International economic and financial hubs like Kuala Lumpur have banks, investment companies and industrial companies that produce lots of data. Even small businesses and startups these days are producing lots of data from their businesses and everyday processes.
The good news is, when there is data, there is always a need for data scientists. Do a quick search on job marketplaces such as JobStreet or Recruit.net and you’ll find lots of companies who needs data scientists and analysts.
And if Malaysia isn’t enough for you, you’ll be happy to find that there are plenty of data scientist jobs in Singapore as well. A search on Indeed.Com.Sg
Ask yourself these questions if being a data scientist is for you
A good data scientist or analysts would need to possess some technical and business skill. The best data scientists are usually those who are all rounded and have few skillsets.
Here are the questions to ask yourself:
1 – Do you like to solve logical problems and work with numbers?
If you are someone who gets excited when it comes to solving logical puzzles and numbers, you’ll have the basic trait of a data scientist.
Enjoy solving logical puzzles like this?
Ask yourself if you have the habit of making guess estimates as part of your work-life. For example, you found yourself guessing the time it takes for you to get to work via different routes or perhaps you found it interesting to estimate the number of cars that passes by a petrol station in your neighborhood.
2 – Do you find yourself challenging assumptions made by people or businesses?
A huge trait of a data scientist is to back every proof and findings with data.
A company can claim that they make most of their sales through running billboard advertisements around the country. However, that alone is a mere assumption. A great data scientist would have stopped the person right there and ask ‘why’? Why did the company assume that most of their sales came from billboard advertisements? Are there data to back up this assumption?
With properly backed data, data scientists can help a business propel in many different ways. In the example above, data scientists can help the business to determine which advertising channel works better. In many other cases, a data scientist would primarily be able to mine, analyze and use data to give the business a competitive edge
3 – Do you like statistics and is willing to dig into programming?
Data science is a vast field and there are many fields you can choose to specialize in. Machine learning is a field that requires mathematics and statistics skills while a technical role in Big Data would require more programming skills.
However, with that said, you will still need a good foundation in both the branches. It could begin to sound like a long journey, but mastering both the skills and getting a working knowledge of both the two fields will put you on towards a great career potential.
If you find yourself constantly curious about statistics or always attending programming courses & workshops, this might be a great indicator that being a data scientist will be a good fit for you.
4 – Do you have good communication skills?
Good in communication and presenting your message? A data science career could be for you.
As a data scientist, you don’t work alone. In fact, the career requires you to collaborate with other people from different backgrounds and skill sets.
A good data scientist would be able to decipher and turn technical data into a visualization that is easily understood by non-technical people. The same way a doctor speaks to a plumber, both speaks English, but both of them would have difficulties understanding each other if they start using their own industry’s technical terms. Good communication skills would be essential for any type of data scientist or business analyst.
Companies often seek data scientists and business analysts who have strong communication skills and business acumen.
So if you have good communication skills, that means you’re only one step closer to being a good data scientist.
5 – Can you work hard?
A final question to ask yourself. Are you willing to work hard?
The reality is data science is hard. But with everything that is hard, it also means its worth doing.
For some people, the data science subject can seem rather hard as it requires interdisciplinary – a broad set of skills. Some people tend to struggle when doing something outside of a skill area that they are specialized in. In summary, the skills you’ll need involves programming skills, math & statistics and also communication skills.
I’m not going to lie to you that data science is easy. You will require some effort to learn data science and then work on real life data science experiments to be a great data scientist. What I can promise, though, is that data science is huge and its demand is only growing, not only internationally but in Malaysia itself.
What is the typical pay of a data scientist in Malaysia?
A senior data scientist can potentially make up to RM25,000 in Malaysia.
Looking at the market rate many Malaysian companies are offering, the typical pay of a junior data analysts range from RM5000 to RM8000 per month. The pay grade is higher when it comes to experienced data scientists. Then again, this comes down to the value you can bring towards a company that is willing to hire you.
If you’re new, work up your portfolio by helping your local startups with data mining and analysis. One best way to work your way up to be an experienced data scientist would be to work on real life data analysis.
Where do you go from here?
Ready to learn data science and start a data science career? The best way to start is to understand data and to understand the role of a data scientist. Without understanding the field, you won’t have a good idea about how the whole industry work.
You can also join the ‘Introduction to Big Data’ course for free or stay notified for classes that I constantly teach in. Otherwise, here are some brilliant TEDx videos that focus on big data and data science.
It will also help to get a data science certification and use it as a leverage to get accepted in job offers. What are some questions that I’ve missed out in this post? Let me know in the comment section below as I’d love to learn from you as well.
Hi, Dr Lau,
I am currently in my final year of medical school. Medicine interests me but mathematics has always been my passion. At the same time, I find the field of data science very intriguing and I learn that we still need data experts in the field of healthcare.
How shall I proceed from here into a career of medicine(not clinical work) and data science combined?
Hi Shar,
We need data experts in all fields, as I always say, all companies are going to become data companies eventually. Remember, the three key components in data science are programming, maths/stats, and domain knowledge.
For programming, you may pick up Python / R programming language as a start, and start looking at different packages. You may look at numpy and pandas, to begin with, and learn how to analyze time-series data, which is popular in cardiology. You may also learn how to use OpenCV to process X-ray and other biomedical images. Check out https://towardsdatascience.com/starting-off-in-bioinformatics-turning-dna-sequences-into-protein-sequences-c771dc20b89f if you are into BioInformatics and DNA sequencing.
You should be quite familiar with maths and stats, especially in analyzing clinical trial data and different kind of sampling methods, experimental design, and diagnostic statistics. You may try to re-implement them yourself using Python and R to sharpen your skills. Lastly, for domain knowledge, you may go to Kaggle and look for medical datasets that pose interesting questions. Ultimately you want to know why things are done currently, and ask questions that challenge the status quo. This way, you can use data science to answer questions and improve the efficiency of the existing workflows and processes.
I look forward to seeing your achievements!
Hi Dr Lau,
Thanks for writing such a detailed article on becoming Data Scientist in Malaysia!
Here’s a little background of me:
1) I have a degree in Analytical Economics and the algorithm and modeling in Econometrics are somewhat a little bit related to the one in Machine Learning.
2) I have 1.5 years of working experience as Digital Marketing (title is changing to Data Analyst & DM soon in the upcoming month). During this period, my job scope is Digital Marketing but also doing some data analyst role for my company using SQL, and some data visualization tool (I had a passion for this so I requested to do it and yeap, I have been a hybrid of both roles in my company all these while).
3) I soon realized I should further my passion for Data Science so I took Jose Portilla’s courses on Udemy. (1st one is Intro to Python, 2nd one is Python for Data Science ). I have plans to take more courses such as NLP, Tensor flow, Spark etc
I have a question that how/where can I learn the foundation of Computer Science which is essential on becoming a Data Scientist (or I shouldn’t?)?
Also, I would like to shift my career to Singapore (I’m working in KL now but I’m a Johorean), do you have any advice on that?
Thanks in advance for your advice!
Hi Steve,
You are welcome, and I am excited to see that you enjoy reading this blog 🙂
Jose Portilla’s courses are excellent. But I don’t think you need to jump into deep learning frameworks like TF and Spark just yet, especially when TF is still constantly undergoing some updates. Instead, you can go broad on techniques like NLP, OpenCV, etc., to understand what data science can do in each vertical, then only go into deep learning.
