DATA SCIENCE PIPELINE

A data science pipeline is the overall step by step process towards obtaining, cleaning, visualizing,?Modelling, and interpreting data within a business or group.?Data science pipelines are sequences of processing and analysis steps applied to data for a specific purpose.?

Below are various stages of Data Science pipeline: -

• Problem Definition?
• Hypothesis Testing?
• Data Collection and processing?
• Exploratory Data Analysis (EDA) and Feature Engineering?
• Modelling and Prediction?
• Data Visualization?
• Insight Generation and implementation?

Problem Definition?

The problem statement stage is the first and most important step of solving an analytics problem. It can make or break the entire project. When a business approaches a data scientist with a problem they want to solve, they will always define the problem in layman’s terms. This means the problem will not be clear enough, from an analytics point of view, to begin solving it right away. The problem needs to be well framed.?

As the data scientist, you need to think of the problem statement in mathematical terms.?

This is easier said than done, but not impossible.?

• Understand Business Goals & Expectations?
• Translate Business Goals to Data Analysis Goals?
• Frame the Problem Statement?
• Success Metric?

Hypothesis Testing?

A statistical hypothesis, sometimes called confirmatory data analysis, is a hypothesis that is testable on the basis of observing a process that is modeled via a set of random variables. A statistical hypothesis test is a method of statistical inference. The purpose of hypothesis testing is to determine whether there is enough statistical evidence in favor of a certain belief, or hypothesis, about a parameter. Hypothesis testing is used to infer the result of a hypothesis performed on sample data from a larger population. In simple words, we form some assumptions during the problem definition phase and then validate those assumptions statistically using data.?

Steps in Hypothesis testing:?

Step 1: State the hypotheses.?

Step 2:?Set the criteria for a decision.?

Step 3: Compute the test statistic.?

Step 4:?Make a decision?

Data Collection and processing?

Data collection is the process of gathering and measuring data, information or any variables of interest in a standardized and established manner that enables the collector to answer or test hypotheses and evaluate outcomes of the particular collection. It is important to collect information from all the relevant sources to find answers to the research problem, test the hypothesis and evaluate the outcomes.?

Data collection methods can be divided into two categories:?

• Secondary methods of data collection and?
• Primary methods of data collection?

The most commonly used methods for data collections are:?

• Published literature sources,?
• Surveys (email and mail),?
• Interviews (telephone, face-to-face or focus group),?
• observations, documents ,records, and experiments.?

Data processing is more about a series of actions or steps performed on data to verify, organize, transform, integrate, and extract data in an appropriate output form for subsequent use. Methods of processing must be rigorously documented to ensure the utility and integrity of the data.?

?Exploratory Data Analysis (EDA)

In data mining, Exploratory Data Analysis (EDA) is an approach to analyzing datasets to summarize their main characteristics, often with visual methods.?

EDA is used for seeing what the data can tell us before the modeling task. It is not easy to look at a column of numbers or a whole spreadsheet and determine important characteristics of the data. It may be tedious, boring, and/or overwhelming to derive insights by looking at plain numbers. Exploratory data analysis techniques have been devised as an aid in this situation.?

Exploratory data analysis is generally cross-classified in two ways.?

• Each method is either non-graphical or graphical. and?
• Each method is either univariate or multivariate (usually just bivariate).?

Univariate analysis is the simplest form of data analysis, where the data being analyzed consists of only one variable. Since it’s a single variable, it doesn’t deal with causes or relationships. The main purpose of univariate analysis is to describe the data and find patterns that exist within it.?

Few visualizations used for performing univariate analysis are as:

• ?Box Plots
• Histogram

Multivariate data analysis refers to any statistical technique used to analyze data that arises from more than one variable. This models more realistic applications, where each situation, product, or decision involves more than a single variable.?

EDA is a crucial step to take before diving into machine learning or statistical modeling because it provides the context needed to develop an appropriate model for the problem at hand and to correctly interpret its results. EDA is valuable to the data scientist to make certain that the results they produce are valid, correctly interpreted, and applicable to the desired business contexts.

Modelling and Prediction

Predictive modeling is a process that uses data and statistics to predict outcomes with data models. Predictive modeling, also called predictive analytics, is a mathematical process that seeks to predict future events or outcomes by analyzing patterns that are likely to forecast future results. ... As additional data becomes available, the statistical analysis will either be validated or revised?. Machine learning can be used to make predictions about the future. You provide a model with a collection of training instances, fit the model on this data set, and then apply the model to new instances to make predictions.?

Predictive Analytics is considered as a branch of Data Science. It is the practice of using existing data sets to predict future outcomes and trends. It uses advanced data science techniques including data mining and machine learning to forecast future events with a high level of reliability and accuracy.?

Data Visualization

Data visualization refers to the techniques used to communicate data or information by encoding it as visual objects (e.g., points, lines or bars) contained in graphics. The goal is to communicate information clearly and efficiently to users. It is one of the steps in data analysis or data science

Insight Generation and implementation?

There is nothing better than deploying the model in a real-time environment. It helps us to gain analytical insights into the decision-making procedure. You constantly need to update the model with additional features for customer satisfaction.?

To predict business decisions, plan market strategies, and create personalized customer interests, we integrate the machine learning model into the existing production domain.?

By reading the article, we summarize the following points:?

• Understand the purpose of the business analytical problem.?
• Generate hypotheses before looking at data.?
• Collect reliable data from well-known resources.?
• Invest most of the time in data exploration to extract meaningful insights from the data.?
• Choose the signature algorithm to train the model and use test data to evaluate.?

Deploy the model into the production environment so it will be available to users and strategize to make business decisions effectively.?