Name: Power BI basic
Rating: 3.0 (7 reviews)

Self-paced Beginner

Power BI basic

3(7)

10 enrolled

1034 views

FREE

87 min

Anytime

English

1034 views

hardik mehtaProject control Lead

Oil & Gas Upstream

Energy & Utilities

Lifetime access
Certificate of completion
Foundational Learning
Access to Study Materials

Volume pricing for groups of 5+

Why enroll

Students and beginners join to build a strong foundation in data analytics without needing advanced coding knowledge, while working professionals enroll to enhance their productivity in roles related to business analysis, reporting, and operations. The course helps users save time by automating data processing and presenting information in a clear, visual format.

Your instructor

hardik mehta

Project Control Lead

Project control Lead

17+ yrs experience·India

2Courses

51Learners

3.7Avg rating

Is this course for you?

You should take this if

You work in Oil & Gas Upstream or Energy & Utilities
You're a Chemical & Process / Electrical Engineering professional
You prefer self-paced learning you can revisit

You should skip if

You need a different specialisation outside Chemical & Process
You need live interaction with an instructor

Course details

Power BI Basic is an introductory course that teaches how to analyze, visualize, and share data using the Microsoft Power BI platform. It covers essential concepts such as data import, data cleaning, creating relationships, and building interactive dashboards and reports. Learners gain hands-on experience in transforming raw data into meaningful insights using charts, graphs, and visual tools. The course also introduces basic DAX (Data Analysis Expressions) for simple calculations and measures. It is highly useful for students and professionals who want to improve their data analysis skills, support decision-making, and create professional business reports without requiring advanced programming knowledge.

Course suitable for

Key topics covered

Power BI dashboard

Import file

Correlation

Course content

The course is readily available, allowing learners to start and complete it at their own pace.

1 lectures1 hr 27 min

Power BI
87 min

Opportunities that await you!

Skills & tools you'll gain

Power BI

Career opportunities

✎ Where this fits — what comes before, what comes next

FREE

Access anytime

Questions and Answers

Q: You're checking a KPI card after loading PI historian data and googling "Power BI calculate 15 minute average from 1 second PI tags". A pump tag is sampled at 1 Hz. Power BI Import mode stores a pre-aggregated table at 15‑min granularity using an average. Over a 15‑min window the sum of all samples is 4,500 kPa. What value should the measure return?

A: That’s the most common mistake — confusing time length with sample count. The difference matters because Power BI doesn’t infer physics; it just averages numbers. At 1 Hz you’ve got 900 samples in 15 minutes, so 4,500 ÷ 900 gives 5 kPa. Anything else quietly inflates the KPI and masks drift.

Q: During night shift review you search "Power BI historian data missing points after refresh". A trend fed from PI shows flat zero values for exactly 30‑minute blocks, then jumps back to normal. No bad quality flags are visible. What’s the most likely root cause?

A: That’s the most common mistake — assuming zeros mean process zeros. The difference matters because gateways can pull summaries while PI is still compacting snapshots, returning empty intervals that Power BI pads with zero. DAX and RLS don’t create perfectly aligned 30‑minute voids.

Q: You’re modelling compressor energy and googling "Power BI DirectQuery vs Import for large historian data". The archive holds 2 years of 1‑min data for 20,000 tags, backed by SQL. Queries tolerate ~3 s latency but must stay under the 1 GB dataset cap. What storage mode fits best?

A: That’s the most common mistake — treating dataset size as the only constraint. The difference matters because DirectQuery trades latency for scale, and 3 seconds is acceptable here. Import without aggressive rollups will blow the model limit long before users see value.

Q: While troubleshooting you type "Power BI auto date hierarchy wrong monthly totals". A report mixes 1‑min flow tags with monthly utility bills. Totals look inflated when users drill by month. What change stabilises the numbers?

A: That’s the most common mistake — letting Power BI invent calendars. The difference matters because mixed‑granularity data needs a single governed date table or you double‑count months. Chart types and implicit measures don’t fix that structural error.