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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Basic Introduction to Data Science Pipeline

Pranshu Sharma
Pranshu Sharma Last Updated : 08 Sep, 2022
6 min read

This article was published as a part of the Data Science Blogathon.

Introduction

The Data science pipeline is the procedure and equipment used to compile raw data from many sources, evaluate it, and display the findings in a clear and concise manner. Businesses use the method to get answers to certain business queries and produce insights that can be used for various business-related planning.

Due to the ever-growing complexity and volume of enterprise data, as well as its crucial role in decision-making and long-term planning, organizations are investing in the Data science pipeline-related technologies necessary to extract useful business insights from their data assets in order to use for planning and other business approaches

Data Science Pipeline
Source: Unsplash

What is meant by a data science pipeline?

A data science pipeline is a process collection that transforms raw data into useful solutions to business issues. Pipelines for data science streamline data movement from source to destination, allowing you to make better business decisions.

The data science pipeline is the method and tools for gathering raw data from various sources, analyzing it, and presenting the results comprehensibly. Companies use the method to answer particular business issues and derive actionable insights from real-world data. In simple terms, a data science pipeline is a sequence of operations that converts raw data from diverse sources into a comprehensible format so that it may be stored and analyzed.

Data Science Pipeline

Source: IBM Developer

Why is the data science pipeline noteworthy?

The data science pipeline is the key to extracting insights from ever-larger and more complicated information. Teams must depend on a process that disintegrates datasets and offers meaningful insights in real-time as the amount of data available to enterprises continues to grow.

  • The data science pipeline makes data analysis and handling of large chunks of data easier
  • Smooth management of various tasks like collecting data from several teams, cleansing it, and displaying it in a readily understandable format.
  • It enables you and your team to make data-driven decisions quickly.
  • We can bypass the time-consuming and error-prone procedure of traditional data collection.
  • It allows consumers to explore deeper into data at a more granular level.

Working of a Data Science Pipeline

Having precise queries is critical before pushing raw data through the pipeline. This allows users to concentrate on the relevant facts to gain the necessary insights.

There are various steps in the data science pipeline, including

  1. Obtaining information
    This is where data is collected and processed from internal, external, and third-party sources into a useful format (XML, JSON, .csv, etc.).
  2. Data cleansing
    This is the process’s most time-consuming step. Anomalies in data, such as duplicated parameters, missing values, or pointless data must be cleaned before a data visualization can be created.
    Data cleansing can be classified into two types:
    a) Examining data to look for errors, missing numbers, or entries that have been corrupted.
    b )Cleaning data entails filling in gaps, correcting errors, deleting duplicates, and discarding obsolete records or data.
  3.  Data exploration and modeling
    After the data has been completely cleaned, data visualization tools and charts can be utilized to detect patterns and values. This is where artificial intelligence (AI) techniques come into play. You can detect patterns and apply specific rules to data or models using classification accuracy, confusion matrix, logarithmic loss, etc.
  4.  Data interpretation
    This stage aims to uncover and link insights with your data findings. You can then use charts, dashboards, or reports/presentations to present your results to corporate leaders or coworkers.
  5. Revision of the information
    It’s critical to reassess your model regularly as your business requirements evolve and new data becomes available.

Stages of Data Science Pipeline

The following are the main steps in a data science pipeline:

  1. Data Engineering consists of the collection, cleansing, and preparation
  2. Computer-assisted learning consists of collection model learning and model validation
  3. The result, which consists of collection model deployment and data visualization

However, establishing the business challenges, you need the data to solve, and the data science methodology is the first step in building a data science pipeline. Formulate the questions you need to be answered, and machine learning and other techniques will offer you answers you can use.