Yes, picking up computer science is always suitable for anyone who wants to break into a tech career. Many subjects like data structures, algorithms, cryptography theory, etc. will give you solid foundations that get you to go much further than the others. From there, you pick some electives that strengthen your database skills such as database design, information retrieval, data mining, etc.
I am Johorean too, so I totally understand why you plan to have a career in Singapore. In terms of data science, there are plenty of jobs in fintech and startups at the moment in Singapore, so you may have a look at it and make sure they are eligible to apply a visa for you. There are quite several companies undergo digital transformation, and I think that’s a good avenue to explore as well.
Thanks for reading, and here’s to your future success!
Dear Dr. Lau,
Thank you for your effort in replying to every single comment that was posted here. Those are all useful information. I would like to take this opportunity to seek your advice on my personal situation.
I was the kind of kid who counts traffic lights passing by when sitting in cars, calculate beans (with eyes) when mother was picking them, calculate pieces of cookies (with eyes) when mother was cutting them. Naturally growing up my math was very good and math result was just happen to be excellent. Among all i enjoy algebra and all the quadratic and graph most while finding median and mean on statistic is rather boring.
Then, I was out of my brain and took law in university, and became a Malaysian lawyer. I am now a junior corporate lawyer advising and solving companies matters. My research is very good and I understand theory very well. Most of my clients are IT start up with new business model which many lawyer cannot understood it or combine them with existing legal principle/concept.
I have a little dream for myself that I wish to encounter NUMBERS everyday in my life. One years ago I then become part time additional math tutor in my free time which i found really fun solving those questions.
I wish to gradually transform from a lawyer (a person manipulating words) to a person dealing more with numbers. However, my math education was until A-level only and I do not want to teach additional math full time.
Looking from my talent since young combined with my life experience, could you shed some light for me on which area of data/computer/IT could suit me?
Because of my little dream, I have look into and briefly read up on accounting, finance, stock, actuarial science, coding, whatever it is so long there is numbers…and of course i am here because i am looking into big data and data scientist.
I am a lone person walking in the jungle in Malaysia finding path to my natural talent. I sincerely wish to receive some insightful advice from Dr.
Looking forward to your reply and thank you in advance.
Dear Yunri,
Oh man, you are just that type of classmates that I envy during my high school. I didn’t understand Maths until PhD time. To be honest, I thought I would never have to use them in my life.
I always believe that everyone is gifted with a unique talent, and once they pick up their superpower tool, and using it to save the world, i.e. solve problems, they will become a superhero.
In our case, the superpower tool is data science. In my case, I am good with logic, programming, and coding since young, and then I pick up data science so that I can quickly use some newer methods that are more efficient to solve old problems in the industry.
For your case, I have a couple of ideas. You shouldn’t give up your legal profession. You should pick up some skills that complement your current jobs using numbers. Here are the exact steps:
1. find an interesting problem in your field that is done boringly. Think about how to solve it with numbers. These problems shouldn’t be too hard, as most of the time it is the processes that make it dry and boring.
2. Pick up some basic coding using Python or R. These languages are free and open source, with many online learning resources.
3. Learn how to implement some of the algorithms using the language of your choice. The algorithms can be classification, regression, or clustering. It depends on the problem you are trying to solve. Do let me know if you don’t know how to pick, and I will give you some pointers.
4. Apply the algorithm to the problem stated in 1), evaluate the performance, iterate and improve it.
5. Repeat step 2 with more depth and efficiency.
Data science is no magic, it’s all hard work and effort, but it is rewarding. Since you love NUMBERS, I am sure that you will enjoy the process and find many exciting bits that keep you going.
Let me know what you think? You have an exciting career path ahead, look forward to hearing from you soon!
HI DR Lau,
I am a MBA holder and currently working in IT industry (cloud computing), and I have worked for the 2 of the 3 market leaders (as per Magic Quadrant). I would like to enroll Data Science program, preferably Part time, to further advance my career. Which institution and steps you would advise?
Hi Bobby,
You are in a good position. You have been through the cycle and understand how the cloud works, and I assume you are familiar with the requirements and points of corporates. You can also explain clearly to them how data science can help to transition into the next generation of the data-driven company.
I teach the Data Science 360 certification program at LEAD (https://www.thelead.io/data-science-360). As the name tells you, DS360 covers the 360 aspects of data science. We cover data science project lifecycle using OSEMN framework, structured and unstructured data, to machine learning. You will learn how to gather data, clean data, explore data, perform predictive analytics and visualization.
Do take a look and let me know if you have any further questions. Feel free to email me directly at [email protected] and I can share more on career and industry specific insights with you too.
Hi Dr. Lau,
I know it has been said so many times before, but let me say it again, your patience and considerations while replying each post is really admirable. Kudos!!
I am an academician and researcher from Electrical engineering background. I have been interested with DS for quite some time now and wondering what could be the smoothest path available for me to have a career transition. I have done some MOOC along the years but would really prefer a intensive bootcamp that can fast track a DS career with real life project experience.
I almost enrolled to Insight fellowship in USA but later didnt follow through as I want to be based here in Malaysia. So whats my option here in terms of quality bootcamp? I really dont want to spend time on intensive university courses.
As a PhD holder I can relate to your experience of critical thinking and problem solving as part of PhD program.
Can you suggest me how to approach for a rapid transition to DS?
Thank you.
M. T. Ferdaous. PhD.
Hi Dr Lau, I’m just graduated and what course should I take in university to be a data scientist?And what university are you suggested?
Hi Lee,
You can start with computer science or a mathematics/stats degree to begin your data science degree. They build your fundamental knowledge. Even in a newly offered data science degree, they focus on the fundamentals and include analytics skills and soft skills like presentation and visualization. Don’t forget to pick up business subjects like Economics or Finance. They help you to think differently in any business domain.
It is pretty hard to name a university that offers data science programs. As they all have their strength and weaknesses. But you can use the following indicators to decide which university that suits you:
Industry collaboration. This provides you the access to internships, seminars, site visits, etc. (e.g. http://www.apu.edu.my/our-courses/undergraduate-studies/computing-technology-games-development/collaborative-industrial). Strong industry ties will give you access to their network, better internship offer, and a potential full-time job.
Research output: If you plan to pursue a Master or Ph.D. degree later, you may consider Universities who are active in academic research. Usually, they are strong in Maths and Computer Science, publish in journals related to machine learning, A.I., data mining, and cryptography.
Teaching team and Facility: Visit the university campuses after you have shortlisted them. Best is to visit during Open Day. You get to speak with the counselors, lecturers and understand their facilities. For example library, computer labs and HPC (High-Performance Computing center). Or is there any infrastructure lab like Cloud/Data Analytics lab or COE (Centre of Excellence)
Do not pick a university based on their rankings only. Their ranking varies as some technology-focused universities rank poorly against other multi-disciplinary universities. Also, compare against different rankings, e.g. Times and QS to get a clearer picture.
Hope this gives you a clearer idea on how to select a university for your data science degree, all the best and let me know if there is anything I can help you further!
Dear Dr. Lau,
I gone through your comments and found that you are replying almost to every post and it encourages me to ask for career advice and it may help others also. I am thinking that I have lose my career path and want to find it back.
