4IR Technical Center

Tangerang – Judging from the increasing need for data talent, prospective data practitioners are competing to learn data science to improve their skills. As a data science learning platform, DQLab held data mentoring with Iqbal Hanif as Junior Data Scientiist at Telkom Indonesia. This event discusses thoroughly exploring a career in the data field, which will be held online Wednesday, December 21, 2021.

Data mentoring begins with the introduction of big data at Telkom. Iqbal explained that big data has 3 main components, including a big data platform, big data management, and big data analytics. As a data practitioner, you certainly need skills and abilities that are tailored to the industry. According to Iqbal, the skill set needed when working in the data field, such as Data Analytic, Data Engineering, Data Visualization, Big Data, and so on.

Explaining more deeply about work in the data field, Iqbal conveys data that is often analyzed by Telkom. The explanation begins with an example of demographic data, then purchased products, billing/transactions, users behavior, service quality, and infrastructure/assets. Everything includes customer data in accessing Telkom services.

Furthermore, Iqbal describes the tools he often uses at work. In databases, he uses Postgre SQL, MySQL, Hortonwonworks, N, and Oracle, all of which have the same language, namely SQL, but differ in the process of accessing the database.

“For those who are afraid of coding, you can use ETL such as Pentaho and Talend,” said Iqbal

The discussion continues with the tools for programming and analytics that Iqbal often uses, namely R and Python. Then, for visualization he uses Tableau and Power BI. According to Iqbal, the main tools that data talent candidates must master are Linex and Office including Microsoft Word, Excel, Power Point.

“Obtaining one customer is more expensive than keeping one customer” According to him

So in this mentoring data, Iqbal explains the process of making projects at Telkom. It begins with a classification that includes customer predictions for the products being marketed. Followed by the process of regression, clustering, statistical analysis/method, and the goal is data visualization. As a data practitioner, when creating a data visualization project, it is very important to convey the results of the analyzed data so that everyone can understand it.

As a professional data practitioner, there are several challenges that need to be faced when starting a career in the data field. First, there is competition among data practitioners, then the work style must also be considered, according to needs. Competition when applying for jobs also needs to be watched out for. Understand the work environment and continue to develop existing skills.

“For friends whose backgrounds are not the majority, you must highlight one advantage in yourself. Like, have won a competition or have interesting work experience and have a portfolio. The next tip is to look for specific job vacancies for friends who are not yet outstanding. Iqbal said

Data Mentoring is closed with 2 additional tips from Iqbal not to stop learning, find a preferred learning platform and don’t forget to implement the knowledge gained through projects.

One of the data science learning platforms that can be used to access projects that can be used to build a portfolio is DQLab.id

By Annissa Widya


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