Data Science Notes _ Part 1

Analytics is essentially the application of logical and computational reasoning to the component parts obtained in an analysis: This definition is the most accurate as it captures the essence of what analytics is about. Analytics involves breaking down complex data into smaller parts and using logical and computational methods to analyze those parts in order to gain insights, identify patterns, and make informed decisions.

Dashboards are primarily used for data visualization, which involves presenting data in a clear and easily understandable format, often through graphs, charts, and other visual aids. The main goal is to enable stakeholders to monitor key metrics and make informed decisions based on the displayed data. In contrast, the other options, such as sales forecasting, fraud prevention, and client retention, often involve the use of machine learning algorithms to analyze historical data, identify patterns, and make predictions.

Symbolic reasoning involves using logic and rules to make deductions and inferences. It is a traditional approach to artificial intelligence that does not involve machine learning algorithms. Therefore, option A is the correct answer because symbolic reasoning is a traditional approach to artificial intelligence that does not involve machine learning algorithms.

From a data scientist's perspective, the process of solving a task typically begins with obtaining a proper dataset. Data is the foundation for any data-driven analysis or modeling. Without a suitable dataset, it would be challenging to perform any meaningful analysis, develop predictive models, or gain insights. The other options, while important in the overall process, usually come after acquiring the necessary data.

Traditional data science methods involve using statistical and machine learning techniques to analyze and interpret data to gain insights and inform decision-making, including predictive analytics. They focus on modeling relationships between variables and making predictions about future outcomes.?

Big data is characterized by its volume, variety, and velocity. It encompasses a wide range of data types, including text data, integers, and digital image data. When working with big data, it is common to encounter various data formats and structures, making it essential for data professionals to develop the necessary skills and tools to effectively process and analyze such diverse data sources.

Quantification is the process of representing observations or data as numbers or numerical values. It involves assigning values or measurements to different variables or attributes in the dataset, which allows for analysis and modeling.

Regression analysis is a statistical technique used to model and quantify causal relationships between variables. It helps to determine the extent to which changes in one or more independent variables can predict or explain changes in a dependent variable. In business and various other fields, regression analysis is often used for forecasting, understanding relationships between variables, and identifying causal effects, making it the appropriate choice for quantifying causal relationships among the given options.

Factor analysis is a technique used to reduce the dimensionality of a statistical problem by identifying a smaller number of underlying factors that can explain the correlations among a larger set of observed variables. This method helps to simplify complex datasets, making them more manageable and easier to analyze.

Time-series analysis is a statistical technique that involves examining data points collected sequentially over time. In this method, values are plotted against time, which is always represented on the horizontal axis. Time-series analysis is used to identify trends, patterns, and seasonal variations in the data, as well as to forecast future values. The other techniques listed—regression analysis, factor analysis, and cluster analysis—are not specifically associated with plotting values against time.

Cluster analysis is a technique used to group data points with similar characteristics together. This method involves partitioning the data into clusters or groups based on their similarity, making it easier to analyze and understand the relationships between data points. Cluster analysis is the appropriate technique to apply when data needs to be divided into a few groups. Factor analysis is used for dimensionality reduction, while time-series analysis is used for examining data points collected sequentially over time.