I started my career in 2005 as software engineer and going trough my career I worked on Java, python, php and other open source technologies and ended in Sr.Software Engineer and Team Lead 8 years back. Then I shifted to Technical Analysis and Production Support Analyst by moving to Productions Support Services and Operations as per job, market and salary demand.
currently I am holding a management position as SDM, but I ever look at myself and I feel insecure of being in management position. I started thinking that being non-technical the doors of opportunities are getting close gradually. So I decided to go for ITIL v3, PMP, Agile/Scrum Master certifications. The closer I reached to these I found it opposite. Because I still not satisfied as these all posses non-technical skills.
When I look back and decide to go to development again to be remain in technical field it sounds that I have to go for development again and need to revise all the open source frameworks like Spring/hibernate/Struts/etc from scratch but do not get interest as I have done so intensive work in all web technologies already and it rounds about mostly CRUD, CMS , transaction and service based actions but I am not getting the interest back. And also Java and open source frame work are much changed now from Java 5 to Java 9. I am just doing job and I do not know whether this career path of management is long lasting or not bcz how many management positions are there in market 100 technical position and <= 10 management positions.
1 week ago I finally decided to start Data Analysis as it is interested and I go for Udacity Data Analyst Nano degree program. Today I was doing some survey and trend analysis on data analyst career path and salary positions and came through your post now. Now I found that data analyst salaries are not as high as currently I am taking. Still my interest is affirm in Data Analyst Nano degree program. I am looking a career advice from you and also by doing the data analyst nano degree program and changing job to data analyst will it be a U-turn from my current career ?
Thank you Dr. Lau,
-Abdul
Hi Abdul,
Sorry for late reply and yes I reply to every post. I missed yours because the WP plugins hide it accidentally. Let’s get to your questions.
There are three key components in data science: programming skills, maths and stats, and domain knowledge. Programming should not be an issue to you since you already know Python. And you also know how to build enterprise programs using Java. I agree that Java has changed a lot, but that’s more on the features sides. The underlying syntax, logic, data structures, and algorithms didn’t change much. So let’s tick programming off.
So what you need to do is to strengthen your data analytics skill. With your experience in ITIL, PMP, and senior management, you understand the business needs, requirements, and pain points better than most data scientists who come from a pure CS or MS background. I don’t think you should U-Turn and start as an analyst afresh. Instead, you should continue and complete the data analyst nano program, then go straight to the data scientist path.
You should completed the nanodegree by now right? How did it go ? Do share with me your current plans and I look forward to hearing from you!
Nice article ! I just graduate from this field and currently looking for a fresh graduate jobs. I think the downside of this field is an extremely difficult to find a company that willing to hire fresh graduate. Most of the positions I have looked often looking for 2-3 years of experienced at least. That is something to consider at.
*If you have any contacts on someone is looking for fresh graduate, pls email me*
[email protected]
Hi Dr Lau,
I have a background in electronics engineering but have now been working with a lot of data analytics for my work in the past year. Mostly large amounts of data from multiple tools and trying to correlate it. I’ve reached to the limits of excel, and have since picked up online courses for Python and also statistics. However, been looking at certifications and masters programmes to improve my value and knowledge. Any recommendations?
Have been looking at the following universities for part-time courses:
Monash – Master of Data Science
UTM – Master Of Science (Business Intelligence and Analytics) – Most Interested
UM – Master of Data Science (Coursework)
APU – MSC IN DATA SCIENCE AND BUSINESS ANALYTICS
USM – Master of Science (Data Science and Analytics)
Is there any other recommended university courses to check out? Or perhaps a credible certification programme because most universities are at 3 years to finish the course if part-time. Thank you.
Hi Dr. Lau,
I have a Degree in Business Administration and have 7 years of working experience.
I know myself well that I’m very interested and have the abilities to involve in data analysis and probably data science industry. As I go through the questions you mention, I am more confident that I want to go into data science industry.
I’m good at simplifying and making conclusions from large amount of data and information. I am very logical and always doubt anf will challenge others’ qualitative assumptions which are not based on quantitative data.
But the problem is, I think I do not have the qualifications for venturing into the data industry as my career. What can I do or start with from now onwards?
Thank you in advance for your reply.
Hi Boon Aik,
Glad to hear that you are clear about the road ahead. With your background, that makes things easier. At first glance, you might not seem to have the necessary technical qualifications. But that shouldn’t stop you from entering data science for the following reasons:
These should be some good starting points for you. Remember, the key criteria to a successful data science projects are: start small, think big, scale fast.
Well done, and look forward to hearing more from you!
Hi Dr ,
I have 9 years + exp in engineering and dealing with statistic everyday . My daily routine is to troubleshooting and solve problems based on statistics and etc
While my network and data getting bigger and I started deal with python because excel no longer a good option for me perform daily task.
And now my field is saturated and I am in the middle of junction either should I start and learn new skills such as BIG data to secure my future or cont work in same field for rest of my life~
I checked few master course offered in Malaysia , but it take 3-4 years . Your advices are important for me ~
Hi Hoong Yong,
I have many students who are in a similar position like you. Excel is good for its GUI, but it does not support intensive scripting. Hence, it is hard to reiterate your processor to pass it on to someone else to continue.
Any new skill we learnt is like a tool, it depends on how we apply and use it right to solve a particular problem. I always suggest my students pick up new skills. But big data skill is not necessary for you right now. Big data is more of an infrastructure problem that tackles really large data with different structures.
You should pick up some data engineering, data ingestion, or what we call the ETL (Extract, Transform and Load) skills. These skills will help you to learn how to gather, cleanse, and process data at scale. Once you know how to do ETL properly, you can then pick a field that you like. From there, you can explore uncharted territories that are related and you are also passionate.
Hi Dr Lau,
I am graduating in applied physics and I am very interested to be a data scientist. So, how I should start my career in that field n how I can be employed by company using my degree?
Hi Izyan,
Glad to hear from you. I love seeing people from different disciplines adopting data science. As an applied physicist, you should have the experience that is closer to Data Science than you realize. I would suggest the following approach:
Find an interesting question that worths answer in your field. It can be triangulation, calculating azimuth of a celestial object, to simulate colliding protons to recreate mini big bangs.
Pick a tool that suits you. Start from Matlab if you have learned before, or look into popular options like Python or R.
Learn how to gather data and clean the data at scale (using scripts and not GUI tools like Excel)
Learn how to analyze data (don’t dive into specific techniques, go broad and learn the strengths and weaknesses of each method. The method that fails at one problem might perform well at another one.
Read on machine learning and visualisation. Learn how to conduct reproducible research and communicate your findings.
Don’t worry too much about other buzzwords like big data or AI. Focus on the fundamentals and how to apply fundamentals techniques to modern problems. Learn about the job, the field, the industry pain points extensively. This will be a good start to begin your career. Learn how to bring values by solving these problems, it will make you qualified and get hired by companies.
Hi, i am from mainframe field working around 7 years already in that but now moving to data science field .. i have good knowledge on python, sql, tableau and getting hands on ML
could i try for fresher in Data science field . i am trying for openings in malaysia and singapore. what could be the salary package i could expect on.
Sorry for the delayed reply, as we were migrating from another domain and also upgrading our servers.
Since you have mastered the fundamentals and BI tools, you may go straight for a data analyst job, or try an associate data scientist position. This will get yourself familiarise with these positions, understand the workflow and job requirements. On average, my Malaysian students make at least a good MYR 4k for an entry-level data science position. Those who work as data scientists in telcos, airlines, and insurance, make around MYR 5k – 7k. My other Singaporean students from the finance and marketing background make about SGD 5 – 6k on average. In Melbourne, a general data scientist makes around AUD 70 – 80k per annum, and it goes up to 100 – 150k if they can combo their data analytics skills with another vertical skill such as marketing, social study, customer service, etc.
