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Digital Skills Resource Guide



Data Analytics is the science of analysing raw data by inspecting, cleansing, and transforming information for the end user. It reveals trends and metrics, so the user can make optimal business decisions and improve the system. 

The process involved in Data Analysis comprises six steps and is called the Data Analytics Lifecycle. The six circular process that defines how information is created, gathered, processed, used, and analysed for business goals. Mapping out the business objective in Phase 1 and then working towards it will draw out the rest.


Check out the tabs below for more details on the Lifecycle!

At Phase 1, you need to define your data's purpose and how it is going to be achieve at the end of the data analytics lifecycle.


Do I have enough information to draft an analytic plan?

Has the organization attempted any similar project in the past?

Do I have the necessary resources available to support the project?

In Phase 2, the attention of experts moves from business requirements to information requirements. Familiarize itself with the data thoroughly and take steps to condition the data.


Collecting --> Processing --> Cleansing 

Phase 3, it is time to build a model that utilizes the data to achieve the good. To determines the methods, techniques, and workflow it intends to follow for the subsequent model building phase. Explores the data to learn the relationship between variables and selects key variable, eventually find a most suitable model.


What type of model am I able to try?

How can I refine the plan?

In Phase 4, it is time to develop datasets for testing, training and production purposes. Using Phase 3 designed model, the team build and operate the model. Next, perform a trial run of the model to determine if the existing tools is suffice to run the model.


Is the model robust enough?

Is there any other ways to refine the model to avoid any failure? 

Remember the goal set in Phase 1?

In Phase 5, it is the time to determine if the results of the project are met based on the criteria set. It starts by communicating to the necessary stakeholders/personnel to determine the result.

Success? Failure? 

Identify key finding --> Business value associated with the results --> Summarize --> Convey finding 

The last Phase, delivers the final report with coding, key finding, briefing and technical documents. Next, to implement the models in a production environment, run a pilot project and observe if the results matches with business goal. 


Finding as per objectives? --> report results are finalized

Findings deviates as per objectives? --> move backwards to any previous phase 

User of Data Analytics

Data Analyst is a person that extracts information from a given pool of data.

An Data Analyst uses data modeling, data cleaning and data conversion methodologies to exact information. 

Industries that uses Data Analytics:

  • Medical 
  • Technology 
  • Business, etc

 It assists industries in analyzing trends in the market, business performance, requirement of client and etc.

Data Analyst uses data visualization techniques and tools to communicate the results.  

Data Engineer specializes in preparing data for analytical usage. 

It involves developing, constructs, tests and maintain architectures. For instances, database and large-scale processing system.

Data Engineer works on both structured and unstructured data. 

Some tools that Data Engineer uses:

  • Hadoop
  • Java, etc

Data Scientist cleans, massages and organizes big data. 

It performs descriptive statistics and analysis to develop insights, build model and solve business needs.

Tools that Data Scientist uses:

  • Python
  • R, etc



Connect your database, create visualization, share with a click 


Discover, prep, analyze, deploy


Close the gaps between data, visualized with colors


Basic, popular and widely used