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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

Top 10 Ultimate Data Science Blogs that you Can’t Miss

Nitika Sharma
Nitika Sharma Last Updated : 08 Feb, 2024
3 min read

Introduction

In the ever-evolving world of data science, staying updated with the latest trends, tools, and techniques is crucial for anyone looking to keep their edge. Whether you’re a seasoned data scientist or just starting out, the wealth of knowledge available online can be overwhelming. That’s where data science blogs come in – they are the lighthouses in the vast sea of information. But with so many options, which ones should you follow? Fear not, for we’ve curated a list of the 10 best data science blogs that are a must-follow for anyone passionate about data.

Table of contents

Also Read: Top Data Science News Websites

Analytics Vidhya Blog

Analytics Vidhya is the go-to hub for Data Science enthusiasts, offering a dynamic community and an abundance of resources. With a diverse array of content spanning blogs, practice problems, courses, and hackathons, it cultivates ongoing learning and interaction. Its wide-ranging collection of technical articles empowers professionals to explore the intricacies of Data Science and AI, bolstering the community’s expertise. For those eager to stay abreast of the latest trends and advancements in Data Science, embracing Analytics Vidhya is indispensable.

You can follow analytics vidhya blogs here.

Data Science Central

Data Science Central is your go-to hub for all things related to data science and big data. Covering topics from analytics to tools and job opportunities, it’s a treasure trove of insights contributed by industry experts. With frequent updates and a community forum, it’s a dynamic space for discussions and learning.

You can access this blog here.

SmartData Collective

SmartData Collective is a community site focusing on business intelligence and data management trends. With contributions from industry experts, it explores the intersection of data science and business, offering valuable insights into the broader field.

You can access this blog here.

What’s The Big Data?

This data science blog delves into the impact of big data on our digital world. Written by Gil Press, a seasoned data researcher, it explores how big data influences technology, business, and government policies, providing valuable news and commentary.

You can access this blog here.

No Free Hunch

This data science blog offers a peek into the minds of data scientists, featuring tutorials, news, and insights directly from Kaggle, a platform hosting data science competitions. With over 200 competitions held, it’s a hub for innovative approaches and diverse perspectives in solving data science challenges.

You can access this blog here.

insideBIGDATA

This exclusive data science blog focuses on the machine learning aspect of data science, covering topics like big data, machine learning, and artificial intelligence. With categorized articles, guest features, and resources, it’s a must-visit for staying updated on machine learning trends.

You can access this blog here.

Simply Statistics

Run by three biostatistics professors, Simply Statistics explores statistics in big data and its applications across various fields. It offers insights, interviews, and discussions, making it an essential resource for statisticians and aspiring data scientists.

You can access this blog here.

Datafloq

Datafloq, led by Mark Van Rijmenam, focuses on the business aspects of big data, offering insights into making data science work for organizations. Alongside articles and insights, it facilitates professional connections through job postings, events, and training opportunities.

You can access this blog here.

Data Science 101

Data Science 101, managed by Ryan Swanstrom, provides valuable advice and tips for aspiring data scientists. With extensive archives dating back to 2012, it offers a wealth of knowledge for anyone looking to succeed in the field.

You can access this blog here.

Dataconomy

Dataconomy offers news, trends, and editorials on big data alongside resources for building a career in data science. From beginner’s guides to job boards, it caters to both newcomers and professionals in the industry.

You can access this blog here.

Conclusion

The world of data science is vast and full of resources, but the blogs listed above are sure to provide you with a solid foundation of knowledge, inspiration, and practical advice. Whether you’re looking to deepen your understanding, stay on top of industry trends, or simply find a community of like-minded individuals, these blogs are your gateway to the world of data science. Remember, the field is always changing, so keep exploring and never stop learning!

Follow us on Google News to stay updated with the latest innovations in the world of AI, Data Science, & GenAI.

Nitika Sharma
Nitika Sharma

Hello, I am Nitika, a tech-savvy Content Creator and Marketer. Creativity and learning new things come naturally to me. I have expertise in creating result-driven content strategies. I am well versed in SEO Management, Keyword Operations, Web Content Writing, Communication, Content Strategy, Editing, and Writing.

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