Our 1000+ Data Science questions and answers focuses on all areas of Data Science subject covering 100+ topics. These topics are chosen from a collection of most authoritative and best reference books on Data Science. One should spend 1 hour daily for 2-3 months to learn and assimilate this which will prepare anyone easily towards Data Science interviews, online tests, examinations and certifications.
– 1000+ Multiple Choice Questions & Answers in Data Science with explanations
– Every MCQ set focuses on a specific topic in Data Science subject
Who should Practice these Data Science Questions?
– Anyone wishing to sharpen their knowledge of Data Science subject
– Anyone preparing for aptitude test in Data Science subject
– Anyone preparing for interviews (campus/off-campus interviews, walk-in interview and company interviews)
– Anyone preparing for entrance examinations and other competitive examinations
– All – Experienced, Freshers and Students
Here’s list of Questions & Answers on Data Science subject covering 100+ topics:
1. Data Science Basics and Data Scientist Toolbox
The section contains questions and answers on basics of data sciences and toolbox, workflow of CLI and git, big data analysis and experimental design.
Basics of Data Science
CLI and Git Workflow-1
CLI and Git Workflow-2
Types of Questions-1
Types of Questions-2
Analysis and Experimental Design
2. Data Analysis with Python
The section contains questions and answers on pandas, time deltas, python plotting, data structures and computational tools.
Plotting in Python
Pandas Data Structure
3. Getting Data
The section contains questions and answers on raw data, processed data, tidy data, web reading, API, data summarization and merging, regular expressions and text variables.
Raw and Processed Data
Reading from Web and APIs-1
Reading from Web and APIs-2
Summarizing and Merging Data
Regular Expressions and Text Variables
4. Data Analysis and Research
The section contains questions and answers on graphical devices and plotting systems, basics of reproducible research, clustering, exploratory graphs and basics of literate statistical programming.
Introduction to Reproducible Research
Literate Statistical Programming-1
Literate Statistical Programming-2
5. Statistical Inference and Regression Models
The section contains questions and answers on probability and statistics, basics of statistical inference, regression models, distributions and likelihood, binary and count outcomes and residual variations.
Introduction to Statistical Inference
Probability and Statistics
Statistical Inference Concepts
Introduction to Regression Models
Residual Variation and Multivariate
Binary and Count Outcomes
6. Machine Learning
The section contains questions and answers on caret, prediction with motivation, regression and model and cross validation.
Predicting with Regression
Model Based Prediction
7. Developing Data Products and Working with NumPy
The section contains questions and answers on shiny, slidify, googleVis and numPy.
Wish you the best in your endeavor to learn and master Data Science!
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