Data Science

A vector is

1. A single value
2. A graph
3. A list of numbers
4. A file type

Big Data is characterized by

1. Only small datasets
2. Very low speed
3. Manual processing
4. Large volume, velocity, variety

NumPy is mainly used for

1. Web development
2. Audio editing
3. Numerical computing
4. Drawing pictures

Bayes Theorem is related to

1. Probability
2. Algebra
3. Cooking
4. All of the above

KNN stands for

1. k- Nearest Neighbors
2. None of the above
3. Known New Numbers
4. Key Neural Network

Correlation measures

1. Difference between two files
2. The mass of an object
3. Relationship between variables
4. The number of rows

Object recognition is commonly used in

1. Self-driving cars
2. Food delivery
3. Gardening
4. Sewing machines

Which is a type of supervised learning?

1. Clustering
2. Reinforcement feedback
3. Classification
4. Random guessing

Which of the following is a classification algorithm?

1. Linear Regression
2. K-Means
3. Naïve Bayes
4. PCA

Object recognition is related to

1. Identifying objects in images
2. Writing poems
3. Drawing charts
4. Compressing files

Describe the concept of Data Science, its importance, and real-world applications.

Explain in detail the characteristics of Big Data and challenges in handling it.

Discuss the full pipeline of data munging, manipulation, rescaling, and dimensionality reduction.

Explain Python toolkits for Data Science such as NumPy, Matplotlib, and NLTK with examples.

Explain vectors and matrices with examples. How are they used in Data Science?

Discuss probability concepts: independence, conditional probability, random variables, and distribution.

Explain linear regression in detail, including assumptions, cost function, and regularization methods.

Describe the different types of machine learning with real-world examples.

Compare classification and regression with suitable examples.

Discuss any two classification algorithms (e.g., Naive Bayes, KNN, SVM) in detail.

Explain stock market prediction with ML, covering regression, time series, and neural network.

Discuss in detail the role of data science in weather forecasting, including data sources and models used.

What is the difference between data analysis and reporting, and why is this distinction important in data-driven decision making?

What is data visualization, and what are the purposes and differences of bar charts, line charts, and scatterplots?

Real-Time Sentiment Analysis

Variety, Velocity, and Veracity in Big Data

Reinforcement Learning

Object Recognition Systems