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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

5 Steps on How to Approach a New Data Science Problem

Yana Khare
Yana Khare Last Updated : 04 Dec, 2023
5 min read

Introduction

5 Steps on How to Approach a New Data Science Problem

Data science is a dynamic field that thrives on problem-solving. Every new problem presents an opportunity to apply innovative solutions using data-driven methodologies. However, navigating a new data science problem requires a structured approach to ensure efficient analysis and interpretation. Here are five essential steps to guide you through this process.

Table of contents

5 Steps on How to Approach a New Data Science Problem

Step 1: Define the Problem

Defining the problem marks the inception of the entire data science process. This phase requires a comprehensive understanding of the problem domain. It involves recognizing the issue and discerning its implications and context within the broader scenario. Key aspects include:

  • Problem Domain Understanding: Gaining insight into the industry or field in which the problem resides. This involves understanding the nuances, challenges, and intricacies of that domain.
  • Objective Identification: Clearly outline the objectives and goals of the analysis. This could be predicting customer behavior, optimizing resource allocation, enhancing product performance, or any other measurable outcome.
  • Actionable Statement Framing: Converting the problem into a well-defined, actionable statement. This statement should articulate the problem’s essence, making it understandable and aligned with the business or project goals.

The aim is to create a roadmap that guides subsequent steps in a focused direction, ensuring that all efforts are channeled towards resolving the core issue effectively.

Step 2: Decide on an Approach

Selecting the appropriate approach becomes paramount once the data science problem is clearly defined. Various factors play a role in this decision-making process:

  • Nature of the Problem: Understanding whether the problem falls under supervised learning (predictive modeling), unsupervised learning (clustering), or other paradigms helps determine the suitable techniques.
  • Resource Constraints: Considering available resources—computational power, data availability, expertise—helps choose feasible methodologies.
  • Complexity Assessment: Evaluating the complexity of the problem aids in selecting the right algorithms and techniques to achieve the desired outcomes within the given constraints.
  • Time Sensitivity: Identifying any time constraints is crucial. Some approaches might be more time-consuming but yield more accurate results, while others might be quicker but less accurate.

This step aims to lay the groundwork for the technical aspects of the project by choosing an approach that best aligns with the problem’s nature and constraints.

Step 3: Collect Data

Steps on How to Approach a New Data Science Problem

Data collection is fundamental to the success of any data science project. It involves sourcing relevant data from diverse sources and ensuring its quality. Key actions include:

  • Data Sourcing: Collecting data from multiple sources—databases, APIs, files, or other repositories—ensuring it covers the necessary aspects of the problem.
  • Data Quality Assurance: Validating the data for accuracy, completeness, and consistency. This often involves dealing with missing values, outliers, and other anomalies.
  • Data Preprocessing: Organizing and cleaning the data to prepare it for analysis. This includes tasks like normalization, transformation, and feature engineering.

A well-prepared dataset forms the foundation for accurate and meaningful analysis.

Step 4: Analyze Data

Steps on How to Approach a New Data Science Problem

With a clean dataset, the focus shifts towards extracting insights and patterns. Analyzing the data involves:

  • Exploratory Data Analysis (EDA): Examining the data visually and statistically to understand its characteristics, distributions, correlations, and outliers.
  • Feature Engineering: Selecting, transforming, or creating features that best represent the underlying patterns in the data.
  • Model Building and Evaluation: Applying suitable algorithms and methodologies to build models, followed by rigorous evaluation to ensure their effectiveness.

This step is pivotal in deriving meaningful conclusions and actionable insights from the data.

Step 5: Interpret Results

Interpreting the analyzed data is crucial to extract actionable insights and communicate them effectively. Key actions in this step include:

  • Deriving Meaningful Conclusions: Translating the analysis results into meaningful and actionable insights.
  • Contextual Understanding: Relating the findings to the original problem’s context to understand their significance and impact.
  • Effective Communication: Present the insights in a clear, understandable manner using visualization tools, reports, or presentations. This aids in communicating the results to stakeholders, enabling informed decision-making.

This step completes the data science lifecycle, transforming data-driven insights into valuable actions and strategies.

Example

Using the example below, let’s solve a data science problem.

Step 1: Define the Problem

Consider a healthcare scenario where a hospital aims to reduce patient readmissions. The problem definition involves understanding the factors contributing to high readmission rates and devising strategies to mitigate them. The objective is to create a predictive model that identifies patients at a higher risk of readmission within 30 days after discharge.

Step 2: Decide on an Approach

Given the nature of the problem—predicting an outcome based on historical data—a suitable approach could involve employing machine learning algorithms on patient records. Considering resource availability and the complexity of the problem, a supervised learning approach, like logistic regression or random forest, could be selected to predict readmission risk.

Step 3: Collect Data

Data collection involves gathering patient information such as demographics, medical history, diagnoses, medications, and prior hospital admissions. The hospital’s electronic health records (EHR) system is a primary source, supplemented by additional sources like laboratory reports and patient surveys. Ensuring data quality involves cleaning the dataset, handling missing values, and standardizing formats for uniformity.

Step 4: Analyze Data

Analyzing the dataset requires exploratory data analysis (EDA) to understand correlations between patient attributes and readmission rates. Feature engineering becomes crucial, extracting relevant features that significantly impact readmissions. Model training involves splitting the data into training and testing sets, then training the chosen algorithm on the training set and evaluating its performance on the test set.

Step 5: Interpret Results

Interpreting the results focuses on understanding the model’s predictions and their implications. Identifying which features contribute most to the prediction of readmissions helps prioritize intervention strategies. Insights gained from the model might suggest interventions such as personalized patient care plans, enhanced discharge procedures, or post-discharge follow-ups to reduce readmission rates.

Each step in this process, from defining the problem to interpreting results, contributes to a comprehensive approach to tackling the healthcare challenge of reducing patient readmissions. This structured methodology ensures a systematic and data-driven solution to the problem, potentially leading to improved patient outcomes and more efficient hospital operations.

Conclusion

As we conclude our exploration into the fundamental steps of approaching a new data science problem, it becomes evident that success in this realm hinges on meticulous planning and execution. The five steps outlined—defining the problem, choosing an approach, data collection, analysis, and result interpretation—form a robust framework that streamlines the journey from inquiry to actionable insights.

As the data science landscape evolves, this guide remains a timeless compass, aiding professionals in navigating the complexities of data-driven decision-making. By embracing this structured approach, practitioners unlock the true potential of data, transforming it from raw information into valuable insights that drive innovation and progress across various domains. Ultimately, the fusion of methodology, expertise, and a relentless pursuit of understanding propels data science toward more extraordinary achievements and impactful outcomes.

Yana Khare
Yana Khare

A 23-year-old, pursuing her Master's in English, an avid reader, and a melophile. My all-time favorite quote is by Albus Dumbledore - "Happiness can be found even in the darkest of times if one remembers to turn on the light."

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