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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

How to become an analytics rockstar?

Kunal Jain
Kunal Jain Last Updated : 29 Dec, 2015
5 min read

I still remember first day in my first job.

how to become analytics rockstar

 

I walked in the office with high ambitions and little understanding of what it takes for an analyst to become successful.

The decision to take up the job was based on the fact that some of my college alumni (whom I thought highly of) thought highly about the company and the role.

It took me almost 6 months to understand what is required to achieve success in Analytics industry. It happened only because I had the luxury of some really good mentors and loads of enthusiasm to learn new things. Not every person is lucky to have them early in their career.

While the understanding has obviously evolved over the years, most of my initial learning has stayed. I have obviously practiced and improved these learning during this time. In the remaining article, I’ll outline these success ‘mantras’ for becoming an analytics rockstar. These ‘mantras’ are as effective for a newbie as they are for a seasoned professional.

rockstar_framework

For sake of better understanding, I have mentioned these in flow of an analytics project:

[stextbox id=”section”]Achieve flawless business (& business process) understanding[/stextbox]

Unless you understand the business, you can not succeed in creating a solution for it. You have to understand the process end to end, why do things happen in the manner they do? What are the alternatives? What are the gaps? What are the opportunities? Are there places where execution differs from intended strategy? Why?

Before starting any project or working on any problem, following are some of the best practices I follow to achieve better business understanding:

  • Ask, ask and ask until the entire process makes sense and you have all the answers
  • Draw out the process flow and identify any gaps and opportunities.
  • If you are new to the area, spend a day on field (or the place of action) to understand the process end to end
  • Nominate yourself as a customer / business lead to see how the process works for customer.
  • Share your understanding with business and make sure there is no gap in understanding. Even a whiteboard / paper based discussion is fine. No need of any fancy presentation at this stage

[stextbox id=”section”]Structure, structure and structure….and then some more structure[/stextbox]

Structured thinking and writing is by far the biggest learning for any analyst. I have written an entire article on this before, but can not stop emphasizing it again. Putting a structure helps you deal with open ended business problems and statements like:

  • How do we keep write-offs from lending portfolio below 3%?
  • Who are our ideal customers and how much can we invest to acquire them?

The article written before has some best practices mentioned in order to improve structured thinking. Find them here.

[stextbox id=”section”]Call out your assumptions[/stextbox]

Remember to call out your assumptions in doing the analysis. Remember the implicit rate of interest? or the product margin you assumed? All of that needs to be mentioned to the stakeholders and decision makers. 

Why is this important?

This is important because you need to tell all the ingredients to your stakeholders. If you do this right, this will help you understand the errors coming in because of wrong assumption vs. gap in framework later on. Especially, in scenarios where you are entering the unknown, you will be able to go back and see what went as per expectation and what did not.

One of the best practice which helps to achieve this is to create a holistic list of assumption in your document / presentation and keep it as ready reference.

[stextbox id=”section”]List down all the hypothesis and then prove, discard or suspend them[/stextbox]

This is the most interesting part for business owners. Some of the answers they would look out for are:

  • Which business hypothesis stand validated with data / analysis?
  • Which hypothesis business was not aware of? What opportunity can they translate into?
  • Which hypothesis stand rejected? What is the impact of these corrections / changes?

In addition to this, list down all the hypothesis you had but have not been able to prove / dis-prove them due to lack of data, time or resources. Decision owners should know the blind spots before taking a decision.

[stextbox id=”section”]Keep implementation in mind and take business feedback on the solution as early as possible[/stextbox]

A solution is only as good as it can be implemented. Hence, keep your business owners in loop on the solution you are proposing. Until they feel excited about the solution, it is unlikely to fly. Following are the best practices to ensure this:

  • Keep it simple: It is always easy to create the next Pandora’s box with the tools and data. But what would you do if your CRM does not support it? or if there is no campaign management solution? Even if there is, can you create so many segments automatically? More often than not, keeping it simple makes it easy to implement solution. You can always propose that Pandora box in next iteration.
  • Apply 80 / 20 ruthlessly: If applying 20% of analysis outcome brings in 80% of benefits, apply them and focus on how you can boost these 80% benefits further.

[stextbox id=”section”]Presentation – What is the story?[/stextbox]

All your hard work gets culminated in the way you present it. So, give it the necessary thought space. Nothing beats preparation here. Consider following aspects before you present:

  • Are you presenting the story (coming out of numbers and analysis) or you are presenting what you did? If you are doing the later, STOP. It won’t help! Even if you have done various pieces of analysis and they don’t fit in the story, don’t present them. Also, if this happens regularly, think about how good was your structure in first place?
  • Who are you presenting to? What would they be interested in? Have you called out the solution to their needs in this presentation?
  • Follow the practice: “Tell them – what you will tell them, tell them – tell them, tell them – what you have told them” in structuring your presentation. Start with summary, present the findings and finish with summary of recommendations and benefits from the project.

[stextbox id=”section”]Learn something new regularly[/stextbox]

While, all the ‘mantras’ mentioned above help you become better with every project, this one improves you in long term. Make sure that you spend time learning something new. Analytics is a dynamic, fast paced industry. New software and solutions arrive every alternate month. Spend quality time regularly on your own development and learning. This will set you up for long term success. Here is what I do:

  • Read for 30 minutes daily before going to bed.
  • Create a learning group with people from diverse background and share each others learning through the week

 

If followed in disciplined manner, these ‘mantras’ will become your guide to make sure you are on your path to achieve success. If you know of any other practices which can help analysts accelerate on their path to success, please feel free to share them below.

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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

santosh shriyan
santosh shriyan

Kunal - thanks for sharing inputs w.r.t. Analytics in a simple manner........find it extremely useful for grasping concepts....you are a true Analytics Rockstar! Look forward to more...cheers! santosh

123

kiran
kiran

Dear Kunal, Can you suggest me a learning group? Warm regards, Kiran

123

Soumya
Soumya

Participate in data science competitions in Analytics Vidya, Kaggle or Innocentive. Join their forums etc. They are very good learning groups. A lot of new things can be learnt from the discussions.

123

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