What are the Steps Required for Students after SPM/O-Levels or Pre-University to Become a Data Scientist in Malaysia
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Data scientists combine statistics, mathematics, programming, problem-solving, capturing data in ingenious ways, the ability to look at things differently to find patterns, along with the activities of cleansing, preparing, and aligning the data. Statistics show that by the year 2020, there will be about two million job openings for data professionals and that the demand for people with this knowledge and skill will outstrip supply by a ratio of two to one. It’s a global phenomena which is already in motion and Malaysia has set its sights on developing 20,000 data professionals and 2,000 data scientists by 2020.
Students after SPM/O-Levels or Pre-University wanting to know how to become a Data Scientist in Malaysia can read on further in the article below which will explain to you in 3 simple steps.
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How to Become a Data Scientist in Malaysia?
What is a Data Scientist?
Data scientists must have expertise in several different disciplines. Generally, data scientists must possess the statistical knowledge and computer skills that are needed for solving complex problems. Using descriptive, predictive, inferential, and causal models, they can explore and anticipate problems then work to model a solution based on a multitude of factors.
Data scientists are part mathematician and part computer scientist. Their skill set encompasses both the business and information technology sectors, which is why they are highly sought after.
Data science is deep knowledge discovery through data exploration and inference. This discipline focuses on using mathematical and algorithmic techniques to solve some of the most analytically complex business problems. In doing so, they leverage troves of raw data to figure out the hidden insight that lies beneath the surface. The core of the field centers around evidence-based analytical accuracy and building strong decision capabilities. However, data scientists must also verbally and visually communicate their findings to stakeholders who may or may not understand the statistical jargon. Thus, data scientists must be excellent communicators.
Now that you know what a Data Scientist is, here are 3 steps on how to become one:
Step 1: Choose the Best University for Data Science
Now that you have decided to study data science in Malaysia, you will need to figure out which university can equip you with the necessary knowledge and skills to be successful in this career. First of all, your results must meet the entry requirements to join the Foundation or Pre-University course. Upon completion of the Pre-U, you will then enter the Data Science degree for 3 years.
Studying at the top ranking university in Malaysia gives you a lot of opportunities in your future professional life. Top universities provide a high standard of education that equips you well for your future career.
Furthermore, a degree earned at a reputable university in Malaysia makes seeking employment much easier whether locally or globally. Part of finding the right university in Malaysia for you will be picking out the criteria that matters to most to you, and then seeing if any of the top private colleges or universities in Malaysia that you’re looking at fit those criteria. University Rankings, Awards & Achievements are indicators of the level of standard that they have achieved and these could serve as a guide for you in choosing the right university to study at.
Choosing the right university to study Data Science can be a complicated and confusing process. There are many universities offering this course but not all Data Science courses are the same at each university. It is important to understand the differences to see which one fits your future career goals.
There are many factors involved in making this choice therefore contact me for a free consultation. I have more than 20 years experience in the education industry and is knowledgeable of the ins and outs of the private universities in Malaysia to be able to assist you in making this important decision.
Step 2: Completing your Data Science undergraduate studies
The most sought-after majors for data science are statistics, computer science, information technologies, mathematics, or data science (if available).
During your undergraduate studies you will become proficient with the most widely used programming languages in data science such as Python, Java, and R — and refreshing their knowledge in applied math and statistics. In addition, continue to learn programming languages, database architecture, and add SQL/MySQL to the “data science to-do list.”
Now is the time to start building professional networks by looking for connections within your university, look for internship opportunities, as well as, ask lecturers and advisors for guidance.
During your university studies, it is also important to improve your command of the English language, communication skills, critical thinking skills, and leadership skills.
Step 3: Looking for your first job in Data Science
Malaysia’s national ICT agency Multimedia Development Corporation (MDeC) has unveiled a plan, supported by seven public and private institutes of higher learning (IHLs), to increase the number of local data scientists from the current 80 to 2000 by the year 2020.
