Data Science

How to learn Data Science?

3 min read

Data Science has emerged as an important discipline for business growth in recent years. With corporations’ increasing demand for data analytic specialists, data analytics skill has become a highly sought-after skill. It motivates many IT talents to pick up the skills for a career change.

How to learn Data Science? For those aspiring data scientists but with absolutely no idea how to get started, what’s the best way to go about it?

How to learn Data Science? In Hong Kong, there are many available options to learn data science and to get officially certified. For instance, HKU, Chinese University, and HKUST provide master’s degrees in Statistics, Data Science, and Big Data Technology.

PROS: The upside is, a university degree is a recognized benchmark to a specific skill set that certainly impresses your future employer, thereby enhancing the likelihood of getting hired.  

CONS: Learning data science in university is a considerable investment of both money and time. It can take up to 2 years to complete a degree in Data Science. It might be time-consuming for those who expect a large return in a short period of time.

2.     Certificate Online Courses

How to learn data science without the luxury of time and money? Taking an online course is a great alternative to finding your way into a data science career. Online courses related to statistics and data science are wide-ranging from comprehensive big data and business analytics to data analytics crash courses, which are designed to accommodate students at different levels.

PROS: The perk is that you can learn at your own pace and select courses that are tailored to your needs and schedule.

CONS: Online course has relatively less interaction with both instructors and other students, which might undermine the overall student success. The quality of instructors is another important consideration because it directly affects students’ learning outcomes.  

Remember to look for internationally recognized online courses, otherwise, your certificate would just go down the drain.

3.     Internal training within organization

Learning data science can be free-of-charge (but only if you are lucky enough to have the right employer)! Due to its high demand, finding data science specialists can be a challenge for employers. Thus, more corporations opt to organize Data Science training programmes to train employees with the aptitude to fill the skill gaps.

PROS: Learning data science can be free! You can also look for internal opportunities to get involved with the Data Science projects, which is a great learning opportunity.

CONS: Not a lot of corporations are generous enough to provide this kind of professional development.  Sometimes, you might need to take the initiative and ask for the option of internal training.

4.     Short training courses

If the above options do not seem conceivable to you, how about taking short-term training courses with a high-quality course provider like Venturenix Lab?

PROS:   Learning Data Science can be fun and effortless! Venturenix Lab offers a wide range of data science-focused courses. With seasoned instructors to preside over the courses, Venturenix Lab-trained students will be equipped with up-to-date data analytics skills that hone their competitive edge.

With a solid bonding within the IT community, Venturenix Lab also helps strengthen students’ resumes and Linkedin profiles to boost their chances of getting a job.

CONS: Enrol in one of our courses and find out yourself!

Sign up for our Newsletter

Join our newsletter and get resources, curated content, and design inspiration delivered straight to your inbox.

Related Posts

Data Science

ChatGpt 都識揼Code?IT人難逃被AI人工智能取代的命運?

IT 人會不會被AI人工智能取代?近年來,隨著人工智能技術的不斷發展,許多人擔心自己的工作可能會被AI取代。目前,許多AI技術已經能夠完成軟件開發的某些部分,因此有人認為,軟件工程師的工作可能會被AI取代,飯碗也可能不保。 AI 人工智能會取代IT人的想法,可能源自於大家對軟件工程師工作及AI的理解不夠充足。 AI技術的發展引發了對於軟件工程師職業前景的擔憂。然而,軟件工程師的工作並非像一些人想象的那樣容易被AI取代。讓我們嘗試解釋為何AI無法取代軟件工程師吧(至少不能大規模地取代)。 要了解AI 人工智能能否取代IT人,首先我地要知道AI 是如何學習的。AI學習是透過大量相似開源數據學習相對重覆的事物,請留意重點,是「大量」,「相似」,「開源」數據。例如認人,搜尋法律案件,分析病人身體數據,等能力。但如果一個只被訓練認人的AI,見到一張猩猩的圖片,它未必即時辨認到這張圖片中的不是人類。又或者一個被訓練分析香港法律的AI,突然香港有需要增加一條法例,AI並不能夠根據一條新的法例提供準確意見。 以上東西都可以有大量數據的原因,是因為人像相在網絡上可以輕易找到的。法律判刑及理據大部份都是公開的,而判刑準則大都依照以往例子。病人數據當然並非完全公開,病人個人資料是絕對保密,但除去個人資料後的血液數據或X光片等不同資料,則有醫學及研究作用,而醫生分析病情都是根據某病人的數據或檢查結果,比對以往類似病歷的病人,而得出某一病人是健康或生病以至於哪一種病的理據。以上的例子都是AI人工智能能代替人類工作的最佳例子,透過「大量」,「相似」的「開源 」或「開放」數據,而「得出結論或結果」的工作。 再以作曲為例子,AI人工智能可以透過大量例如廣東歌,再透過告訴它哪一首歌最大熱,它便可以透過以前流行大熱的歌中找一些相似的「Pattern」,例如這些歌大部份幾分幾秒會去到副歌,副歌多長,通常每段配搭多少個音節,或者靠寫該AI 的人告訴它,還有甚麼因素及Pattern能影響一首歌會否大熱,它再嘗試根據這些條件或Pattern寫一首歌。但它寫不到新的風格,或者它隨機寫到新的風格之後,它無法估計這首歌有否大熱的機會,最終仍是需要人類作最終決定。...

Don't forget to join our upcoming free IT CAREER TALK on Eventbrite