Fundamentals of Data Science

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Key data science skills

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Machine learning, Statistics, All the above

Answer explanation

The correct choice is 'all the above' because both machine learning and statistics are essential skills in data science. They enable data analysis, model building, and interpretation of results, making them key competencies in the field.

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Common data visualization tools

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Tableau, Excel, Python, All of the above

Answer explanation

Tableau, Excel, and Python are all widely used for data visualization. Tableau is specialized for this purpose, Excel offers charting tools, and Python has libraries like Matplotlib and Seaborn, making 'All of the above' the correct choice.

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Primary purpose of data cleaning

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Enhance data quality

Answer explanation

The primary purpose of data cleaning is to enhance data quality by removing inaccuracies and inconsistencies, ensuring that the data is reliable for analysis. This is crucial for making informed decisions in data science.

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Significance of EDA

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Summarize main characteristics of data

Answer explanation

Exploratory Data Analysis (EDA) is crucial in data science as it helps summarize the main characteristics of data, revealing patterns, trends, and anomalies that inform further analysis and decision-making.

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Programming language for data analysis

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Python

Answer explanation

Python is widely used for data analysis due to its simplicity and the availability of powerful libraries like Pandas and NumPy, making it the preferred choice among data scientists.

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Role of a Data Scientist

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Collect data, Analyze & interpret complex data, Manage databases

Answer explanation

A data scientist's role encompasses collecting data, analyzing and interpreting complex data, and managing databases. Therefore, the correct answer is 'All of the above' as it includes all essential responsibilities.

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Common ML library in Python

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Scikit-learn

Answer explanation

Scikit-learn is a widely used library for machine learning in Python, providing tools for classification, regression, clustering, and more. While NumPy, Pandas, and Matplotlib are essential for data manipulation and visualization, Scikit-learn specializes in ML.

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Purpose of feature engineering

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Create new features from existing data

Answer explanation

Feature engineering is crucial in data science as it involves creating new features from existing data, which can enhance model performance and improve predictive accuracy.

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Programming language for statistical analysis

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R

Answer explanation

Dia should consider R for statistical analysis because it is specifically designed for data analysis and visualization, making it a powerful tool for statisticians and data scientists.

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Main goal of data visualization

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Present data in graphical format

Answer explanation

The main goal of data visualization in data science is to present data in a graphical format, making it easier to understand patterns, trends, and insights, which is essential for effective data analysis.