Data Science

Three Data Visualization Tools You Must Know

2 min read

Through Big data analytics, hidden patterns and correlations can be uncovered, which drives business growth and success. Therefore, all major corporations attempt to maintain their competitiveness through frequent and in-depth data analysis and visualization.

Data Analysts are in hot demand. To become an invaluable asset for employers, there are 3 data visualization tools you must know.  

1) SAS

SAS is a software suite for advanced analytics, business intelligence, and data management that is used by a variety of large enterprises. It makes analytics accessible to anyone seeking insights from data through a rich interface, and easily handles analytical problems of any size or complexity.

The downside of SAS, though, is that it is difficult to learn and has a deep learning curve. Also, think twice whether you are willing to invest your financial resources to purchase the pricey software before dedicating yourself to learning SAS.

2) Microsoft Power BI

Power BI is a cloud-based business analytics service, enabling anyone to visualize and analyze data with greater speed, efficiency, and understanding. With more than 20 built-in visual effects, Power BI is an effective tool to make rich visual charts. A personalized dashboard with a unique, 360-degree view of a business can be created to obtain insights throughout the organization.

The function of Mobile Alert allows immediate notification, so management can catch up with updates and make highly-informed decisions in time.

3) Tableau

Although Excel helps make sense of the data and allows for a quick table configuration, Powerpoint is usually used together to present the data in a more visual and understandable way.

With a direct connection to the database, Tableau can display data in a meaningful way through interactive data visualization. Tableau is user-friendly and has a relatively low start-up cost. With the affordable purchase of a license, you are good to go!

We are living in a new world of analytics. To stand out in the Big Data Labour market, having full knowledge of analytical software will certainly put you in a favorable position.


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

X
Facebook
YouTube
LinkedIn
Instagram