STAGES OF Data Science Pipeline

Source: Geeksforgeeks.org

The following are the steps in a data science pipeline:

  •  Data collection includes identifying data sources and extracting data from those sources into formats that may be used.
  • ETL (Extraction, Transformation, and Loading) may be used in data preparation.
  • Machine learning is deployed to detect patterns and apply the rules to data using algorithms, which are subsequently validated on sample data in data modeling and validation of the model.
  • Model deployment, which entails deploying the model to both old and new data
  • Reviewing and upgrading the model in response to shifting business needs

Benefits

Following are the benefits of Data Science Pipelines

  1. The pattern that can be replicated
    Individual pipes are patterns in a larger architecture that may be recycled and reused for new data flows when data processing is viewed as a network of pipelines.
  2. Integration of new data sources takes less time.
    Having a common concept and techniques for how data should pass through analytics systems makes it simpler to plan for integrating new data sources and minimizes the time and expense of integrating them.
  3. Data quality assurance
    Understanding data streams as pipelines that need to be regulated and useful to end-users increases data quality and minimizes the chances of pipeline breakdowns going undiscovered.
  4. Assurance of the pipeline’s security
    With repetitive patterns and consistent knowledge of tools and architectures, security is baked in from the start. Good security procedures can easily apply to new dataflows or data sources.
  5. Build in stages
    When you think of your dataflows as pipelines, you can scale them up gradually. You can get started early and achieve benefits immediately by starting with a modest controllable segment from a data source to a user.
  6. 6 Agility and flexibility
    Pipelines give a structure for responding dynamically to modifications in the sources or the needs of your data users.
    Extensible, modular, and reusable Data Pipelines are a bigger topic in Data Engineering that is very significant.

Features

A well-designed end-to-end data science pipeline can find, collect, manage, analyze, model, and transform data to uncover possibilities and create cost-effective business operations.

features

Source: Burtch Works

Current data science pipelines make extracting knowledge from the big data you collect simple and quick.

The finest data science pipelines contain the following features to accomplish this:

  • Data processing that is both continuous and expandable
  • Elasticity and agility afforded by the cloud
  • Access to data on a large scale and the capacity to self-serve
  • Disaster recovery and high availability

How do various industries use the data science pipeline?

Regardless of the industry, the data science pipeline is beneficial to teams. The following are some instances of how different teams have used the process:

1 Risk analysis: Risk analysis is a method financial institutions use to make sense of enormous amounts of unstructured data to determine where potential hazards from rivals, the market, or consumers are located and how they might be avoided.

Organizations have also used Domo’s (a software company) DSML tools and model findings for proactive risk mitigation and planning. Medical experts make use of data science to help them conduct research. Machine learning algorithms are used in one study to aid in the research of how to increase picture quality in MRIs and x-rays.

Domo’s (a software company) Natural Language Processing and DSML have been used successfully by companies outside the medical field to predict how specific actions affect the customer experience. This allows people to anticipate dangers and maintain a favorable experience.

2 Forecasting: Data science pipelines are used by the transportation industry to estimate the impact of development or other road projects on traffic. This also aids experts in formulating effective solutions.

Domo’s(a software company) DSML solutions have also shown to forecast future product demand for other business teams effectively. The platform includes multivariate time series modeling at the SKU level, allowing them to appropriately plan across the supply chain and beyond.

What will the future data science pipeline look like?

The data science pipeline is essential to extracting insights from ever-larger and more detailed information. Organizations must depend on a methodology that disintegrates datasets and offers meaningful insights in real-time as the amount of available data to enterprises continues to grow.

The data science pipeline’s agility and speed will only improve as new technology arrives. The method would become smarter, more agile, and more flexible, allowing teams to dig a little deeper into data than ever before.

Conclusion

So in this article, we studied Data Science Pipelines. Some of the key takeaways are:

  • Working of data science pipelines.
  • Various stages in data science pipelines.
  •  Various features of data science pipelines
  • Real-Time usage by industries

Data science isn’t about working with various machine learning algorithms; it’s about creating solutions using them. It’s also critical to ensure that your pipeline is strong from beginning to end and that you identify specific business problems to provide precise solutions

I hope you liked my article on the data science pipeline; please share in the comments below.

My name is Pranshu Sharma, and I am a Data Science Enthusiast. Thank you so much for taking your precious time to read this blog. Feel free to point out any mistake(I’m a learner, after all) and provide respective feedback or leave a comment.

Feedback:Email: [email protected]

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

Pranshu Sharma
Pranshu Sharma

Aspiring Data Scientist | M.TECH, CSE at NIT DURGAPUR

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