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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

Why most data science trainings fail to deliver? How to overcome these failures?

Kunal Jain
Kunal Jain Last Updated : 13 Apr, 2015
4 min read

At the outset, it looks like there is no dearth of data science / analytics trainings available today. Our training listing page probably has more than 300 trainings listed and they come in all shape and size: short term / long term, tool specific courses, self paced / instructor led trainings, Online / offline and the list goes on!

But, how many of them enable you for a real life analytics career? Probably, only a handful of them! I think that there are some major flaws in the way data science trainings are being designed and delivered currently. Until we correct them, it is very difficult to create meaningful impact in careers of people undergoing these trainings.

jump

A good analogy to drive the point is to look at offerings of Apple vs. others. If you compare the products by writing down specs, you would not understand what Apple is delivering against its competitors. Similarly on the trainings comparison, whether a training is self paced vs. instructor led, delivered online or offline, which tool is it teaching are just specifications. You need to look at how they prepare you for an analytics career!

This is where a lot of trainings would fall through the cracks. In this article, I will bring out some of the common short-comings of various analytics programs and leave you with thoughts on how to overcome these short-comings.

P.S. My aim is not to point of the flaws in the eco-system. I too am part of it. The idea is to make sure people undergoing these trainings are making the right choices and decisions.

 

Limitations of the data science trainings & how to overcome them:

 

1. Limited / no attention to structured thinking:

Most of the trainings I know of, don’t emphasize the need of structured thinking enough. They assume that people from various backgrounds would be able to take amorphous business problems and put a data science framework around these problems to solve them.

On the other hand, most of the good analytics companies would train people on structured thinking as part of their induction.

What is the reason for this gap to exist? I think part of it is down to the fact that the need and expertise of structured thinking are difficult to quantify currently. It is something which can not be communicated in form of certifications. But, the people undergoing these trainings would feel the heat, the minute they face an interview for data science positions.

How to emphasize / learn structured thinking:

You can start by reading these articles:

The best way to cover for these shortcomings is to practice structured thinking and practice it in day to day activities. The more structured your thoughts, the easier it is to solve business problems.

 

2. Focus on tools instead of learning fundamentals of the subject:

Stakeholders in the training ecosystem need to understand that learning data science is different from learning its tools. A certification in SAS or R does not prepare you for solving real life problems. On the other hand, if you understand the fundamentals of the subject and can put frameworks in place, you can always learn and apply the tools very easily.

What do I mean by fundamentals of data science? By fundamentals, I meant that the person undergoing training understands what are we trying to do with regressions, their underlying assumptions and their shortcomings.

The best way to solve for this shortcoming is to be curious. If you don’t understand something, just ASK! You can ask your mentors / instructors or even use our discussion platform.

 

3. Not enough emphasis on feature engineering / data cleaning:

Most of the training would provide you small datasets to play around and apply your programming techniques. Learning clustering? Take the standard IRIS dataset. Learning time series? Take the airline passenger data.

While these toy datasets are good to get the hang of concepts, they fail to provide understanding of real life challenges. They fail to make the trainee understand the importance of hypothesis building and spending time cleaning your data.

How to solve for this?

Kaggle is probably the best source to learn the importance of feature engineering. Start from the Titanic competition and move upwards on the level of complexity! Participate in as many competitions as possible (but one at a time) and see what other data scientists are doing by following the forums.

 

4. Trainings don’t prepare you for real life implementation problems:

How many people passing out of these trainings / courses would appreciate the difference in approach required to implement a data science solution in a manufacturing setup vs. e-Commerce setup vs. BFSI companies? Not many. While a lot of this might come from experience, including them as part of curriculum definitely makes a better data science professional.

Again, the easiest solution to this is by being curious. Ask your mentors and instructors, read out case studies, network with people in industry – talk to them about their challenges and learnings. The least you should do is make the most of your instructor’s experience.

Internships can also turn out to be a useful way to get a real hang of these problems. If you can get such an internship, nothing like it!

 win

End Notes:

Choosing trainings by just comparing on a few features can lead you to wrong outcomes. Hopefully, you would see what I am trying to tell.  Ask these questions while selecting the trainings. Chances are that you won’t find a perfect training out there. But, you will be aware of these short-comings and can then cover up these short-comings on your own.

Hope you find these questions useful. If you are facing challenges in figuring out the right data science training, feel free to reach out to us. We will love to help you in any manner we can.

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

Clear

Rashmi
Rashmi

Awesome article.thanks....

123

Anupam Basu
Anupam Basu

This article nailed it. Talking from personal experience nobody pays attention to the points mentioned in this article but this is the most important part of it that most Data Science trainings miss. Great thinking. By the way one of the best Data Science website I've ever come across. Very powerful & well structured. Keep posting & keep spreading the good things & knowledge you do!

123

Ranadev
Ranadev

U have put in a good article!! For us who r trying to get into analytics we are in a confused state on how to use the data and more importantly on how to collect the right data!

123

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