The demand for Data Scientists in the digital industry in Indonesia has sharply increased since the rapid growth of e-commerce in recent years. This can be observed from the announcement made by the Minister of Communication and Information Technology, Rudiantara, in 2017, that Jack Ma had officially become the e-commerce advisor for Indonesia. This demonstrates the government’s commitment to oversee the e-commerce industry. In mid-March 2018, Lazada received a pouring investment of Rp27 trillion from Alibaba. With the presence of Lucy Peng, one of the eighteen founders of Alibaba, as the CEO, it is possible that Lazada will become the “Alibaba” of Indonesia in the years to come.

Data Science is closely related to Big Data, like two sides of a coin. The emergence of Data Science is a result of the Big Data phenomenon. So, what are Big Data and Data Science? According to Wikipedia, Big Data refers to an enormous amount of both structured and unstructured data that is difficult to process using traditional database and software techniques. In most enterprise scenarios, the volume of data is too large, moves too fast, or exceeds the current processing capacity.

Meanwhile, Data Science is a specialized field that focuses on studying data, particularly quantitative data (numerical data), both structured and unstructured. Various subjects covered in the field of data include all data processes, ranging from data collection, data analysis, data processing, data management, archiving, data grouping, data presentation, data distribution, and how to transform data into a unified piece of information that can be understood by everyone.

Here are the 8 main skills that a Data Scientist should have, according to Udacity, based on the industry needs in its case studies (Google, Amazon, and IBM):

1 Proficiency in basic software usage and programming

Performing data analysis requires a minimum proficiency in software usage (tools) such as SPSS, Microsoft Excel, and SAS. The ability to use programming languages such as Python and R to assist with data analysis is certainly an added value that the industry considers when selecting a Data Scientist candidate.

2 Fundamental knowledge of statistics

Fundamental knowledge of statistics is a vital skill for a Data Scientist, including Descriptive Statistics, Maximum Likelihood, Mathematical Statistics, and others. As statistical knowledge is required by all industries and institutions to perform data analysis.

3 Machine Learning

Machine Learning is a branch of Artificial Intelligence (AI) that focuses on developing a system capable of learning “on its own” without having to be repeatedly programmed by humans. Examples of this include image recognition applications, personal assistants like Siri and Google, chatbots, facial recognition, self-driving cars, and other specific domains.

4 Data Mining

Data Mining is a process that utilizes techniques from statistics, mathematics, artificial intelligence, and machine learning to extract and identify useful information and relevant knowledge from various large databases.

5 Calculus and Linear Algebra

Calculus and Linear Algebra are the first branches of mathematical sciences. Calculus is the study of limits. Understanding of functions, integrals, derivatives, and trigonometry in Calculus is a fundamental skill for a Data Scientist. Similarly, a solid understanding of Linear Algebra plays a crucial role in performing computational calculations using certain algorithms on data.

6 Data Visualization

According to Randy Krum in his book “Cool Infographics,” data visualization is a visual representation of numbers or quantities, such as bar charts or line graphs. The rise and fall of the graph illustrate the intended numerical values.

7 Software Engineering

Software Engineering is a field of Computer Science that focuses on the development and construction of computer software systems and applications. The ability to apply software engineering principles is essential for building a system that can streamline the operations of a company.

8 Think like a profesional Data Scientist

To become a Data Scientist, the chosen candidate for a company should think like a Data Scientist. Thinking like a Data Scientist means being a problem solver who uses methods and reasoning to solve company problems.

Data Science is essential for a company to predict many things in the future related to its products or services. It is also a tool to find solutions and value propositions in line with the changing times and technologies. This was also revealed by Achmad Zaky, the founder and CEO of Bukalapak.com, during the Hypergrowth Through Data Science event in Jakarta 2017. He emphasized that the Data Scientist profession plays an important role in finding and translating useful data for the company.

The power of Data Science is so great that even Harvard Business Review published an article titled “Data Scientist: The Sexiest Job of the 21st Century”. This is not without basis. In 2011, the McKinsey Global Institute predicted a shortage of nearly 200,000 data scientists by 2018.

Here are some interesting facts about Data Scientists:

• The McKinsey Global Institute predicted that by 2018, “the United States could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts capable of analyzing data to make effective decisions”.
• In a survey conducted by Robert Half Technology of 1,400 Chief Innovation Officers (CIOs) in the United States, “53% of respondents stated that their companies lacked the staff who could access data and generate insights from it.” This means that Data Scientists are in high demand.
• According to Techinasia, an online transportation provider in Indonesia, an experienced Data Scientist with 1-4 years of experience is valued at up to IDR 30 million per month. Of course, this salary can be adjusted based on the qualifications and experience of the Data Scientist.
• Jakarta is the city with the highest demand for data scientists due to the large number of developing companies in the city. The emergence of many startups, such as Bukalapak, Salestock, Traveloka, Blibli, Kudo, and others, is also a golden opportunity for Data Scientists to develop their careers in this field. Unfortunately, the shortage of Data Scientists is still a problem for some companies.
• The job satisfaction rate for Data Scientists is very high, at 4.4 out of 5, which means that those who pursue this career are very satisfied with their work.

Sources

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