Hi Dr Lau
Thank you for the great insights! My name is Muhammad Nazrul and I am currently working as a data analyst but looking for opportunities to go deeper into Data Science. I have a degree and masters in Mechanical Engineering but have been an analyst in EC and Marketing companies in most of my 5 years career.
I work mostly using SQL, SAS and MS Excel at work, but I am taking courses on Data Science(currently machine learning) using Python, to expand my knowledge in the field.
My questions are;
1. Does most companies in Malaysia look at the degrees when hiring? Mine is not Computer Science or anything related to Data Science so should I consider getting a certification first?(FYI, I am currently not working in Malaysia, planning to go back but have no idea of the industry’s situation over there)
2. What industries are starting to use Data Science more in Malaysia? I understand that Finance and Aviation industries have been using heavy analytics for their business, but what about the others?
3. Do you have any advice for someone who is trying to get a Data Analyst/Data Scientist job in Malaysia?
I really hope to hear back from you soon.
Thank you in advance.
Regards,
Muhammad Nazrul
Hi Nazrul,
Thanks for your email. Sorry for the late reply, as we were migrating the site. I am always excited to see people who are coming back to our home country. Back to your questions
1. Most of the companies that hire data scientists don’t really look at your degree. In most cases, having a degree is just a requirement, and not necessary a degree in data science. Data science degree is a relatively newer degree. Universities offer data science degree to meet the market demand. Most of these degrees evolve from data mining related degrees. Some companies do specifically look for degree holders in particular field like statistic, mathematics and data mining (e.g. https://www.jobstreet.com.my/en/job/data-scientist-3644169?fr=21). This happens when they have specific projects. They will only reveal further during the interview, or after you have joined the company.
2. Almost all industries are trying (hard) to apply data science and improve their business. You are right, finance and aviation have been using analytics for a long while. We have clients from fast food industry using data analytics to streamline their supply chain operation. We also have consumer goods manufacturer using analytics to forecast their sales for the factories to prevent overstocking raw material. Hospitals use data to optimise the bed occupancy and measuring the average length of stay to detect unexpected disease outbreak. Telcos also start to analyse customer calls duration and email frequency to improve customer satisfaction.
3. Try to secure as many interviews as possible. Don’t accept any offer until you have interviewed with at least 5 companies. Ask your interviewers as many questions you can to find out their actual business questions. Most of the time they might be just looking for dashboard designer or data engineer, rather than a data scientist. I have seen many candidates with wide range of background like yourself ended up doing data engineer role, focusing on ETL and writing SQL scripts rather than analyzing data. So make sure you and your potential supervisors are both clear about your role.
Your skill sets are highly relevant to data science. You shouldn’t have too much of a problem making the transition.
Once again, I look forward to seeing more talented folks like yourself to be back in Malaysia. Let’s advance the country together. I’m more than happy to provide you with additional information related to the industry developments in Malaysia, chat soon!
Dear Dr Cher Han,
I have a degree in health sciences and have been working for 5 years. I am looking for a career change and the prospect of working as a Data Analyst/Scientist have attracted me.
As someone with NO formal education/ experience in computing and data, how do you reckon I start?
Looking forward hearing from you!~
Hi Nadz,
Yes, you don’t need to have a computer science (CS) background of becoming a data scientist. Now, here are the steps to get you started.
1. Have a broad overview of data science.
2. Look for a niche that interests you.
3. Apply it to an interesting problem.
For step 1, read broadly about data science, don’t worry so much about theories. Focus on the techniques and how they are applied. Understand the difference between supervised and unsupervised learning model. How to use these techniques to solve problems. Learn about the differences between regressions, classifications and clustering. This should be enough to get started.
Along the way, you will find something that interests you. Given your background, you may look into subjects like genomics analysis, early diabetes detection, or dengue detection. Then google “data science +“, and you will find resources that talk about how to apply data science in that particular niche. Then you will find some actual articles on how we can use the techniques above to approach a problem.
The step 3 is where we get technical and our hands dirty. If you want to try out some programming, you may download free tools like Anaconda (i.e. Python) and R. Give it a try. If programming is not your thing, switch over to graphical tools like PowerBI, Tableau, Rapidminer, or even Excel. Start analyzing by applying these techniques you have learnt on a problem that interests you. Get yourself very familiar with that particular niche, and repeat Step 1.
You should have a good head start by now. Do let me know how you go. So that I can show you more specific resources. Keep up with the good work and welcome to data science!
p/s: Read the story of a recent graduate from our data science bootcamp. He is from finance background and has degree in IT or data science. Now he is into data science and just completed a project to analyze sentiment from social media. https://www.thelead.io/data-science-interview-with-chia-hooi/
Hi Dr Lau,
I graduated from Bachelor of Information Systems (Hons) Business Information Systems and have 6 years experience of Business Intelligence background. Is it suitable for me to taking some extra courses for Data Scientist?
Thanks
Hi Joanne,
Certainly. Your background in IS is a great help to understand business acumen that identifies key business questions. In all my consulting projects, I require business owners/decision makers to come out with 5 business questions. It is hard for them to appreciate what they can gain from the data, without these questions.
Do you mind sharing a bit more on your BI background? Do you work with the analysts, or more into dashboard engineering and information display? Either way, data science is going to help you. For example, you can be more efficient at data cleansing and use scripts to automate repetitive tasks. You can also build predictive models, or using time series analysis to forecast sales figures.
Hi Dr Lau,
I am currently doing my Masters in Civil Engineering. However, I’ve decided I would like to transition to a job in Data Science field once I finish my Masters. My only programming experience is using Matlab. However, I have co-authored two papers which utilizes Artificial Neural Network and Particle Swarm Optimization, which I wrote from scratch. My questions are:
[1] Would writing more papers in the veins of the aforementioned papers help in getting me an interview?
[2] Which one of R, Python and SAS is the most relevant in the Malaysian industry? I’m planning to learn all three, however I think focusing on one as a main language at first would be more beneficial.
[3] What are some other concrete things I can do (projects, etc) which I could pursue in the meantime, that I can add to my resume to land an interview?
Thank you for reading this long post. Hopefully it’s not to much of a bother. Looking forward to your thoughts!
Hi MR,
Knowing Matlab gives you a good head start when transiting to a data science career. You have implemented ANN and PSO from scratch, you should have no problem understanding machine learning and data science theories. Please find my feedback on your questions below.
[1] Having more papers are not going to help much unless you are in the academic or getting a research position in the industry.
[2] I don’t recommend learning all three languages together at the beginning. On a higher level, they are all data science tools. You need to understand the differences among these tools base on your requirements. Both R and Python are available for free, and SAS requires a paid license. SAS comes with a good UI but using R and Python gives you more control. Python suits individuals from a CS background, where R is more intuitive for academics or statistician. You might find R easier to learn at the beginning due to your Matlab skills. But Python does more beyond data science (e.g. web server, scripting, scraping etc.)
[3] Any project is fine. If your goal is to land an interview, then build something that people can relate and practical. For instance, sales forecasting, dengue analysis or product recommendation. Something that you can present and elaborate during the interview.
