Data science is a vast field encompassing various techniques and methods that extract information and help make sense of mountains of data. Moreover, data-driven decisions can deliver immense business value. Therefore, Data science frameworks have become the holy grail of modern technological businesses, broadly charting out 7 steps to glean meaningful insights. These include: Ask, Acquire, Assimilate, Analyze, Answer, Advise, and Act. Here’s an overview of each of these steps and some of the important concepts related to data science.
Data Science Frameworks: Steps
1. Asking Questions: The Starting point of data science frameworks
Like any conventional scientific study, Data science also begins with a series of questions. Data scientists are curious individuals with critical thinking abilities who question the existing assumptions and systems. Data enables them to validate their concerns and find new answers. So, it is this inquisitive thinking that kick-starts the process of taking evidence-based actions.
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2. Acquisition: Collecting the required data
After asking questions, data scientists have to collect the required data from various sources, and further assimilate it to make it useful. They deploy processes like Feature Engineering to determine the inputs that will support the algorithms of data mining, machine learning, and pattern recognition. Once the features are decided, data can be downloaded from an open-source or acquired by creating a framework to record or measure data.
3. Assimilation: Transforming the collected data
Then, the collected data has to be cleaned for practical use. Usually, it involves managing missing and incorrect values and dealing with potential outliers. Poor data cannot give good results, no matter how robust the data modeling is. It is vital to clean the data as computers follow a logical concept of “Garbage In, Garbage Out”. They do process even the unintended and nonsensical inputs to produce undesirable and absurd outputs.
Different forms of data
Data may come in structured or unstructured formats. Structured data is ordinarily in the form of discrete variables or categorical data, having a finite number of possibilities (for example, gender) or continuous variables, including numeric data such as integers or real numbers (for example, salary and temperature). Another special case can be that of binary variables possessing only two values, like Yes/No and True/False.
Sometimes, data scientists may want to anonymize numeric data or convert it into discrete variables to synchronize it with algorithms. For example, numerical temperatures may be converted into categorical variables like hot, medium, and cold. This is called ‘binning’. Another process called ‘encoding’ can be used to convert categorical data into numerics.
4. Analysis: Conducting data mining
Once the required data has been acquired and assimilated, the process of knowledge discovery begins. Data analysis involves functions like Data Mining and Exploratory Data Analysis (EDA). Analyzing is one of the most essential steps of data science frameworks.
Data mining is the intersection of statistics, artificial intelligence, machine learning, and database systems. It involves finding patterns in large datasets and structuring and summarizing pre-existing data into useful information. Data mining is not the same as information retrieval (searching the web or looking up names in a phonebook, etc.) Instead, it is a systematic process covering various techniques that connect the dots between data points.
Exploratory data analysis (EDA)
EDA is the process of describing and representing the data using summary statistics and visualization techniques. Before building any model, it is important to conduct such analysis to understand the data fully. Some of the basic types of exploratory analysis include Association, Clustering, Regression, and Classification. Let us learn about them one by one.
Association means identifying which items are related. For example, in a dataset of supermarket transactions, there could be certain products that are purchased together. A common association could be that of bread and butter. This information could be used for making production decisions, boosting sales volumes through ‘combo’ offers, etc.
Clustering involves segmenting the data into natural groups. The algorithm organizes the data and determines cluster centers based on specific criteria, such as studying hours and class grades. For example, a class may be divided into natural groupings or clusters, namely Shirkers (students who do not study for long and get low grades), Keen Learners (those who devote long hours to study and secure high grades), and Masterminds (those who get high grades despite not studying for long hours).
Regression is done to find out the strength of the correlation between the two variables, also known as a predictive causality analysis. It comprises conducting a numeric prediction by fitting a line (y=mx+b) or curve to the dataset. The regression line will also help in detecting outliers – the data points that deviate from all other observations. The reason could be incorrect input of data or a separate mechanism altogether.
In the classroom example, some students in the ‘Mastermind’ group may have prior background in the subject or may have entered wrong study hours and grades in the survey. Outliers are important to identify problems with the data and the possible areas of improvement.
Classification means assigning a class or label to new data for a given set of features and attributes. Specific rules are generated from past data to enable the same. A Decision Tree is a common type of classification method. It can predict whether the student is a Shirker, Keen Learner or Mastermind based on exam grades and study hours. For instance, a student who has studied less than 3 hours and scored 75% could be labeled as a Shirker.
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5. Answering Questions: Designing data models
Data science frameworks are incomplete without building models that enhance the decision-making process. Modeling helps in representing the relationships between the data points for storing in the database. Dealing with data in a real business environment can be more chaotic than intuitive. So, creating a proper model is of utmost importance. Moreover, the model should be evaluated, fine-tuned, and updated from time to time to achieve the desired level of performance.
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6. Advice: Suggesting alternative decisions
The next step is to use the insights gained from the data model to give advice. This means that a data scientist’s role goes beyond crunching numbers and analyzing the data. A large part of the job is to provide actionable suggestions to the management about what could be to improved profitability and then deliver business value. Advising includes the application of techniques like optimization, simulation, decision-making under uncertainty, project economics, etc.
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7. Action: Choosing the desired steps
After evaluating the suggestions in light of the business situation and preferences, the management may select a particular action or a set of actions to be implemented. Business risk can be minimized to a great extent by decisions that are backed by data science.
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Data science has wide-ranging applications in today’s technology-led world. The above outline of data science frameworks will serve as a road map for applying data science to your business!
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Is NumPy considered a framework?
The NumPy package in Python is the backbone of scientific computing. Yes, NumPy is a Python framework and module for scientific computing. It comes with a high-performance multidimensional array object and facilities for manipulating it. NumPy is a powerful N-dimensional array object for Python that implements linear algebra.
In data science, what is unsupervised binning?
Binning or discretization converts a continuous or numerical variable into a categorical characteristic. Unsupervised binning is a sort of binning in which a numerical or continuous variable is converted into categorical bins without the intended class label being taken into consideration.
How are classification and regression algorithms in data science different from each other?
Our learning method trains a function to translate inputs to outputs in classification tasks, with the output value being a discrete class label. Regression issues, on the other hand, address the mapping of inputs to outputs where the output is a continuous real number. Some algorithms are designed specifically for regression-style issues, such as Linear Regression models, while others, such as Logistic Regression, are designed for classification jobs. Weather prediction, house price prediction, and other regression issues may be solved using regression algorithms. Classification algorithms may be used to address problems like identifying spam emails, speech recognition, and cancer cell identification, among others.