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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

Data Driven Culture: A Far-fetched Goal for Organizations

A
Aman Last Updated : 12 Jul, 2022
5 min read
This article was published as a part of the Data Science Blogathon.

Introduction

Creating a collaborative, data-driven culture is one of the most important goals of many modern organizations. A data-driven culture is when data is used to make decisions at every level of the organization. A data-driven culture is about replacing the gut feeling to make decisions with facts.

However, access to the data and data processing tools remain restricted to the selected few technical users or upper management echelons.

“On average, people should be more skeptical when they see numbers. They should be more willing to play around with the data themselves.” — Nate Silver, founder, and editor in chief of FiveThirtyEight.

Data-driven culture cannot exist without the democratization of the data. Data democratization certainly does not mean unrestricted access to all the organizational data. The aim of data democratization is that the data that can help an employee make an effective business decision should be available to them in a usable format, fast enough to access, and not require them to be a technical data expert to make sense of it.

Can data be Democratized by Ensuring Access to it?

Below is the survey result of where do data scientists spend most of their time:

On average, data scientists spend almost 80% of their time in data preparation. Is it a good use of their time, though?

Despite spending most of their time on the data preparation tasks, most data scientists find these tasks the least enjoyable part of their job. This observation is coming from one of the most technically advanced communities of data users. Understandably, people want insights from the data but have to spend too much time in data preparation itself, thus hindering the propagation of a data-driven culture.

Can centralized Data Engineering Services help in Data Democratization?

To overcome this issue, traditionally, organizations have tasked a centralized data engineering team to create an enterprise-level data warehouse or a data lake. Analysts can then plug into this central data store to get insights. However, this model of data delivery is currently under strain. Let me paint a situation for you:

Just imagine that one needs an additional metric to complete their data analysis. However, to this metric requires additional data to be processed in the data warehouse.

To get this serviced by a centralized data engineering team employee need to wait for their turn even if it is a minor change that the they are capable to write themselves

There are three main issues with the centralized data engineering services:

  1. The tailor-made design and delivery model is not scalable
  2. When a centralized team has to write business logic within data pipelines, the ownership of the business logic gets diluted.
  3. As data pipelines are supposed to extract data from the source and also need to transform the data based on the business rules, these pipelines can become highly susceptible to the changes happening at both the source and the target ends. This tight coupling between source and target pushes up the maintenance cost of the data pipelines many times.

How can the Data Engineering Teams Achieve Scale?

Interestingly, the solution to the scalability of the tailor-made delivery model also comes from the same fashion industry — Readymade garments.
Fashion companies produce readymade garments in various standard fits and sizes as ready products. However, if a customer still needs minor customization, she can always get it done at a fraction of the cost and the time compared to a bespoke solution from scratch.

Building on a similar analogy, Data engineering teams too can achieve scale by centrally managing the heavy-technical aspects while establishing technology, process, and people practices for enabling the minor customizations to be done by the business users in the self-service model. Let us see what the components which can be centrally delivered as a managed service are:

  1. Platform-As-A-Service (PAAS/ SAAS) — The infrastructure to create data pipelines and perform analytics is centrally managed. It should ideally include the tools and applications as well. The platform should be highly scalable and should preferably be cloud-based
  2. Data-As-A-Service (DAAS) — Data procurement, data quality, and the performance are managed centrally by creating a centralized Data Lakehouse as a product
  3. Data Governance — Data governance takes a much more central stage in the decentralized delivery model because every governance lapse can be amplified by the number of users who can also directly manipulate the data. The data engineering team needs a renewed focus on quality, access, lineage, cataloging, etc., before the access to the data can be opened up for the whole organization.

The only component that we plan to decentralize is the business-logic component of the previous data pipelines. The domain-specific business logic component should be decentralized to handle the technical users closer to the domain, such as data analysts and data scientists.

Conclusion

Organizations struggle to propagate data-driven culture due to below significant challenges:

  1. Access to the data is limited
  2. If access is available, then it is in the read-only format via reports, etc., thus limiting the playing-with-data aspect, which is essential for data democratization.
  3. Access to the raw data does not democratize it as too much time is spent in the repetitive and redundant tasks of data preparation itself.
  4. Centralized data engineering teams suffer scalability challenges due to tailor-made delivery models.

One way to promote data-driven culture can be by promoting a hybrid data delivery model. The hybrid delivery model can promote data democratization by providing self-service of business logic while centrally managing the heavy-lifting of the data preparation.

The modern data stack is the driving force behind this concept of the hybrid data-delivery model. We will look into the technical details on how to achieve this by leveraging the right tool and the architecture in the upcoming articles.

“We are now into an era where big data is the starting point, not the end”  – Pearl Zhu, Digital Master Series

With the modern data stack, we are
at the inflection point where end-user enablement is the necessity and not just a wishlist for a truly data-driven culture.

Please reach out to me on Linkedin for further conversations.

If you enjoyed reading this article, don’t forget to follow me. Feedback is welcome!

Many thanks!

Reference:

  1. https://www.linkedin.com/pulse/20141118145642-24928192-predictably-inaccurate-big-data-brokers/
  2. https://visit.figure-eight.com/rs/416-ZBE-142/images/CrowdFlower_DataScienceReport_2016.pdf
  3. https://www.researchgate.net/figure/Time-consuming-activities-of-data-science-5_fig1_343441512
  4. https://aryng.com/blog/blogmain/what-is-data-driven-culture-and-how-to-develop-it/#:~:text=But%20what%20is%20data%2Ddriven,decisions%20with%20facts%20and%20assumptions.
  5. https://www.goodreads.com/work/quotes/43583663-digital-master

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

A
Aman

Aman is a Senior Data Engineering Manager at Akamai, responsible for Enterprise Data Warehouse and enabling BI and analytics. He talks about Data democratization, Data-driven culture, and Engineering Management.
Aman is a data democratization enthusiast and advocates the enablement of citizen data analysts through modern technologies.

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