I always look forward to more people embarking on the data science journey, and the pleasure is all mine 🙂
Hi Dr. Lau,
Where should I start if I would like to get into data science?
I major in accountancy and have no background in programming and statistics.
I recently joined a brief introduction to data science and very interested in picking up the skills to become one. Please let me know your thoughts.
Hi Izati,
That’s fine if you don’t have any programming knowledge to begin data science. Data science is not only about programming. It’s about picking the right tools to solve the problem.
First, you need to pick an interesting problem from a field that you are familiar. For instance, how to help a retail shop to increase their sales? How to reduce operational costs for an airline company? How to recommend products for an e-commerce store? Once you have identified a problem, start collecting data. Don’t worry about the size of the data. It should be big enough (say 4 weeks to 3 months) for you to visualise trends and gain insights.
After you have collected the data, you can then run some basic analytics (what we call EDA – Exploratory Data Analysis) using the tools you are familiar (e.g. Microsoft Excel). There are many things you can do. Clean data using built-in functions, draw different types of charts on the same data to understand the characteristics of each chart, look for trends, etc. Then you learn what has been achieved, and what are the limitations. You will encounter dirty data (e.g. outliers, missing data), and you will need to figure how to handle them.
Once you are happy with the result, start to define your audience and how to present your findings. What is the type of charts that make sense to senior management, company stakeholders, or end users? Then think about what is the gap? Do you need to collect more data? Run more in-depth analysis? Or you need more advanced techniques like labelling items, group them according to characteristics, etc.
By now, I have covered most tasks that data scientists spend most of the time. All without using any programming. You collected data, processed the data, cleaned the data, and presenting the results. Once your data grows, then you only will need programming skills to process the data in a scalable way. Programs will also save you time from repeating the same process.
Hi there.
Just curious about is becoming a data scientist requires any professional papers?
Hi Edward,
At the moment there isn’t any prescribed professional papers in order to get a data scientist job. There are some certification programs out there but more targeted towards people who are aiming for a specialization. Is there any particular area you are keen to enter? Or you would like to pick up general data science skill as a start?
Hi Dr. Lau,
First up, thanks for having this site. I refer to your reply dated Sept.23 above on the “bootcamp”. I too am considering whether there are Data Science certifications in Malaysia today as an option that one can take instead of committing to a degree program that requires more commitment.
I came across this Harvard Business School Executive Program (http://www.thecads.org/leadership-skills/) that claims potential job role as data scientist upon completing the program.
What are your thoughts on this program? Would this be worth exploring and investing in?
Are there any other similar data science certification programs that you are aware of that I can consider besides the one mentioned above?
Thank you.
Hi Shireen,
The HBS program seems more suitable for people who want to be data scientist but more towards a management role. So it depends on whether you are looking to develop the soft skill side or technical skill? Bootcamp programs are more hands-on and focus on technical skills. Do let me know your requirements and your area of interest then I can give more precise suggestions.
Hi, Im Hoong and currently doing Msc in Data Science. However, i feel im learn something but not meet the industry standard. Could you provide some advise about what is the necessary knowledge must acquired before enter into data science field.
Hi Hoong,
Which university are you attending? Most universities equip students with knowledge that spans across industries. You need to understand the industry first before you can apply the knowledge.
You need to know how to gather data to start. Understanding different file formats, and web format standards (e.g. XML, JSON). Next, you need to learn data cleansing techniques (e.g. handle outliers, missing values). From there you then extract features from different types of data. E.g. structured data such as transactions, log file, or unstructured data like text, signals, videos and images.
Once you have extracted the features and stored them into usable representation, you can perform exploratory data analysis (EDA). This type of prescriptive analytics allows us to see if there is any information we can derive by inspecting the data. For instance, find the best selling item in an e-commerce store, what is the current trend of your website visitors, or what are the hot topics people are discussing in a Facebook group.
From there, we work on the problem we have formulated earlier. It could be how do we attract more similar visitors to my website, how to reduce churn rate for a telco company, or what is the probability that a person will likely to quit their current job. You need to learn how to do modelling and the evaluation methods to validate your model.
Once you have defined the model, you can then apply it to a new dataset and track the performance from there. From there we begin predictive analytics. We utilise the prediction to present the findings using visualisation. Lastly, we communicate the results with stakeholders.
In a nutshell, don’t just focus on your technical knowledge. Develop your critical thinking, presentation and communication skills. They are as essential and also crucial requirements to become a good data scientist.
Hi Dr. Lau,
I’ve completed a BSc in Mathematics as well as a MSc in Operational Research. I’m particularly very interested in the simulation modelling aspect of this field. However, I’m finding it hard to get to through the right channels to get into this field. I’m interested in the operations analytics sector, i.e. reducing queue times of a hospital and optimizing operations. I appreciate your time and guidance!
Thanks very much!
Hi Varsha,
Good to hear that. We have used A.I. a lot lately in the OR analytics field, to effectively reduce cost and unnecessary wastage. You can pick up Python to get familiarize with scripting. Then you can download Anaconda and use Jupyter notebook to play with some datasets. You can use existing data from your Master study, or download from Kaggle. Start from ETL, data cleansing and how to do exploratory data analysis using the default tools, you should be able to extract some insights pretty quickly.
Next, for reducing queue times, you may look at some techniques that model patients in flow and staffing. From the raw data, you can estimate how many patients will visit on any given day. Start looking at the patient arrivals, and their average wait time to be attended. From there, try to reduce the number of peaks so that at any point of time, any patient that comes will only wait for a reasonable amount of time.
Let me know how you go, and then I can share more information with you about the other techniques, and also how to utilize unstructured data.
Hi Dr. Lau,
I am graduated from bachelor in accounting and wish to further studies in data science field but I do not have any research, IT and programming skill.
Do you have specific university that you can recommend to me? If possible any public university?
Thank you.
i am a bachelor art of degree in business administration holder. i am interest in this data analyst job, but my maths not strong, how to improve this and am i suitable to learn this ？
Hi Dr. Lau,
I’m interested to take data science program ( prefer kind of boot camp ) to explore more in data analytics. May I know if there are training providers/Univ. that offering such a program for public on part time mode for working professionals.
At the moment there aren’t too many bootcamp style training programs in Malaysia’s market. There are some universities like APU, UiTM that offer data science program. But degree programs require more commitment. You can also self-learn some skills from MOOC courses. That will give you a brief introduction and understanding about data science.
Hello Dr!
Nice article. My name is Ady. Even though my study background was not in IT but I’m a self taught web developer with experience in C# and Asp.Net framework. Recently I have learned python and I want to be a python data scientist.
The thing is, I am kinda lost. I am not sure where or which course to learn. I’ve found courses in Udemy and Edx for data science and machine learning, will they be any good?
Also I realized companies usually expect nice amount of experience for anyone who wish to apply the job. Can you elaborate further about getting experience in data science like you mentioned in the article? I don’t really see how to actually do that.
I can learn the courses, it’s just that I’m afraid if I do not know what to do after that.
Hi Dr. Lau,I am doing many research on Data science(DS) lately to figure out best way to start my career path in DS. As I’m marketing graduated student and do not have background in statistic. But over past 4 years of working experience in digital marketing specialized SEO and SEM I have good understanding of transform data into powerful insight by using advanced excel formula. I understand the big data in science is beyond than excel knowledge, where R,python,SOL many programming language come to picture, thus I plan to take course for those knowledge.
