Siksha Sarovar

Siksha Sarovar (sikshasarovar.com) is a free educational web application that helps students in India learn programming and prepare for academic and competitive exams. The platform offers structured coding courses (C, C++, Python, Java, HTML, CSS, PHP, Power BI, AI, Machine Learning, Data Science), complete university curriculum notes for BCA/MCA students with previous year question papers, Class 10 and Class 12 CBSE/HBSE school notes, and dedicated preparation material for SSC, UPSC, Banking, Railway and other government exams. Browsing the site is completely free and requires no account. Users may optionally sign in with Google solely to save their learning progress, quiz scores and personal preferences across devices.

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Siksha Sarovar is a free e-learning platform for coding courses, BCA university notes and competitive exam preparation. Optional Google sign-in saves your learning progress across devices.

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MID Term Important Questions

Lesson 30 of 32 in the free Data Visualisation and Analytics notes on Siksha Sarovar, written by Rohit Jangra.

MID Term Important Questions

Section A – Short Answer Questions

  1. Define Data Analytics.
  2. What do you mean by Basic Nomenclature in Analytics?
  3. Explain the Analytics Process Model.
  4. Write the role of analytics in different job profiles.
  5. Define Data Visualization.
  6. What is Hypothesis Testing?
  7. Define Null Hypothesis and Alternative Hypothesis.
  8. What is a p-value?
  9. Explain Confidence Interval.
  10. Define Correlation.
  11. What is Correlation Coefficient?
  12. What is Simpson’s Paradox?
  13. Define ANOVA.
  14. What is the difference between Correlation and Causation?
  15. What are the applications of Data Analytics?

Section B – Medium Answer Questions

  1. Explain Basic Nomenclature in Analytics with examples.
  2. Describe the Analytics Process Model in detail.
  3. Explain the importance of analytics in business decision making.
  4. Discuss the role of analytics in different job profiles.
  5. Explain the concept of Statistical Hypothesis Testing.
  6. Describe the steps involved in Hypothesis Testing.
  7. Explain p-values and their significance in statistical analysis.
  8. Write a detailed note on Confidence Intervals.
  9. Explain Correlation with suitable examples.
  10. Discuss Simpson’s Paradox with an example.
  11. Explain the concept of Correlation and Causation.
  12. Describe Correlation Statistics used in analytics.
  13. Explain ANOVA and its importance in data analysis.
  14. Compare Correlation and ANOVA.
  15. Explain the limitations of correlation.

Section C – Long Answer Questions

  1. Explain the Analytics Process Model and its stages in detail.
  2. Discuss the role of analytics in different job profiles and industries.
  3. Explain Statistical Hypothesis Testing with its steps and examples.
  4. Discuss the concept of p-values and confidence intervals in statistical analysis.
  5. Explain Correlation and its different types with examples.
  6. Describe Simpson’s Paradox and its implications in data analysis.
  7. Explain the difference between correlation and causation with examples.
  8. Discuss Correlation Statistics and their role in analytics.
  9. Explain ANOVA with suitable examples.
  10. Compare Hypothesis Testing and ANOVA in data analytics.