This space is hosted by nestinstitute_
Space Summary
The Twitter space focused on key aspects of internships within a Development Agency, emphasizing proactive engagement, curiosity, and value addition. It advocated for interns to ask questions, be proactive in communication and self-promotion, while exploring different roles to discover passions. The space underscored the importance of relieving workload pressure on managers by adding value and promoting self-improvement, confidence, and seizing opportunities within the company. Recognition through effective communication and showcasing work was highlighted as vital for interns.
Questions
Q: What was the focus of the Twitter space discussion?
A: The discussion emphasized being proactive, curious, and valuable, especially as an intern.
Q: How was the value of asking questions highlighted?
A: Participants stressed the freedom to ask questions, promoting a continuous learning environment.
Q: Why is self-promotion within the company essential?
A: Self-promotion and effective communication are crucial for recognition and success within the company.
Q: What advice was given to interns regarding their roles?
A: Interns were advised to update others on their work, be proactive, and self-promote.
Q: What was suggested about exploring different roles?
A: Exploring various roles can help interns discover passions while maintaining financial stability.
Q: How can interns add value to managers and CEOs?
A: By being proactive, curious, and relieving workload pressure on senior management.
Q: What was the focus on personal growth and work ethics?
A: The discussion highlighted self-improvement, dedication, and seizing opportunities.
Q: How was confidence and self-advocacy addressed for interns?
A: Interns were encouraged to be confident, self-promote, and communicate effectively in the workplace.
Q: What did the discussion stress about continuous learning?
A: The space advocated for asking questions, exploring roles, and seizing opportunities for learning and growth.
Q: What was emphasized in terms of recognition within the company?
A: The importance of self-promotion, communication, and showcasing work for gaining recognition.
Highlights
Key Takeaways
- Emphasize being proactive and curious in the team or company.
- Advocate for asking questions freely to learn and contribute.
- Highlight the importance of self-promotion and effective communication.
- Encourage confidence
- self-promotion
- and showcasing work.
- Suggest exploring roles to find passion while ensuring financial stability.
- Acknowledge workload on managers
- urge interns to add value.
- Promote self-improvement
- dedication
- and seizing opportunities.
- Encourage interns to be proactive in updating others on contributions.
- Support trying different roles to gain experience and self-fulfillment.
- Remind interns to advocate for themselves in the company environment.
Behind the Mic
Good morning. How are you doing? Okay, I’m good. So basically during Pro’s Share, there’s about 50 people on the call. Just basically keep you on mute, don’t skip, and then after the speaker calls you out, then you can just unmute, then raise your question or raise your contribution as the case may be. So Dara, over to you. Thank you. Thank you, Didi. Good morning, everyone. Thank you for joining on a Saturday morning. I think the first topic we’re going to jump into is breaking into data science. So whether you’re transitioning from a different industry or you’re already in tech and looking to specialize, like, how do you do that? I guess the first question would naturally be, for anyone trying to break into data science is what exactly would a data scientist be doing? What would this role be entailing? Whether that is for Fintech or for any other industry? Does anyone want to jump right in? All right, David. Okay, thank you so much. Good morning, everyone. David here. Just to answer your question, Dara, breaking into data science can be somehow frustrating, in my experience, particularly for those that want to transition from an entirely different field. Now, the job role, it’s quite vast, right? Companies differ, job descriptions differ, and it depends on the industry. For fintech, I won’t really say it is an industry that drill down data science, right? It mostly revolves around analyzing customers. So what usually happens is if you are in the field, what exactly is the focus? If you’re in fintech, what are the products? What product are you the data scientist of? Is it for fraud detection? Is it for mostly on the sales campaign side? I think what usually happens is first you should understand the area that you want to focus on in data science. I think the first thing, like, Dara, we’re talking about those that are transitioning from a different field to data science, try as much as possible to know what part of data science you actually want to go into. Because we have product analytics, we have those on the machine learning side, we have those that are focused on natural language because that field is quite large. Once you are sure that this is exactly what you want, you want to go into machine learning, that’s what you should know, then I think it makes more sense. You focus on the field and start skill up towards that specifically. If it is product analytics, you start taking courses around that field. So I think the best advice from my own, because you know why it took me between 2018 and 2021, that’s when I got my first role as a data analyst, was because I was jack of all trades, trying my hand in everything NLP, trying my hand on deep learning. It was not going through, right? But when I pick a particular area, I was trying to know exactly where I want to focus, what I want to do as a career, it now made sense for me. Try as much as possible to know the area you’re interested in and focus on that. Go through LinkedIn, check job postings, what are recruiters asking for in their area? Because primarily, field when you join a field, also depend on the industry that is hiring for that particular role, it could be Fintech, it could be AgriTech, or it could be Edutech, now all the requirements differ based on field. So basically first know what you should do, finance companies, fraud detection, knowing all these requirements can help you focus. Then another thing you should do when trying to enter the course is understand the tools, understand what tech stacks are required in that field. For the Fintech, when we talk about Fintech basically these are mostly finance firms, you won’t have the data scientist to do when it comes to using a proper visualization tool because that’s mostly required. So my advice would be basically because depending on the field you are interested in, there could be tools that you need to know as a data