S0E1 | Pengantar dunia Data Science, role, dan gajinya

Summary

This session introduces the world of data science, focusing on its multidisciplinary nature and its role in extracting insights from data. It explores key phases such as analysis, modeling, and testing, and highlights the differences between roles like business analysts, data analysts, and data scientists. The presentation also discusses the growing importance of machine learning, the impact of new technologies, and career opportunities in the field, referencing salary data from platforms like Glassdoor. The next session will dive deeper into machine learning.

Takeaways

• 😀 Data Science is a multidisciplinary field that extracts insights from data using a variety of techniques and technologies.
• 😀 The process of Data Science involves three main phases: Analysis (diagnosis of past data), Modeling (creating predictive models), and Testing (improving predictions).
• 😀 Business Analysts focus on understanding business questions and gathering the right data, while Data Analysts analyze the data and present actionable insights.
• 😀 Data Analysts also need storytelling skills to effectively present their findings to business decision-makers.
• 😀 Data Engineers build the infrastructure (data pipelines, architecture) needed to collect and process data for analysis.
• 😀 Database Administrators manage data access, ensuring proper data security, backup, and accessibility for analysts.
• 😀 Machine Learning (ML) Engineers or AI Engineers work with Data Scientists to turn predictive models into automated applications and systems.
• 😀 Data Scientists use statistical and mathematical models to predict future outcomes based on historical data.
• 😀 Software Engineers involved in Data Science need to integrate machine learning models into applications, ensuring that they are efficient and scalable.
• 😀 The demand for Data Scientists is growing rapidly, with many job opportunities available in this field, driven by increasing reliance on data-driven decisions.
• 😀 Salary data indicates that roles in Data Science, like Data Analysts and Data Scientists, command competitive salaries, reflecting the high demand for these positions.

Q & A

What is Data Science, and what are its core objectives?

Data Science is a multidisciplinary field that combines various scientific methods, algorithms, and systems to extract insights and knowledge from structured and unstructured data. Its core objective is to extract useful information from data to support decision-making, predictions, and other actions.

How has technology impacted Data Science?

Technology has revolutionized Data Science by introducing new tools, technologies, and algorithms, especially in fields like machine learning. These advances have made it possible to process and analyze large datasets faster and more accurately, which has fundamentally changed how data science is applied.

What are the three phases of Data Science, and what do they involve?

The three phases of Data Science are: 1) Analysis - which involves analyzing past data to diagnose and identify problems; 2) Modeling - where statistical or machine learning models are created to make predictions; and 3) Testing - where models are evaluated, refined, and improved based on their accuracy in making predictions.

What is the difference between a Business Analyst and a Data Analyst?

A Business Analyst focuses on understanding business problems and generating business-related questions to guide decision-making, often without focusing on data specifics. A Data Analyst, on the other hand, works with data to answer these business questions, analyzing historical data and presenting the insights to decision-makers.

What role does a Data Engineer play in the Data Science process?

A Data Engineer is responsible for designing, building, and maintaining the infrastructure (data pipelines) that allows data to flow from various sources into systems where it can be analyzed. They ensure data is accessible, clean, and organized for analysis by Data Scientists and Analysts.

How does Data Science relate to Machine Learning?

Data Science and Machine Learning are closely related. Data Science involves extracting insights from data, and one of the most powerful tools for doing this is Machine Learning. Machine Learning models can make predictions and automate decision-making, making them an integral part of modern data science.

What are the responsibilities of a Data Scientist in the Data Science lifecycle?

A Data Scientist is responsible for building predictive models using statistical and machine learning techniques. They work with data, clean it, and train models to make predictions based on the available data. Their role bridges the gap between raw data and actionable insights.

Why is the role of a Machine Learning Engineer crucial in implementing Data Science models?

A Machine Learning Engineer is responsible for deploying and maintaining machine learning models in production. They work on the technical side, making sure that models are integrated into applications or systems and can perform predictions in real-time, ensuring efficiency and scalability.

What are the salary ranges for roles in Data Science and related fields in Indonesia?

Based on data from Glassdoor, the average salary for a Business Analyst in Indonesia ranges from IDR 6.4 million to IDR 18 million. Data Analysts earn between IDR 8.4 million and IDR 10 million, while Data Scientists make approximately IDR 12-13 million per month. Software Engineers and DevOps Engineers earn around IDR 11 million per month.

What is the expected growth of job opportunities in Data Science in the near future?

Job opportunities in Data Science are growing rapidly. According to reports from LinkedIn and the World Economic Forum, roles like Data Scientists and Machine Learning Engineers are expected to dominate the tech job market. These fields are projected to grow quickly, though competition is also intensifying as the demand for these skills increases.