My full path from SIT103 to an analyst internship, for anyone in year 2 Data Science who feels behind. In SIT103 I treated every SQL lab like a mini portfolio piece: I saved the queries in a GitHub repo with a short README explaining the question each one answers. Boring, but recruiters opened that repo more than anything else I linked. Over the break I did the free Kaggle Learn courses on pandas and data cleaning. They take a weekend each and they fill exactly the gaps SIT191 leaves. Then I picked ONE finished Kaggle competition, a retail sales forecast, and spent three weeks on it. I did not care about the leaderboard. I cared about writing a clear notebook: what the data looked like, what I cleaned and why, which features mattered, and what I would try next. That notebook became the centre of every interview. Two interviewers literally opened it and asked me to walk through a cell. The SQL questions in the technical round were the same joins and window functions I had practised in SIT103. If you only do one thing this trimester: make your SQL labs public and write one honest Kaggle notebook. Depth beats a long list of certificates.
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Make your SIT103 SQL labs a public portfolio, fill the pandas gap with free Kaggle Learn courses, and write one clear notebook for a finished competition. That notebook and those SQL basics carried the analyst interviews.