Database Administrator vs Data Engineer

Data Engineer is responsible for designing and managing the database infrastructure.

Data Scientist is responsible for developing statistical and machine learning models.

One of the responsibilities of a DBA is to acquire and integrate data from various sources.

Data Engineer is responsible for developing ETL processes.

Data Scientist is responsible for ensuring data security in the database infrastructure.

Collaborating with other teams to ensure the success of the analytics project is a common responsibility shared by all three roles: DBA, Data Engineer, and Data Scientist.

In Unsupervised Type of machine learning, there is a label column used to classify or predict.

Supervised machine learning algorithms are mainly used for discovering patterns in data.

Binary Classification is a method used under Unsupervised Type of machine learning.

NCI in machine learning stands for Numerical and Categorical Input.

Rule Base in decision trees creates rules for classifying data.

Unsupervised Type of machine learning focuses on predicting outcomes based on labeled data.

Descriptive analytics focuses on characterizing data by analyzing the central tension, dispersion, and shape.

Predictive analytics involves using exploratory data analysis (EDA) in SPSS.

Operationalize phase involves delivering final reports, briefings, code, and technical documents.

Supervised learning is a type of predictive analytics.

Prescriptive analytics focuses on characterizing data by analyzing central tendency, dispersion, and shape.

In the communicate results phase, the key findings are identified in relation to the analytics challenge and the business problem.

Decision trees can handle only numerical inputs, not categorical inputs.

Decision trees assume linearity between predictor variables and the label.

Small variations in training data do not affect decision trees.

Having many irrelevant variables in the dataset makes decision trees a good choice.

Decision trees are robust when dealing with redundant or correlated variables.

Overfitting is not a concern when using decision trees.

Increasing the number of epochs to an infinite number makes sense for improving accuracy.

SVM is always better than ANN when dealing with linearly separable data.

Linear approaches are more effective than non-linear approaches in separating data points.

Kernel methods transform training data into lower dimensional spaces.

Ensemble Classification techniques can provide worse results compared to a decision tree model.

An orchestra is comparable to a soloist in terms of generating sound and music quality.