The Data Analyst Certification is a comprehensive program designed to equip participants with essential skills in data analysis, business intelligence, as well as data processing and integration. Through intensive learning sessions, participants will gain a deep understanding of how to collect, clean, validate, and structure data professionally.

Program Duration

• Training: 3 days (4 hours/day) = 12 hours
• Exam Preparation: 1 day
• Certification Exam: 1 day

Who Can Join This Program?

• Final-year D4/S1 students (8th semester) aiming to start a career as a Data Analyst
• Graduates of data analytics certification programs or relevant data science training
• Professionals with at least 1 year of experience in the data field

Required Documents

• A photocopy of your National ID
• Four (4) passport-sized photos (3×4 cm)

Benefits of the Data Analyst Certification

• Industry-Recognized Certification – Earn an official BNSP certificate to enhance your professional credibility
• Career Opportunities – Gain a competitive edge in the job market for roles in data analysis and business intelligence
• Practical Skills – Master key skills in data collection, validation, analysis, and integration

• Data Collection
• Data Review
• Data Validation
• Defining Data Objects
• Data Cleaning
• Data Structuring
• Data Utilization

This program ensures that participants not only understand theoretical concepts but also gain practical experience through case studies and a final project that can be added to their professional portfolio.

Required Documents

• A photocopy of your National ID (KTP)
• Four (4) passport-sized photos (3×4 cm)

To ensure an effective learning process, participants are expected to:

• Be from any educational background (not limited to IT majors)
• Submit a CV and a portfolio related to the data field
• For those without experience, the training certificate obtained from this program can serve as a supporting document for the BNSP certification process
• Have a laptop with at least 8GB RAM to ensure smooth practice using SQL, Python, and other supporting tools