Reading from other post, I know statistic/math is the starter for everything in DS. Due to budget concern(plan to allocate more budget in DS course), is it possible for me to self study on statistic? As i’m concern on how deep statistic knowledge I have to understand, If it is only basic knowledge i have to know, I guess I can self study.
I found alot of courses for DS(e.g:certified in DS,DS in R and python,master in DS,etc), but I not sure which is the best course, and how should i choose? Also, between R,python,hadoop,sas,sql,tableau,etc.. which is the main languages I should pick first and how should I prioritize?
I have a goal of within 6 months have myself understand DS knowledge ready for me go for DS job interview. I believe it is possible if I put my hard work at the right path. I hope Dr.Lau could be give me advise on this =). Thank you!
Hi Jin,
I am glad to hear that you have set a timeline to achieve your goals. Lack of background in statistics is not a deal breaker. Your experience in digital marketing, in fact, gave you the advantage to understand the particular domain.
Basic maths and statistics are sufficient to kick start a data science career, and it is not hard to study on your own. You can learn some solid statistic topics online. Khan Academy does a fantastic job in explaining in explaining the concepts. You can start with topics like probability, linear regressions, ANOVA, etc., and how to apply these techniques and models to use cases in SEO and SEM.
Next, the key question. With so many languages, technologies, and software, which one should you learn first? I break it down to the following table to clear your mind.
1. R vs. Python – Both are popular programming language used by data scientists. I found R suits people who came from an academic research background, and Python suits people from a computer science background. I would recommend learning Python If you have no programming experience at all. Python is a beginner friendly language, and you get to learn programming language and data science processing at the same time. You can also use Python to write scripts to automate tasks. Later, you can use Django, a web framework to build modern websites.
2. SQL – SQL (Structured Query Language) is a data definition language (DDL) used by major relational database systems (RDBMS). We use SQL to perform CRUD (create, retrieve, update, delete) records in database engines like Microsoft SQL, MySQL, and Oracle.
3. Hadoop – A technology designed to store huge data files. Big companies like Google, Facebook, Amazon currently use Hadoop to store big data. Most businesses do not need storage at this scale. Thus you don’t have to worry too much about it at this stage.
4. SAS and Tableau – SAS is a software suite for Advanced Analytics, Business Intelligence, and Data Management. Tableau is a BI (Business Intelligence) tool to create reports that comprise visualizations. SAS has its own version of BI tools called Visual Analytics. You don’t have to rush into learning one it as it is vendor specific. You may decide later depending on the company’s choice.
I would recommend you to learn Python and database design first. Follow by how to normalize and store data efficiently. Next, you learn how to perform operations using SQL language. Start analyzing and modeling data using different techniques, this will give you a good head start in your data scientist journey.
Do not hesitate to contact me if you face any questions. Let me know once you are ready and I can help you further in beginning a data science career. Keep up with the good work!
Hi Dr.Lau, would you recommend a person who is interested in Big Data to take up a Bachelor in commerce majoring in business analytic under a business school or a more Information System based business analytic under an IT school ?
Hi PL,
Big data is often a concept used when the data is too big for current technologies and software to handle. We can look further at the term in engineering side and business side. On the engineering side, we need a robust architecture that can ingest data at high speed and understand different formats. On the business side, we want to combine data from multiple silos and extract valuable business insights.
On the other hand, A Bachelor of Commerce degree teaches you the subjects that help you to understand how the financial world works. This includes corporate accounting, taxation, auditing and couple of business and finance related subjects. For an IS degree, you will learn how to apply IT systems to achieve specific business goals. From my experience, I’d say an Information System course from IT school will benefit you more. This way you learn to look at the business as a whole, and learn how big data can play an essential role in business.
Dr. Lau, thank you for your reply, does data science and analytics mean almost the same thing ? I see a lots universities offering Bachelor/Masters degree in analytics but not so in data science. How advance is data science field in Malaysia comparing to US, Uk and Australia ?
Hi, i am currently taking Bachelor in Computer Science and majoring in Software Engineering. It’s been a while since i heard about this Data Science and i am interested of pursuing my career into this field after i graduated. So i would like to ask you what should my next step be after i graduated? should i work first and gain experiences or should i continue my studies and takes Master in Data Science? and also should i take online courses regarding the statistic subject and also other machine learning course ?
Hi Aida,
I think you have a good head start there with CS and engineering skills. So don’t hurry into a decision yet, spend some time looking at different projects instead. Try to learn how people apply data science to solve problems in various industries. Aim to broaden your understandings. You may start by learning Exploratory Data Analysis (EDA) with statistical methods, then move into machine learning, understand what is supervised and unsupervised learnings, and take it from there.
Hi,
I am currently taking a Bsc degree in Actuarial Science and have some basic in R and SQL. I would like to ask about the future of data science. Does it have a great future for data science related startup in Malaysia? Actuary vs Data Scientist which one have a better opportunity and greater future?
Hi BS,
Both data scientists and actuaries offer great career prospect in the 21st century. I have seen a steady demand of actuaries in the past few years. A data science career gives you more flexibility as it is currently a growing field that is in demand in many industries. For the day-to-day responsibilities, there is overlap between mathematics and analytics. But there are some differences as well. For instance, actuaries focus more on the development of financial engineering, and data scientists focus more on the data and software engineering. In fact, many of my students come from an actuarial background. They attend my class to learn data science skills and prepare themselves for data science related roles in their companies.
While an actuary manages and deals with risks and uncertainties, a data scientist will look at data problems on a macro level. A data scientist also leads a data team to gather, clean, model, and present the results. Data scientists work across many fields and industries, where actuaries traditionally work in mathematics and finance related industries like banking and insurance. Nonetheless, modern actuaries and data scientists should equip themselves with communication skills and understanding of information systems to excel at their job.
Hi,
I have a Bachelor’s degree in Software Engineering and currently work as an application support (dominant in Java Programming). I’ve no experience with data mining, data science, machine learning during my university time. (Pure Java-based knowledge for system development)
I have a goal to step into Data Science environment and I do have research on Data Science and Machine Learning during my free time. However I’ve no working experience for Data Science and machine learning. Base on your opinion, do I still have chance to work in Data Science environment?
Best Regards
Chew
Hi Chew,
Yes, you certainly have an opportunity to work in Data Science environment. Every domain generates large amount data these days, and with your background in Java programming language, it shouldn’t be too hard to switch to a scripting language like Python or R. Perhaps you can share with me what research have you done on machine learning and what is your area of interest, so that I can give you more specific recommendation ?
Hi Dr!
I just turned 18 and will be doing my A-levels for 2 years. After that, I’m thinking of pursuing in Bsc Data Science at Warwick University. My question is , is it better for me to enroll in Data Science immediately for degree or is it better for me to enrool in Bsc in Computer Science first and then take a Masters / PhD in Data Science ?
I’m also taking an online data science course at Udacity and learnt a bit of Python online. What do you recommend me to do to build my skills as someone who is beginning to learn to become a data scientist aside from doing project and research ?
Hi Faris,
It depends on your interest and what you want to be eventually because CS degree is pretty different from DS. If your genuine interest is to pursue a ds career or become a ds, you may still take the DS course and pursue a general CS degree later, then at Ph.D. time decide what your area of focus is. Another way is to sit in and audit the class. Some of the courses between CS and DS are overlapped so it will save you some time. I’d also strongly recommend you to take some electives on DB systems, Algorithms, data structure and web development. For web development, it helps to strengthen your presentation skills using web pages, and you will find it useful later on in your other projects.
