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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

Our top 10 Data Science articles in 2014

Kunal Jain
Kunal Jain Last Updated : 04 Nov, 2024
4 min read

2014 has been a year of growth for us. We now get 10x traffic compared to what we used to get 12 months back. It gives us immense satisfaction to be able to create something which is helping more and more people every day. We only hope that we could get some more time to create more content for our audience!

Not only we wrote more articles and better articles in 2014, we also started a jobs listing and a trainings listing page

2014

While we are on the brink of entering into 2015, I thought it might be a good time to recap some of the best articles we have written in 2014. Please note that these are articles which were written and read the most in 2014. The analyst in me can not help from pointing out that the articles early in the year have some advantage. But as I said, the later articles were written when we had far higher reach!

So, here are the articles, which were written and read the most in 2014. In order to make the article more interesting, I have included some behind the scene stories along with the articles:

  1. SAS vs. R vs. Python – which tool should I learn?

    This was clearly the best article we wrote this year. It not only helps people compare and decide which is the right tool to learn, but is also one of the fairest comparison of these tools till date. This article has a few stories associated with it. First of all, I was not sure whether to include Python in this article. I knew that usage of Python is increasing, but I was not sure whether it is mainstream enough! When I look back, including Python in the mix was probably one of the better decisions I made in the year. The article got a lot of appreciation for it’s non-biased view towards all these tools and it continues to be a traffic puller for us. Here is another interesting fact about the article – I added the scores on various attributes at the end as I was not satisfied with the previous version of the article.

  2. Tips to crack a Guess estimate (Analytics case study):

    A must read article, if you are appearing for interviews for the position of Business Analyst. As the name suggests, the article gives you a nice framework and a lot of tips to excel in analytics case studies during interviews. When Tavish first discussed this idea of writing an article on this topic, I wasn’t sure it would come out so nicely! The only reason I agreed to go ahead because Tavish felt very passionately about the topic and said that he can write an awesome article on the topic. Another bet which paid off!

  3. Set Analysis in QlikView:

    Set analysis is one of the most complex, useful and dreaded topic in QlikView. All said, they are also the life line of any good business dashboard. I had some acquaintance with set analysis before I read this article, but this article explains such a complex topic in very simple way using a lot of examples.

  4. How to use AGGR() function in QlikView?

    An awesome article to read, if you have used QlikView for some time. AGGR() functions along with set analysis is what differentiates a pro from a novice! This article continues from the awesome article on set analysis.  are the life line of any good business dashboard. I myself was not comfortable using AGGR() functions in QlikView, till I read this article from Sunil. He has played on his strength here – how to take a complex topic and simplify it to explain it to any lay man!

  5. Must have books for data scientists (or aspiring ones):

    This article is on one of my favorite subject – books. We have a popular article on books from 2013. This was the one for 2014. Each of these books is a treasure of knowledge and are becoming immensely helpful to people learning R or Python by themselves. If you have still not read some of the books mentioned here, check out if you can get some discounts during the holiday season.

  6. Planning a late career shift to analytics / Big Data? Better be prepared:

    One of the most useful article, if you are making a lateral shift to analytics. The article contains a lot of useful advice on what to expect during the transition and how to go about making this career transition.

  7. Performing exploratory data analysis in Pandas (baby steps in Python): 

    I started the baby steps series when my daughter took her first steps. This is one of the best tutorials on use of python (pandas) for performing data analysis. This solves one of the most common problems on Kaggle, but still manages to teach the subject to a new Python convert!

  8. Data Science projects to learn data science:

    This article came straight from the heart! This is how I feel each one of us should go about learning data science. Whether you are new to the field or have spent years in the domain, you might still find an interesting project here. The article provides a good mix of problems across several categories, which are not only fun, but will leave you wanting more of them.

  9. Should I / How do I become a data scientist?:

    Our take on one of the most commonly asked question by wannabe data scientists across the globe. The first article provides our self developed framework to help you decide whether you fit the role of a data scientist. The second article starts exactly where one article ends, how to become a data scientist once you are clear that you should become one.

  10. Definitive guide to prepare for an analytics interview:

    Another article with tons of information – the only resource you need to crack an analytics interview. Do read it, if you are planning career switches in 2015!

These were some of the most popular articles we have written this year. They were instrumental in helping us grow tremendously and continue to bring us traffic. These are just the tip of the iceberg. There are many more articles which would provide you with wealth of information.

Gearing up for 2015:

Hope you found this recap useful. If you want us to write more on specific topics, do let us know through comments below. We will make sure your wish is granted in 2015! Looking forward to hear more from you.

Happy New Year 2015

If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page.

Kunal Jain
Kunal Jain

Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of Al professionals. With over 17 years of experience in the field, Kunal has been instrumental in shaping the global Al landscape. His expertise spans diverse markets, from developed economies like the UK to emerging ones like India, where he has successfully led and delivered complex data-driven solutions. As a recognized thought leader, Kunal has empowered countless individuals to realize their Al ambitions through his visionary approach to Al education and community building. Before founding Analytics Vidhya, Kunal earned both his undergraduate and postgraduate degrees from IIT Bombay and held key roles at Capital One and Aviva Life Insurance across multiple geographies. His passion lies at the intersection of analytics, Al, and fostering a thriving community of data science professionals.

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Responses From Readers

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Ena
Ena

Hello Kunal I have MBA (Marketing) degree. Now i am working as a desktop support anlayst for a company in canada. How can i choose my career? HOw can i use my Business degree with my IT knowdledge? Should i go for MS SQL or System admin or Network admin or business analyst? I seen you provide some good advise in website Please help me with your ideas. I am kind of frustrated for my career. thanks

123

Sandeep
Sandeep

Hi Kunal, I have an experience as SAP BO BI platform administratio. Pursuing BABI at Great lakes. I have been offered a job of BI analyst recently at startup. Would an experience as BI analyst be right move To further aspirantion as analytics consultant ? Regards Sandeep

123

Riya
Riya

All are great blogs! Thanks for sharing! Data Science is the competitive advantage of the future for organizations interested in turning their data into a product through analytics. Industries from health, to national security, to finance, to energy can be improved by creating better data analytics through Data Science. The winners and the losers in the emerging data economy are going to be determined by their Data Science teams. Booz Allen Hamilton created The Field Guide to Data Science to help organizations of all types and missions understand how to make use of data as a resource. The text spells out what Data Science is and why it matters to organizations as well as how to create Data Science teams. Along the way, our team of experts provides field-tested approaches, personal tips and tricks, and real-life case studies. Senior leaders will walk away with a deeper understanding of the concepts at the heart of Data Science. Practitioners will add to their toolboxes.

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