# @woman.engineer on Instagram

- **Type:** Video
- **Original URL:** https://www.instagram.com/p/DVRV-F7COQD
- **Gondola URL:** https://gondola.cc/posts/61845455-womanengineer-instagram
- **Thumbnail:** https://img.gondola.cc/tr:w-,h-,fo-auto/postThumbnails/cad9062405.jpg
- **Posted:** 2026-02-27T18:17:30.000+00:00
- **Account Owner:** Zeynep Küçük  Woman Engineer (@woman.engineer) — https://gondola.cc/woman.engineer

## Caption

📍How to prepare for Data Scientist role in 2026 🚀

CORE SKILLS YOU MUST MASTER: Programming You must be fluent in:

● Python

● NumPy

● Pandas

● Scikit-learn

Writing clean, readable, bug free code

Data transformations without IDE help

Expect:

● Data cleaning

● Feature extraction

● Aggregations

● Writing logic heavy code

SQL

Almost every Data Science role tests SQL. You should be comfortable with:

● Joins - inner, left, self

● Window functions

● Grouping & aggregations

● Subqueries

● Handling NULLs

Statistics & Probability:

● Probability distributions

● Hypothesis testing

● Confidence intervals

● A/B testing

● Correlation vs causation

● Sampling bias

Machine Learning Fundamentals. You must know:

● Supervised vs Unsupervised learning

● Regression & Classification

● Bias Variance tradeoff

● Overfitting / Underfitting

Evaluation metrics:

● Accuracy

● Precision / Recall

● F1-score

● ROC-AUC

● RMSE

FEATURE ENGINEERING & DATA UNDERSTANDING:

● This is where strong candidates stand out.

● Handling missing data

● Encoding categorical variables

● Feature scaling

● Outlier treatment

CORE SKILLS YOU MUST MASTER: Programming You must be fluent in:

● Python

● NumPy

● Pandas

● Scikit-learn

Writing clean, readable, bug free code

Data transformations without IDE help

Expect:

● Data cleaning

● Feature extraction

● Aggregations

● Writing logic heavy code

SQL

Almost every Data Science role tests SQL. You should be comfortable with:

● Joins - inner, left, self

● Window functions

● Grouping & aggregations

● Subqueries

● Handling NULLs

Statistics & Probability:

● Probability distributions

● Hypothesis testing

● Confidence intervals

● A/B testing

● Correlation vs causation

● Sampling bias

Machine Learning Fundamentals. You must know:

● Supervised vs Unsupervised learning

● Regression & Classification

● Bias Variance tradeoff

● Overfitting / Underfitting

Evaluation metrics:

● Accuracy

● Precision / Recall

● F1-score

● ROC-AUC

● RMSE

+++ for more look at the comment 
#datascientist  #aiengineer #softwareengineer #datascience #dataengineer

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## Tags

dataengineer, aiengineer, softwareengineer, datascientist, datascience

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