Picking up Python is a good move. You can also look into soft skills e.g. story telling and creating effective presentations. These skills are essentials because many times data scientists need to convey the ideas and results to the stakeholders. On technical skills, you can learn some web programming as mentioned, HTML, CSS, JavaScript are essentials and will be beneficial in a long run.
Hi,
I got a phd scholarship and job offer, both in deep learning. Which one do you think will have an impact on my future carier, a 4 years experience or a PhD?
P/S: My plan for the next 10 years is to be deep learning engineer/researcher, not lecturer
You will learn more while working if the job you are offered is really on deep learning, where you get to apply the techniques to solve actual problems and not just evaluating different framework and packages.
Since you are not interested in becoming a researcher, the PhD degree might not suit you. As a PhD holder is more likely to get ‘research’ orientated positions (both academic and industry).
You can always go back to school later if the situation allows. It is true though that the longer you stay out of school, the harder it is to return to an academic environment (lower pay, lack of set work hours, etc.) However, a one to two years of gap might be ideal to provide time to identify your priorities, explore different areas in deep learning, then only decide whether you want to pursue a research degree.
Hi Dr! I was looking up data science in Malaysia and I just want to say that it’s a great delight to have found your blog. For the record I’m a math major and aspiring data analyst/scientist (I somewhat know the differences, I just can’t make up my mind yet). I’ve always been interested in I.T. and I regretted not taking it as my minor but oh well. First off, I’m going to enroll on http://dataquest.io where I can learn data science by doing hands-on practices. Then I think of doing some projects to create a portfolio. I’m sure it’s gonna be a challenging yet exciting journey to embark on. Will doing these without taking a Masters or Phd in a related field be sufficient to apply for the job? Is there anything else that I should do?
Hi Fatin,
You are right, as these positions emerge, it is pretty hard to distinguish the differences between a data scientist and a data analyst, because they are both key members of a data science team! Data scientists look after many issues on a higher level including the processes, governance, applications, strategies, all the way to big data architecture. Data analysts focus more on the analysis, problem formulation, and they need to figure out the analytical processes to make sense of a dataset and how to communicate the results. Therefore data scientists work closely with data analysts and data engineers.
I always recommend my students to start some side projects to build their data science portfolio. By doing this, you will have a better understanding of the entire data analysis process, and identify your areas of strengths and weaknesses. Taking a Master or PhD will be a plus point, but I won’t say it is a must in getting a job.
A postgraduate degree provides formal training in fundamentals such as mathematics, computer science, data analysis, etc. For my case, PhD shapes my thinking and trains me to be good at critical thinking, formulating the hypothesis and formal research process. And spending 3 years to solve one problem (machine learning in short text) gives me a good intuition in understanding data and applied machine learning. Personally, I think passion, strong self-motivation and determination to work on real projects is the only way towards a great data scientist. I would suggest working with some real data in the industry, then only decide whether you need to further your study.
Hi Dr Yap,
I have a Bachelor’s degree in Accounting and worked in a Sales role for various non computing industries for the past 6 years.
I have taken an inclination towards Data Science. I understand the skills required while browsing through Data Scientist/Analyst job listings are something I have to learn from scratch.
What is the best approach to acquire these skills?
A Msc in Data Science vs online courses/ certifications from various workshops providers?
I am in the midst of learning Python online but not sure if online learning is the right way towards gaining employment in this industry?
Hi Shi Wei,
After teaching in both universities and conduct online courses, I find that the difference between formal courses and online courses is the learner’s motivation.
If time permits and your financial situation allows, taking a Master degree is not a bad choice. It immerses you in an environment where structure motivates you, and the social interaction with other students might give you the support. One important feedback I receive from my online course students is that they find it useful where they can rewind, watch the video again, participate in the forum and get the particular attention from my teaching team to solve their problems. They can learn at their own pace and progress only when they are confident about a particular concept. In most cases, the concepts itself are not hard, but it requires some time on the hands-on exercises to fully understand them.
I wouldn’t say data science itself is an industry although that seems like how it is portrayed. The two key factors to getting a data scientist or data science related jobs in an industry are number one: your domain knowledge. For example, how well you know the industry, what are the pain points people are facing. Secondly, you have a portfolio to show that you are capable of applying data science techniques to solve (some of) the pain points.
You’ve made the right first move by picking up Python, as data scientists mainly use R and Python in our day job, and sometimes both, as long as we get the high quality analysis done with least effort. Since you have experience in other fields, I would suggest to work on some side projects and start building your data science portfolio. For example, using exploratory data analysis on the previous sales results to find patterns, conduct a time series forecasting on the sales trends, or using predictive analytics to predict the sales volume next year. You can find many online tutorials on these topics, and feel free to contact me if you need any further assistance.
Hi Dr.
I’m currently doing my Bachelor of Computer Science course in Multimedia University. Next semester I’ll have to choose my majoring which is Data Science. I’m kinda weak in programming languages. So would it be suitable for me? Once I graduate as data science student, do I need to work related with programming? Is there any job which is not related with programming in Data science?
Hi Yap, I can totally relate to your feeling, as many of my students have the same issue (see I use the word issue, not the word “problem”?). To be a good data scientist, you need programming skills. A big part of any scientific research is the reproducibility, and we can easily achieve that by using scripts. In our day-to-day job, we spent a lot of time on tasks like cleaning data, exploring data, experimenting different parameters and evaluate the models. You can’t actually repeat these tasks or pass it on to somebody manually, as some tasks run for an extended period, some of them run in particular sequence. You need to use programming skills to write scripts to complete these tasks.
Since you mentioned you have a Bachelor of Computer Science, I assume that you have a fundamental understanding of logic, algorithms and data structure. That should be sufficient.There are data science related jobs that do not require programming, but you probably won’t get too far with that. Imagine we even to write VB Script while using Microsoft Excel, and other tools like SAS also requires scripting to perform task automation.
Programming is not that scary, and it is not everything of what a data scientist does. Data scientists spend even more time on understanding the actual underlying problem, formulating hypotheses and finding the right way to solve problems. Perhaps you haven’t gotten the eureka moment of the programming concepts. Once you get there, programming is going to be a breeze for you.
Do let me know which part of programming got you stuck in particular and I will show you the examples in a more understandable manner.
Thanks for your reply Dr. What is the difference between Data Analyst and Data Science? Once I graduate as Data science student, can I get a job which is IT data analyst?
Sorry I missed your reply. While there is no formal definition for a data analyst and data scientist yet, there are three points that you should take note:
1. Data scientists often formulate problems with the management team, then go on to solve them. Data analysts usually are given specific problems to solve.
2. Technical knowledge required for data analysts are much less. Data scientists and analysts work closely with data engineers to gather, clean and retrieve data, but data analysts typically work on simpler queries, and they don’t have to write much code. Data analysts mainly rely on existing BI tools or packages. Data scientists are expected to run experiments, build statistical models and machine learnings.
3. Data analysts analyze known historical data, from new perspectives and providing insights, while data scientists work on estimating the unknown.
A data science degree should give you sufficient knowledge and training to prepare you for a data analyst job (you may look up the JD on job search website, or http://www.snagajob.com/job-descriptions/data-analyst/). Do take note that either job requires you to have an effective communication and storing telling skill, and ability to think and analyze information critically.
