Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining.
Data science is a “concept to unify statistics, data analysis and their related methods” in order to “understand and analyze actual phenomena” with data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, computer science, and others. Turing award winner Jim Gray imagined data science as a “fourth paradigm” of science (empirical, theoretical, computational and now data-driven)
Data science basics
Data collection is a process of gathering data from various sources. It includes all the steps involved in acquiring the data. Data can be collected from primary or secondary sources. Primary data is collected firsthand from respondents through surveys, interviews, and focus groups. Secondary data is retrieved from previously collected sources such as databases, reports, and other literature.
After collecting the data, it is important to clean it and organize it in a way that makes it easy to analyze. This step is known as data cleaning. Data cleaning involves identifying and removing errors, inconsistencies, and duplicate values from the dataset. Once the data is cleaned, it can be processed and analyzed to extract useful insights.
Data processing is at the heart of data science. It is the process of transforming data into a form that can be used for further analysis. This involves cleaning, organizing, and sometimes even manipulating data to get it into the desired form.
The goal of data processing is to make raw data more accessible and easier to work with. This can be done for a variety of reasons, such as making it easier to run statistical analyses or machine learning algorithms on the data. Sometimes, data processing is also done in order to improve the quality of the data, or to make it more representative of the population as a whole.
Data processing is an essential part of data science, and it is often one of the most time-consuming tasks in the data science workflow. However, it is also one of the most important steps, as it can make all the difference in the accuracy of your results.
Data analysis is at the heart of data science. It is the process of inspecting, cleaning, transforming and modeling data with the goal of discovering useful information, suggesting conclusions and supporting decision making.
Data scientists often use a variety of techniques and tools to perform data analysis, including statistical methods, machine learning, data visualization and data wrangling (the process of cleaning and preparing data for analysis).
Data visualization is the graphical representation of data. It involves producing images that communicate relationships between different pieces of data.
There are a number of different techniques that can be used to visualize data, and the choice of technique will often depend on the type of data being visualized and the purpose of the visualization. Some common techniques include bar charts, line graphs, scatter plots, and histograms.
Data visualization is an important tool for data scientists, as it allows them to quickly communicate complex information in a easy-to-understand format. It can also be used to uncover patterns and insights in data that would be difficult to spot using other methods.
The role of statistics in data science
Statistical methods are a key part of data science. This is because statistics allow us to make inferences from data. In other words, they help us understand patterns in data and make predictions. Statistics also allow us to assess the reliability of our predictions.
Descriptive statistics are a way of summarizing data. They can be used to describe the data set in terms of its mean, median, mode, range, etc. This information can be helpful in understanding the data set and can be used to make predictions about future data sets.
Statistical inference is the process of using data analysis to deduce properties of an underlying distribution of probability. A common use for statistical inference is to calculate a value for a population parameter. This value is usually an estimate based on a statistic calculated from a sample drawn from the population.
Statistical inference can be used to make predictions about future events, estimate unknown quantities, and test hypotheses. It can be used in both scientific and business applications. For example, it can be used to determine whether a new product is likely to be successful, or to diagnose a medical condition.
There are two main types of statistical inference: inductive inference and deductive inference. Inductive inference uses data to make predictions about future events. Deductive inference uses data to test hypotheses about the relationships between variables.
Inductive inference is more common in data science, because it is typically used to make predictions about future events. For example, if you have data on the heights of 100 people, you could use inductive inference to predict the height of 101st person. However, if you have data on the heights of 100 people and you want to test the hypothesis that taller people are more likely to be successful in business, you would use deductive inference.
The role of machine learning in data science
Machine learning is a subfield of artificial intelligence (AI). It is a process of teaching computers to learn from data, without being explicitly programmed. Machine learning is the backbone of data science because it allows us to make predictions about data.
Supervised learning is the process of teaching a computer to perform a task by providing it with training data. This training data consists of a set of input values and the corresponding desired output values. The goal of supervised learning is to build a model that can take new input values and predict the desired output values accurately.
Supervised learning algorithms can be divided into two main groups: regression algorithms and classification algorithms.
Regression algorithms are used when the output value is a continuous value, such as predicting the price of a stock based on historical data. Classification algorithms are used when the output value is a discrete value, such as deciding whether an email is spam or not.
There are many different supervised learning algorithms, each with its own advantages and disadvantages. Some of the most popular supervised learning algorithms include support vector machines, decision trees, and neural networks.
Unsupervised learning is a branch of machine learning that deals with data that is not labeled and therefore not attributed to any specific target. This type of data can be in the form of images, text, or even numbers. The goal of unsupervised learning is to find hidden patterns and relationships in data in order to create meaningful groups or clusters.
Some common examples of unsupervised learning algorithms are k-means clustering and hierarchical clustering. These algorithms are used to group data points that are similar to each other and to determine the relationships between them. Additionally, unsupervised learning can be used for Dimentionality Reduction which is the process of reducing the number of features in a dataset while still retaining as much information as possible.
Unsupervised learning is an important part of data science because it can be used to discover hidden patterns and relationships that can be difficult to find using other methods. Additionally, it can be used to reduce the dimensionality of a dataset, which can make it easier to work with and understand.
The role of data mining in data science
Data mining is the process of extracting valuable information from large data sets. It is an essential part of data science, which is the process of extracting knowledge from data. Data mining can be used to find trends, patterns, and correlations, and to make predictions about future events.
Association rule mining
Association rule mining is a technique for finding patterns in data. It is commonly used in market basket analysis, where the goal is to find combination of items that are frequently bought together. For example, if you know that people who buy bread also tend to buy butter, you can use this information to make sure that these items are always stocked together.
Association rule mining is a type of unsupervised learning, which means that it does not require any labeled data. This makes it very powerful, as it can be used on data sets that would be too large to label by hand. However, it also means that the results can be less accurate than with supervised learning methods.
The most common algorithm for association rule mining is the Apriori algorithm. This algorithm works by first finding all combinations of items that occur together in at least a certain number of transactions. These are called ‘frequent itemsets’. Once the frequent itemsets have been found, the algorithm looks for rules that say that if one item is present in a transaction, then another item is also likely to be present. These rules are called ‘association rules’.
The Apriori algorithm has two parameters:
- support: This is the minimum number of transactions that an itemset must occur in order to be considered frequent.
- confidence: This is the minimum probability that an association rule must have in order to be considered valid.
You can think of support as the ‘popularity’ of an itemset, and confidence as the ‘reliability’ of an association rule. Higher values for these parameters will result in fewer patterns being found (since they will need to be both more frequent and more reliable). Lower values will result in more patterns being found (since they will only need to meet one of these criteria).
Association rule mining is a very versatile technique and can be used for a variety of different purposes. For example, it has been used to help store owners stock their shelves more effectively, to recommend movies to viewers based on what other movies they have watched, and even to detect fraud
Classification and prediction
There are two main types of data mining tasks: classification and prediction.
Classification is used to predict a discrete value, such as whether an email is spam or not. Prediction is used to predict a continuous value, such as the amount of rainfall in a given location.
Both types of tasks can be performed using a variety of algorithms, including decision trees, support vector machines, and neural networks.
Data science is the backbone of data-driven decision making. By understanding the relationships between data sets, data scientists can uncover trends and insights that would otherwise be hidden. Data science is essential for businesses to make informed decisions about where to allocate resources and make strategic decisions.
Despite its importance, data science is still an emerging field and there is no one-size-fits-all approach to data science. Data scientists need to be able to identify the appropriate methods and tools for each specific problem. In addition, data scientists must be able to communicate their findings to non-technical stakeholders.