Python, which we encourage people to look at, but it’s not part of the assessment or anything like that. And we do the same thing by giving them a flavour of things like machine learning and to show them what they can do.

The Data Analytics Transform course is meant for exactly that, for people who are trying to get into the field of data analytics. I guess that’s the intent of it. It’s an intensive course and I think it’s designed to be around 15-20 hours a week, you know for 14 weeks part time. That is what it’s what it’s intended for, and it covers the gamut from learning how to use Python. Using statistics and ending up with being able to build regression models, as the capstone project, where you can pick a subject of your choice or topic of your choice build regression model out of it and be able to really complete that loop, I suppose, from problem definition stage to driving some insights and communicating it out to external stakeholders.

Audience question: If you don’t have a data analyst in the business it’s hard to get the data you want extracted, any tips on how to implement a data analyst into an organisation?

JP: I think it goes twofold: I think it’s important for data analysts to understand what the business objectives are, what their priorities are and how they work.

And conversely, it’s important for the organisation to understand what data can tell you, and what the data analysts can do for you and what they can’t do. So, understand the limitations. That’s because it’s important for data analysts at the problem definition stage to clearly understand the objectives as far as what the business objective is. So that they can translate that into actionable goals.

If that doesn’t happen necessarily well, what happens is that the business stakeholders would provide a brief and that necessarily doesn’t get translated into a data analyst’s understanding. They go off and do the work, get some results back and it turns out that it doesn’t necessarily answer the question that they were looking to answer; or it’s not particularly actionable.

And from the data analyst perspective, quite often the complaint I hear is, “well they asked me to do this thing that’s not actually possible so now, I have to manage their expectations about what is actually possible with data, as well as to try and answer this question some way”. So if they have an understanding of the limitations of data and analysis they can obviously integrate the data analyst or team better into the organisation and make better use of that resource.

Audience question: Do you have a structure when communicating the insights you’ve garnered from data to stakeholders?

JP: Yeah, that’s a good question. I think of data communication as similar to a part of rhetoric, so when you’re trying to convince someone or change their minds on something, the data should really be designed on backing that up.

A good place to start would be to think about what your goal was when you started the data analysis and kind of anchoring everything back to that.

In terms of this structure or the methodology it doesn’t have to be something complex, so you might see some beautiful data visualisation online or on Twitter—I see and I follow a lot of these people—so I see a lot of that, but then you kind of look at it and even for me who is quite used to them, I kind of go oh that’s really pretty but I have no idea what that says, but it’s really pretty.

So sometimes just a table with like three numbers might be better than a very complex beautiful data visualisation because it tells a clear message.

So that’s my way of saying that, whatever does the job is good. It always is what you want. And, I know I said less is more in terms of visualisations but it fits for the right audience, you can actually do more complex things.

For certain clients of mine, I’ve built them some dashboard apps that look at data that connects to their data pipeline. But what that allows the audience to do is to dig further into the data themselves. Those types of outputs tend to be a little bit more complex but because they have the expertise in their domain and because they are a little bit more data savvy, they’re able to dig into the data themselves. They won’t be performing the analysis and cleaning the data and so on and so forth, and building those models; they can look into it and answer their own questions as they go.

Audience question: I am a junior marketer and hoping to be able to make use of marketing data to optimise marketing decisions and campaigns, however, I don’t come from a technical background, so I wonder which course I should take in terms of elevate versus transform?

JP: I think, for me, the Elevate course would be preferable in your use case. It is designed around non-data professionals looking to upskill and really understand more about the world of data and data analytics at answering questions using data. That’s my two cents on that.

Of course, if you’re looking to challenge yourself, I wouldn’t discourage you from taking that Transform course, but my recommendation is to do the Elevate course.

In the Transform course we do cover a little bit more of things like statistics and hypothesis testing and A/B testing and how to analyse results of that more clearly. If you’re specifically interested in those areas, you might look into the Transform course. It is significantly more challenging because it is programming heavy.

Audience question: I have a background in tech sales but am quite curious in terms of how I can use data. I’m thinking of reaching out to our marketing team in house or other departments to see where I can add value. Any tips on what tools I can leverage to work on during my own time?

JP: A lot of things that people do is try and build their online portfolio in data science and analysis and there’s a lot of data out there that’s good – really, really high quality and publicly available. What people do is to source some of that data in whatever domain you’re interested in and build a portfolio of analysis and build your publicly available profile.

Now you can do a similar thing with your organisational data probably too depending on what is available to you, given your role and how accessible that data is. It is always always useful to solve a problem that the organisation has and quite often what they’ll have is things like there’s a lot of data in this field and they’ve collected it, for whatever reason and they probably haven’t had time to do anything with it, and so they can probably give you a background on what they were thinking when they collected this data. And why they haven’t actually done anything with it and it might be something as simple as they have collected all this data but it’s quite messy.

So it needs someone to go through that and clean it and that’s a really valuable process. There’s a running joke that people think data science is extremely glamorous and you’re building these AI bots and whatever. What you’re actually doing is just cleaning data and getting rid of typos like 90% of the time and that’s more true than people actually think.

So, you know I would say talk to people who have data in your organisation and talk to them about the background, as to why. Talk about what their needs are, and you can probably help them to address some of that and it’s a good way to build your and to network as well with the parts of the business.

Audience question: How do people in an organisation best work with specific data teams? There are often teams of specialists who are excellent at what they do, but how can others in the business really tap into that resource in an effective way?

JP: I think it’s about engagement and about data literacy. I think quite often what you see in organisations is that perhaps they’re a little bit too siloed in terms of what they do.

So really engaging with them, talking about what they do, and then that also helps them understand what you do and what your needs are as well.

I’ve spoken to people who are doing things like rolling out tools like Tableau in the organisation. They said getting to engage throughout the organisation, because they were rolling out these tools, helped them to speak to different parts of the organisation that they otherwise wouldn’t have to.

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