Statistics show that by the year 2020, there will be about two million job openings for data professionals and that the demand for people with this knowledge and skill will outstrip supply by a ratio of two to one. It’s a global phenomena which is already in motion and Malaysia has set its sights on developing 20,000 data professionals and 2,000 data scientists by 2020.
There is a tremendous requirement for Data Scientists and Big Data Specialists worldwide now and in the future, with hundreds of thousands of new job opportunities emerging globally. In Malaysia alone, by the year 2020 this need is expected to reach at least 20,000 data professionals and 2000 data scientists. Job demand as well as salary for qualified Data Scientists or Big Data Professionals in Malaysia is high.
Data science fresh graduates can demand starting pay in the range of RM4,000-RM8,000 — making it the highest paid entry level job in the country today. Furthermore, an experienced professional in the field can demand up to RM15,000 a month.
Many employers are building their teams from ‘scratch’, accepting candidates with entry-level industry experience. The large salary range reported by employers for a data scientist from more than RM 15,000 per month to less than RM 5,000 per month may reflect a lack of precision in defining the role of a data scientist. While compensation will differ by candidate experience and performance, it is important that this role is not undervalued. Data science competencies such as statistical modelling and machine learning require a high education investment which should be recognised.
What does a Data Scientist do?
Essentially, a data scientist extracts meaning from the varying types of data (e.g., structured, unstructured, semi-structured) that flow into the enterprise. On any given day, a data scientist may be extracting data from a database, preparing the data for various analyses, building and testing a statistical model or creating reports that include easily understandable data visualizations. There is a data science cycle which isn’t a set of rules as much as it is a heuristic:
- Data collection
- Data preparation
- Exploratory data analysis (EDA)
- Evaluating and interpreting EDA results
- Model building
- Model testing
- Model deployment
- Model optimization
The above is iterative, meaning a data scientist will be in “evaluation mode” throughout the entire process. Or, perhaps, after the EDA phase, they find that the data doesn’t fit the problem they are trying to solve (or the question they are attempting to answer). They may need to start over or carefully choose which portions of the data that does apply, then go back and collect additional data. Such is the reason they need a higher level of combined skills including research design.
Job Description for a Data Scientist in Malaysia
While data science projects and tasks may vary depending on the enterprise, there are primary job functions that tend to be common among all data science positions such as:
- Collecting massive amounts of data and converting it to an analysis-friendly
- Problem-solving business-related challenges while using data-driven techniques and tools.
- Using a variety of programming languages, as well as programs, for data collection and analysis.
- Having a wealth of knowledge with analytical techniques and tools.
- Communicating findings and offering advice through effective data visualizations and comprehensive reports.
- Identifying patterns and trends in data; providing a plan to implement improvements.
- Predictive analytics; anticipate future demands, events, etc.
- Contribute to data mining architectures, modeling standards, reporting and data analysis methodologies.
- Invent new algorithms to solve problems and build analytical tools.
- Recommend cost-effective changes to existing procedures and strategies.
- Data Scientist Skill Set
- Experience and Fluency in many of these computer/coding programs: SAS, SPSS, MATLAB R, Python, Java, C/C++, Hadoop Platform, SQL/NoSQL Databases.
- Business Savviness: Data scientists need to understand the business sector they are working in and create solutions to complex problems that align with business logic/objectives.
- Communication skills: A data scientist can clearly and fluently translate their technical and analytical findings to a non-technical department. They must also be able to understand the needs of their non-technical departments (such as business development or marketing teams) in order to analyze the data correctly. A data scientist must empower the business to make decisions by presenting robust and verifiable information.
- Expert Technical skills in the following:
- Math (g., linear algebra, calculus, and probability)
- Machine learning tools and techniques
- Data mining
- Data cleaning and munging
- Data visualization and reporting techniques
- Unstructured data techniques