scientist. Like the Fintech, for instance, you won’t have to use deep visualization tools like PowerBI or Tableau, you know, just to show reports and try to make decisions in case the company, you know, wants to do a product launch or to analyze the performance of a particular product. So that’s what mostly it is within the data scientists, data science field when it comes to Fintech or basically finance industry. What other tools? These are other tools, I think, basically apart from the visualization tools. This might be highlight until you get to the deep part, which is machine learning and AI, right? Which we’ll talk about much later on, I think mostly is the visualization aspect. Tableau is the online courses coding understanding SQL Python are always a plus. You know, tools like Rafael is asking, yes, there are other tools which we could talk about along the line. Another thing to add is understand the recruiting process. What do recruiters look for when they want to bring on board? Right. I think is basically in the job descriptions industry straddles first know what is particular to a particular industry and then try as much as possible to scale code and apply to those companies you want to work with. Thank you. Thank you so much, David. I think you touched on summarizing you need to know what you want to do in data science because data science is . So many different roles to data science. You could be very focused on product analytics where you’re looking at optimizing the conversion funnel of your product. You could be a fraud data scientist. Thank you so much for that, David. For me, I started out working as a technical recruiter, but the pandemic sort of pushed me online and yeah, I started getting gigs and all that online. Thank you. You?re welcome, no problem. We can go into that later on today as well. Next speaker is Rich auntie digitalization. I’m curious to hear from you If it’s something you sort of just fell into or was it your plan all along? Okay. I think Rich auntie raised her hand. So you can just go, Rich. Auntie. Yeah. thank you, Dara, for saying that so quick one. I remember when I was doing exactly that, like, trying so many things, you know, trying as much as I could. So I studied public administration in university, but I already knew that from the onset that I would never, you know, be a public administrator. Right. I was trying to find, you know, other means. I think I started out from mass communication. I was doing oAp Oau’s great FM at the time. I tried that because I, you know, I enjoyed talking to people and all that. And then I was forced into accounting. So I did Icann. Oh, my God. And then I tried finance, management consulting, and HR. What did I not try? Oh, my God. I also tried development. I did so many things because I was trying to find where I actually fit in. I did so many things, so much that somebody looked at me and said, oh, you’re such a career prostitute. I think that was what she said at the time. I remember at that time when she said, I made me just sit down and think about my life. Like, okay, what am I actually doing? But just like what Darren said, especially if you’re an undergraduate or your first graduate, like, just take your time to find what you enjoy. All right? I believe that life is too short to just sit down behind the desk and. And just do whatever you’re doing, just for the money. Money is important, right? And sometimes it might even require that you take on a role that would pay your bills while you’re finding what you really want to do, that’s okay, too. But I think it makes sense to try as many things as you can and always as intense. Like wherever, whatever company you work at. I think I would say always put yourself out there, even in your company. Let people know you know what you’re doing. If you buy as much, just update a document, let them know you did that. Don’t keep quiet, because most of the time, you find out that the people that you know that get the most accolades are not usually the people that work the most. It’s the people that can sell themselves the most. The people that can talk about, you know, what they do the most. They are the ones that get the most accolades. So you may be tempted to be shy or to be reserved because you’re an intern, to not be confident. But remember that the whole point of an internship is, you know, what I think of or what I tell people is, do you want to flip the coin? Like, let what you do count, right? Let every opportunity be count and add value, you know. And so when people think about the internship, they think, oh, well, I was just an intern. But there are tons and tons of people that started as interns that are, you know, running businesses today, right? So just see it as an opportunity to truly learn. Put yourself out there. Like, put yourself out there. Even on this same twitter, we see people who, people know that their work is not so great, but they know how to, you know, sell themselves and shout about, you know, their work and what they do. And because of that, they get exposed to opportunities that people who are better than them do not have access to. Right? So sell yourself everything they’ve said and telling yourself, put yourself out there. Let people know that you do data science. Right? You’re a data scientist. Reach out to people, tell your friends, people in your circle, do they even know that you have started taking courses? Do they know that, you know, you are looking into, you know, the data science field and all of that. Just always put yourself out there. Talk about what you do on LinkedIn. Let people know so that when opportunities come, you’re the first person they think about. So, yeah. Oh, thank you so much, guys. Thank you so much. Doctor James, I believe your question has been answered, right? Yes, yes. Thank you so much. Yeah, you’re welcome. Okay. Thank you so much, everybody. Thank you. Really, really good. David, do you want to say something? I just wish everybody the best and it’s good to have this kind of platforms and conversations on daily, maybe engage more people, because I think people try to learn these things or they don’t know, but they don’t have you providing this information for them and maybe the content can be repurposed in a different format and share definite community or something. Yes. Thank you. Thank you so much, guys. I, I really appreciate your time, your efforts, your experiences and your expertise. Thank you so much to everyone that joined in. And I hope we get these words for just like David said, much more people, the younger folks actually, and even the people in the game already going to be joining the question of this conversation now. Thank you so much for joining and have a great evening, everyone. Bye. Bye. Thank you. Bye, everyone. Bye.
