Data Analyst is one of the "hot hit" jobs now. Maybe someone interested in this position, especially those studying in the opposite field, has many concerns and worries when initially "turning in the direction".
We invite you to listen to exciting sharing from Thao Dang - Data Scientist of Underwriting team of Risk & Collection Division at Home Credit!
💭 Can you share the opportunity to bring you to the Data industry, Thao?
🍀 I am an alumnus of the University of Economics Ho Chi Minh City (UEH), majoring in International Business. My current job is also not related to my major at school. But fortunate enough that my chosen field of study is quite extensive, so I have some knowledge and skills that I can still apply to my current job - so if you study in a different field and still want to do Data, then it's okay, don't worry ;)
At first, I did not know much and did not realize my passion for Data Analysis. But when I went to school, I liked the subjects related to numbers, such as probability, statistics or finance, and research, and I also studied these subjects quite well. So, when I was looking for a new field, my friend recommended I learn about Data Analysis. The more I tinkered with it, the more I liked it and decided to follow this path. In addition, I am also passionate about technology and always want to apply technologies/tools to improve performance, so I feel more attached to the current choice.
After a while of exploring and researching, I'm now most interested in underwriting, which is also the area I'm working on. In the consumer finance industry, Underwriting is often understood as assessing the level of risk when a financial institution lends to a specific customer. People often think this appraisal will be done manually by credit experts, and it will take a lot of time to check and verify the customer's information before lending to the customer. But for fintech companies like Home, we certainly don't do it manually. With the automated due diligence process, the Underwriting department's job at Home will revolve around building credit scoring models, calculating customers' solvency to offer them a suitable loan, and controlling risks.
💭 So what difficulties did Thao face when "turning" into the Data Analysis industry?
The first difficulty I encountered when approaching Data Analysis was choosing which skills to learn. Because I don't have a technical background in math or computer science and I don't know anyone in the network who works in this industry to ask, I was bewildered about where to start; What technical skills to focus on (SQL, R, or Python). Fortunately, when learning about Data Analysis, Home also opened the Future Analyst Program 2018. I took advantage of the opportunity, applied, and was fortunate to have the chance to practice at Home. The internship period at Home helped me realize the knowledge and skills (domain knowledge and data analysis skills) I was lacking so that I could explore and learn on my own, as well as help me better understand this field.
💭 Can Thao suggest a route for beginners?
🍀 Drawing from personal experience, I would like to suggest a path that I think is suitable so that those interested in Data Analysis can start: https://imgur.com/UBgeUu6. These skills are divided into three stages equivalent to levels from beginner, intermediate and advanced:
1️⃣ Beginner
With the beginner stage, the analysis will be simple to medium and does not require much automation. At this stage, you should focus on learning how to turn the business requirements/challenges into an analysis; Turn data into specific insights that can be used to make business decisions.
Regarding technical skills, you should know simple SQL statements to query data and Excel to analyze and visualize your findings.
2️⃣ Intermediate
At this stage, you should improve your skills:
SQL: data querying and automation
Dashboard: helps reduce workload, effectively serving reporting and monitoring.
Data storytelling: if you want your findings to be put into practice, this is the skill you need to convince your boss/stakeholders and others.
3️⃣ Advanced
Regarding this stage, you should improve your work efficiency by automating everything you can :D. Depending on your goals and interests, you can invest a lot of time and effort in which skill group.
💭 When starting, you often don't know which source of knowledge to approach is suitable and appropriate; Can Thao share some valuable resources?
🍀 Anyone new to Data Analysis must also study Statistics :D. I don't have a favorite Statistics course to recommend, but there is one book that I quite like and think is adequate for beginners: Statistics for Business & Economics by David R. Anderson
The second thing to research is the skills and tools needed in the industry. For those of you who are just starting to learn about Data Analysis, I find Datacamp to be a good choice :D Their courses focus on interaction with learners, constantly alternating video lectures and exercises with remembering and understanding the lesson immediately. In addition, they also design their pathways according to the skills or career paths they want so that they can easily choose and sharpen these skills better :D
💭 Is equipping specialized knowledge and skills enough to thrive in this industry?
🍀 My answer is no. Firstly, we must continuously foster new knowledge by self-studying at home through Datacamp, Udemy, Coursera,… Besides, to be able to remember and improve our skills, we have to apply them constantly. I learn in real work. Luckily at Home I am always allowed to try new things so that I can improve my skills and improve my work performance.
In addition, I think that to become a good data analyst and bring specific values to the company; it is not technical skills but domain knowledge that is most important. Because no matter how sophisticated advanced tools you use, your analysis is worthless if you can't turn numbers into insights to improve your business. And these insights are usually only available when you have particular expertise. While working at Home (including the internship), I have learned a lot of valuable knowledge from my superiors and from the training/workshop sessions that the company organizes so that I can work better.
Hopefully, through Thao's exciting and valuable sharing, young people with a passion for Data Analysis will be more confident on the path to conquering their passion.
You can also join Webinar #1: Roadmap for beginners to conquer Data Analysis & Product Development, listen to seniors & experienced managers at Home Credit about their career paths, and map your own.
👉 Register now: https://bit.ly/HomeRacerEventSeries
—------
OWN YOUR RACE WITH THE HOME RACER PROGRAM 2022!
🏁 Apply now for a fulfilling race with Home Credit at: https://bit.ly/HomeRacerProgram2022
📅 Deadline: 𝟭𝟭:𝟱𝟵 𝗣𝗠 (𝗩𝗡𝗧), 𝟯𝟭/𝟬𝟴/𝟮𝟬𝟮𝟮 (𝗪𝗲𝗱)