Data Analysis Tutoring in Bali: Clean It First Analyse Afterwards

Browse every private tutoring programme in Bali

• 01

  Numbers Stored as Text

  A column that looks numeric and behaves like words, usually from a copied spreadsheet or a thousands separator. Nothing calculates and no error appears; the average simply comes back empty or wrong.

• 02

  Blanks With Different Causes

  An empty cell can mean not asked, refused to answer, or does not apply. Treating all three the same way changes the result, so each is coded separately before anything is run.

• 03

  One Question, Two Answer Formats

  Yes and Y and 1 in the same column, or a scale answered in words by some respondents and numbers by others. Any count on that column is wrong until the forms are reconciled.

• 04

  Duplicated Rows

  The same respondent twice, from a form submitted twice or a merge done twice. Duplicates inflate the sample and shrink the apparent variation, which makes results look stronger than they are.

• 05

  Values Far Outside Range

  An age of 220 or an income with three extra digits. Each one is checked against the original form where possible, and corrected or coded as missing with the reason recorded.

• 06

  Mixed Date Formats

  Day-month and month-day in one column, which silently swaps two numbers for every date before the thirteenth. Dates are the single most common source of errors that survive to publication.

1. Stage 1

   Checking and Cleaning

   Column by column, with every decision written in a log as it is made. The log matters as much as the cleaning: a supervisor or a reviewer will ask why a row was removed.

   • Column by column
   • A written log
   • The original kept untouched

2. Stage 2

   Settling Variable Types

   Deciding what each column actually is: category, order, count, or measurement. Half of all wrong tests come from treating an ordered scale as a measurement without saying so.

   • Category and order
   • Counts and measurements
   • Recoding deliberately

3. Stage 3

   Distribution, Then the Test

   Looking at the shape of the data before choosing anything, then selecting a test that fits that shape and the question. The reason for the choice is written next to it.

   • Shape before test
   • Matching the question
   • Writing the reason

4. Stage 4

   Reading and Presenting the Result

   What the output says, what it does not say, and how to put it in a table a reader can follow. Learners practise stating the limits of their own finding out loud.

   • Reading the output
   • Naming the limits
   • A table readers follow

• Final-Year Students

  Learners in Bali with a dissertation deadline here on the island. The work runs on their own data and their supervisor's expected tool, with the cleaning log built as they go.

• Researchers and Lecturers

  Learners in Bali preparing a paper for review. Weight goes to assumptions and to stating limits, since those are what reviewers question first.

• Staff Who Produce Reports

  Learners in Bali assembling monthly figures. One place for the records comes first, then the summary, then the chart.

• Business Owners

  Learners in Bali with sales records and no reporting habit. Simple comparisons done honestly beat complicated analysis of an unclean file.

• Learners Whose Results Came Back

  Learners in Bali whose analysis was returned by a supervisor or reviewer. The first session reads the comments and locates the actual objection.

• Learners Starting From Nothing

  Learners in Bali who have never run a test. Spreadsheet cleaning comes first, and the statistics follow once the file is trustworthy.

1. 5 minutes

   Copying the Data and Opening the Log

   Work happens on a copy and the original file is never edited. The cleaning log is opened beside it so decisions get written as they are made.

2. 15 minutes

   Checking a Few Columns

   Three or four columns are examined: type, range, blanks, duplicates. Checking the whole file at once produces a list nobody acts on.

3. 20 minutes

   Settling Them and Recording Why

   Each problem is fixed and the reason written in one line. The learner writes it, because they are the one who will be asked to defend it.

4. 15 minutes

   Checking the Effect on the Summary

   The summary figures are recalculated to see what the cleaning moved. A change nobody looked at is a change nobody can explain.

5. 5 minutes

   Setting the Next Columns

   The columns for next session are named, with any question to ask the supervisor written down.

In a 90 or 120 minute session, a full dataset is cleaned and its summary rebuilt within one sitting, which is what learners close to a deadline usually choose.

• The Raw Data Itself

  Exactly as collected, not a tidied version. A cleaned copy hides the decisions someone already made, and those decisions are usually where the problem is.

• Your Research or Work Question

  One sentence saying what you want to know. Without it there is no way to choose between tests, and the session turns into a tour of software menus.

• The Application Your Supervisor Expects

  If a supervisor or an institution requires a particular tool, bring that requirement. Analysis redone in a different tool costs weeks, and the requirement is cheap to check first.

• Any Previous Correction Notes

  Comments from a supervisor or a reviewer, in their own words. They usually name the objection precisely, and that saves a session of guessing.

• Agreement About Personal Data

  If your file holds names, addresses, or health details, bring whatever permission governs it. Identifiers are removed or masked before the data is opened in a session.

If you do not yet have a question, say so during the consultation. The first session is then spent turning what you want to know into one sentence a test can answer.

• ★★★★★

  I was completely lost about how to run my data in SPSS for my dissertation. My Edupola tutor patiently explained everything from entering the data to interpreting a multiple regression. The results and discussion chapters came together.

  Dissertation finished on time

  RD

  Ratna D.

  Final-year management undergraduate · Verified learner

• ★★★★★

  I needed structural equation modelling for my research. My Edupola tutor was very competent, helping from building the model and running it to interpreting the goodness of fit. My paper was accepted.

  Paper accepted by an indexed journal

  AK

  Adi K.

  Researcher at a research institute · Verified learner

• ★★★★★

  Python and R lessons at Edupola helped my career a great deal. I could only use spreadsheets before and now I build dashboards and predictive analyses. My manager was impressed and I was promoted.

  Promoted to senior analyst

  AW

  Ayu W.

  Data analyst at a startup · Verified learner

• ★★★★★

  My thesis needed complicated multivariate analysis. My Edupola tutor helped me choose the right statistical test, process the data in SPSS, and interpret the results.

  Thesis finished before the deadline

  AP

  Arya P.

  Postgraduate health thesis candidate · Verified learner

• ★★★★★

  I had to produce a programme evaluation report from survey data. Edupola taught me how to process questionnaire data in SPSS, from validity and reliability testing through to the analysis.

  Programme evaluation report rated well

  AS

  Ayu S.

  Government department officer · Verified learner

• ★★★★★

  As a young lecturer, I need to improve my data analysis skill for research. The R Programming and Stata lessons at Edupola are very practical. Now I am more productive and have published 3 papers in a year.

  Published 3 journal articles in a year

  DA

  Dwi A.

  Young university lecturer in Bali · Verified learner

1. 01

   Name the Question and the Deadline

   What you want to know, in one sentence, and the date it has to be finished. Those two set the tool and how much cleaning time there is.

2. 02

   Agree the Schedule

   Days, times, session length, and sessions per month are agreed with the admin, following the hours you can give to cleaning columns between sessions.

3. 03

   Matching the Tutor

   The team looks for a tutor whose travel to your location in Bali stays short, and that search runs 1 to 5 working days. The tool matters here, so SPSS, R, Python, and structural models are matched to tutors who work in them.

4. 04

   The First Session

   Your raw data is opened on a copy, a few columns are checked, and the cleaning log is started in your own words.

• 

  Spreadsheet Tutoring in Bali

  Taken by learners who clean and summarise in a spreadsheet before anything else.

• 

  Programming Tutoring in Bali

  Chosen by learners moving to R or Python who need the logic underneath.

• 

  University Tutoring in Bali

  Often added by students whose dissertation needs more than the analysis chapter.