Hi, Dr Lau.
I love to follow your story and I can see you are progressing well since few past years.
I’m currently self-teaching myself on data science and will graduated soon in September 2017. I also experienced on using Big Data Framework like Hadoop and Spark, as I’m using it for my current projects. Some deep learning framework like TensorFlow, Caffe and Keras also my everyday tools. I’m doing time-series prediction and image processing with Python. (RNN and CNN, other machine learning algo like RF, SVM, GBT etc)
I really pushing myself hard to study all these.
My questions is, what else should I learn, and I plan to find jobs, if possible as Data Scientist, is that possible event though I’m just have Bachelor Degree ? Because most of the jobs requirement for data scientist required Ph.D or Master Degree.
Thank you.
Hi Nazmi,
Selamat Ramadhan. I am delighted to hear that you found the blog post useful. In most cases, a Bachelor degree is sufficient unless you are looking into more research intensive or ultra-niche fields. You have a broad range of skills that are highly on demand for the current market, which is good news. What I would suggest is to document them, start to showcase how can this be applied to real world applications, and helping companies to solve problems. This is the unique value of a data scientist. You can look into Indeed, Seek, Glassdoor to read about the job requirements for a data scientist and find the areas that you are passionate. If you can’t find any, keep expanding your skillset and become a master generalist, you can always decide which area you want to be specialized later.
Feel free to contact me if you need guidance, and we can then explore other aspects of your interest.
Thank you for your reply, it really inspired me. I do as what you advise, documenting some problems, and publish in Github. People even starred my projects and I just got offered Data Scientist position in one of most known company in Malaysia today ! I not even apply for it, I just got offered by them ! Very happy with this news. I will share to you more details about the company when I finish fill up the acceptance form and start working in the company.
Thank you very much Dr Lau Cher Han !
Good to hear that Nazmi, keep up with the good work and let me know if anything I can help you with, cheers!
Hi Dr Lau,
I have a master in mechanical engineering. I have been working for 5 years. Is it possible to be a data scientist without a master or degree in data science or even statistic? I am very interested to learn Python. I am thinking of getting some certificate s. Can you share some views or some suggestions to become a data scientist through part time. Thanks. Hope to hear from you soon.
Hi Ng,
Yes, everyone has the potential to become a data scientist with the right tools and knowledge. You don’t have to compare yourself with mathematicians/statisticians/software engineer (and the list goes on). What I suggest is to take the full advantage of what you already know from engineering, like Matlab, simulation, optimization, calculus, modeling to be your competitive advantage. Something that most people didn’t realize is that a lot of the data scientist who has the advantage of programming spent a significant amount of time to pick up the fundamentals if they want to become a great (not just mediocre) data scientist.
Learning Python gives you a good head start, you will learn the basic programming concepts and scripting, an essential skill in any level of data science task. You may take a look at https://www.drhanlau.com/beginners-guide-to-setting-up-your-first-python-data-science-workspace/ where I showed how to install Anaconda, a bundled package with many tools that are data scientists’ favourite. From there, look into building some side projects and grow your portfolio. Feel free to contact me here or buzz me on my Facebook page.
Hi Dr Lau,
Yes I am currently learning python. Thanks for your long reply, really appreciate for your great advice.
Glad to hear that it helps, you are welcome 🙂
Hi,
I don’t have any undergrad degree in stats but I do take some subjects in statistics. Any opportunities for me? I am interested in becoming a data scientists. Looking for a full time course to join in Malaysia.
Hi Mei,
There are many companies that take interns/apprentices depending what are your career goals, but there are many opportunities. You might want to take a look at some job openings and participate in data science competition to understand the range of skillsets you need to have. Statistic helps you to understand the theories and applying the techniques when handling different dataset.
I am doing Msc. in Data science and Business Analytics at APU Malaysia. By June of this year hopefully I will be graduate. Can you please suggest which sector or company will be good for beginners in this field?
Startups, banking, finance are the usual sectors that are always hiring data scientists and analysts. I have also seen an increasing number of media, real estate, and insurance companies started to looking for data scientists, however, their problems are not clearly defined, so you might need to spend some extra time to find out what are their actual data pain points.
Hi Hossain Ahmed,
i am planing to apply for M.Sc Data science and started looking for universities may i know how about Msc. in Data science and Business Analytics at APU Malaysia in context of course curriculum and classes ?.
Hi Hossain Ahmed,
I am looking at Msc in Data Science in Klang Valley, and found not many universities offering this course, APU is one of the few that offers this. May I know how’s is the programme so far? Easy to follow for someone without strong programming background?
Hi Hossain,
I was thinking of starting this course next year. Hopefully you have graduated by now, thus I was hoping you could give me some advice regarding this course and the job opportunities. And may I know if you’re a local or a foreigner, since I am a foreigner working in sales in KL.
Thanks. 🙂
im still searching my passion, ive experienced in Account for 1 year plus, however my background is degree in math and master in statistics, i want to improve my skills in data science during my free time after work because i want myself fit in that position, what would you suggest for me to do first? and how long should i practice, so that i can start apply for data scientis job or maybe start selling service in data science.
*During my study, ive learned R and SPlus software, ; i dont want my statistics knowledge gone wasted, plus i find statistics is very useful and interesting,,
thanks
Hi Fatin, you have a head start compared to others who are beginning their data scientist career. Not only you have learned R programming, but you also have a solid background in statistics that is the foundation of many data science fields.
I would suggest you to start a side hobby project to start analyzing some of the data you already have from existing projects and take them to the next level. Otherwise, you can start with something fun like analyzing your friends’ music taste based on their public Spotify playlist, their food preference based on their Foursquare check-ins. Something more business related will be examining the churn factors behind people who switched telcos, or study the association among buyers pattern using different techniques. You can also head to Kaggle to look for challenges. There are many open sourced datasets available and lots of discussions going on.
Let me know if you have any other specific questions that I can be helped.
Is it possible to enter data science/big data field without an official degree in statistic/maths/data science? I am taking a Microsoft Professional Program Certificate in Data Science hosted on edX. Find it quite interesting to learn SQL.
Hi Kam Siong, yes of course. You can pick up mathematics and statistics on your way to becoming a data scientist, and you are taking the great first step to start with a MOOC. SQL is an important language as it lays the fundamental of the modern database technologies, I picked it up 20 years ago, and the language itself is still the same! Do you have any Object Oriented Programming (OOP) background? Take a look and compare OOP and SQL, you will find lots of similarities between them.
Hi Dr Lau,
I have learned a bit about data science since 2016 from different MOOC platforms. I learn about R & Python (preferred Python) from coursera, Statistic & probability from Stanford online, SQL and Tableau.
Recently I just completed a Data Foundation Nanodegree offered by Udacity that include combination of statistics, SQL, excel, and tableau. Thinking to go for higher level skill by enrolling into Data Analyst Nanodegree that specialise in Python skill.
Hi Kam Siong,
Good to hear that you are progressing well. The skills that you have obtained and crucial and definitely help you in a long run. Have you completed the Data Analyst nanodegree yet?
Hi Dr Lay,
I haven’t enroll in Data Nanodegree due to tight schedule of my job and financial constraints. Anyway, I always check out other free resources to keep my memory fresh.
Recently I plan to change my role in my team so that I could shift my responsibility from finance analysis to process improvement for finance. Hope the changes will allow me to apply more data science skill.