About Data Science & Analytics

What is Data Science & Analytics?

These assessments evaluate analytical proficiency, featuring statistics, database queries (SQL), machine learning algorithms, and data programming languages like Python and R.

Assessment Syllabus Areas

  • Mathematics: Linear algebra, calculus, descriptive statistics, and probability distributions.
  • Machine Learning: Supervised/unsupervised algorithms, evaluation metrics, and model tuning.
  • Data Wrangling: Query optimization, feature engineering, and visualization principles.

Available Exams for Data Science & Analytics

Select an exam test series below to access subject-wise mock tests and solved previous year papers.

No test series available for this category.

Frequently Asked Questions

Yes, solid command over SQL is necessary, and intermediate programming in Python or R is expected for data science.

Core subjects include hypothesis testing, confidence intervals, regression analysis, and Bayes' theorem.

Supervised learning (Linear/Logistic regression, decision trees, SVM) and unsupervised learning (K-Means, PCA).

Evaluating competence in joins, subqueries, CTEs, aggregation functions, and window operations.

Yes, basic neural network layouts, activation functions, and training parameters are tested for advanced profiles.
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