RoadMaps

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Data Professional RoadMaps

Data science is paramount in the data era (industry 4.0) and its fundamentals should be mastered by all data-related professionals such as data analysts, data scientists, data engineers, or AI engineers. However, due to the complexity and dynamics of the field and the vast technologies or tools that involve, it is very challenging for data enthusiasts to keep up. One of the remedies to this situation is constant learning and improvement for all of the related parties in an organization.

There have been numerous formal and informal educational institutions dedicated to overcoming the knowledge gaps in data science. Informal data science education is currently a growing business, due to the slow developments in formal education to adapt to industry needs. Nevertheless, a couple of major flaws from most informal education are the lack of roadmap/curriculum and fundamental science behind the skills that are being taught. Practical skills produced from these systems are superficial and limited to some basic cases only. Participants are having trouble solving the ever-evolving data challenges.

taudata roadmaps (learning paths/curriculum) is one of our answer to the issues previously mentioned. We understand that the conventional curriculum system is incapable to solve sophisticated and rapid industries’ needs. taudata roadmap is different from conventional curriculum due to the following properties. First, it is highly customizable for different data professions and with different focus/levels of interest. Moreover, it is open-sourced and adopt the AI/Big data philosophies of crowd-based knowledge system to rapidly evolve continuously through time. Thus, the curriculum presented on this page is a progressing roadmap, and the improvement will continue evermore. Finally, as an informal “curriculum”, tau-data’s roadmap is also very flexible in its content. For example, each module in the roadmap need not be of the same unit of time as in formal curricula.

Data Analyst (DA)

For those who have just started learning Data Science, we recommend the following Data Analyst Roadmap (click on the image for further details):

Data Scientist (DS)

Upon finishing DA roadmap, we recommend the following learning path if one wants to become a data scientist (click on the image for further details).

Data Engineer (DE)

Upon finishing DA roadmap, we recommend the following learning path if one wants to become a data engineer (click on the image for further details). Note that DE does not need to finish DS roadmap:

AI Engineer (AE)

Upon finishing DA roadmap, we recommend the following learning path if one wants to become an AI engineer (click on the image for further details). Note that AE does not need to finish DS & DE roadmap:

Open/Public Datasets

• https://taudata.blogspot.com/p/openpublic-dataset-repositories.html

More taudata Articles

• https://taudata.blogspot.com/

Acknowledgments:

We appreciate inputs from experts in data science field that enriched the roadmap (curriculum) with their vast knowledge and experience:

• Dr. Taufik Sutanto, MScTech (taudata Analytics)
• Pak Doan Siscus dan Pak Juan Intan Kanggarawan (Traveloka Indonesia)
• Data Science dan Analytics Leaders Traveloka Indonesia.
• Dr. Sarini, M.Stats (Universitas Indonesia)
• Setia Pramana, M.Sc, PhD (Badan Pusat Statistik Republik Indonesia – STIS)
• Dr. Andry Alamsyah, S.Si, M.Sc (Telkom University)
• Dr.Eng. Anto Satriyo Nugroho, M.Eng (BPPT – INAPR)
• Dr.rer.Pol. Dedy Dwi Prastyo, Msc (Institut Teknologi Sepuluh Nopember – ITS)
• Dr. Bagus Sartono, M.Si (Institut Pertanian Bogor – IPB University)
• Dr. Tri Handhika, S.Si, M.Si (Universitas Gunadarma)
• Dr. Syopiansyah Jaya Putra, M.Sis (NICT – UIN Jakarta)
• Dr. Imam Marzuki Shofi, M.T. (ZiShof – UIN Jakarta)