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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

The importance of context for an analyst!

Kunal Jain
Kunal Jain Last Updated : 24 Apr, 2015
4 min read

We analysts enjoy crisp, objective and to the point conversations. An ideal conversation for us is when we come straight to the point, discuss and finish the conversation.

Don’t understand what I mean? Here is an example: This is a mail I received in my inbox (name of sender removed). The mail might look crisp, but it does not connect with me. Can you spot the problem?

no context

The problem: It has absolutely no context! No Subject and a 3 word qualification of the person…that’s it. How do I help this person, even if I want to? Imagine what would I do if I am not some one inclined to help? Just delete it.

Don’t get me wrong here. I am as much an advocate of brevity as any of you are. I am not asking for a 2 page long mail here. All I am saying is providing right amount of context is critical to influence people. For example, if this person would have mentioned details like: Where has he done B.Tech. form? Which stream? What are his interests? Why does he want a job in analytics? and what all has he already tried? I could have been of more help to him.

Same principle applies to your projects / analytical work as well. 

Until you provide the right details and context, it would be difficult to influence your stakeholders and business owners. In rest of this article, I explain how this principle applies to various situations in life of an analyst and then provide simple tips to overcome these situations.

[stextbox id= “section”]Case 1: A business Intelligence professional:[/stextbox]

Lets take a scenario, where you are responsible for creating dashboard and monitoring business for a e-commerce website. You have tallied your numbers across sources and put in all the hard work to make sure they are accurate. You present these absolute numbers to business users in form of a neat dashboard, but they don’t use it.

GA_screenshot

Why? You forgot to include the right context for them.

If you just provide number of sales in last month as a metric, it does not help. You need to compare this number against last year / last month / plan / benchmark. Whether is has improved / degraded? If the Sales were higher, were they because of more visitors or better conversion?

Providing these details makes your easier and more insightful to use. So, next time when you create a dashboard, ask yourself, whether you have provided enough context?

[stextbox id = “section”]Case 2: A predictive modeler:[/stextbox]

Lets take another scenario. You were asked to build next generation fraud detection model for your employer (a credit card provider). You have put in a lot of hard work and are excited about your findings. Just one final step, before you can implement the model – you need to get it approved from credit risk approval committee. You prepare your presentation making sure you explain every minute detail. But guess what? While presenting this model to the committee, you feel like you are talking to stones. No one is responding to the details you provided. Reason – they are not at same page with you.

young business people in office

While making any presentation, you need to start with what is in it for business users? Even if they are aware of the background, quickly recap it before you get into details. This will align everyone back to the business problem. Then, you can explain how your solution can benefit and then present the solution!

[stextbox id=”section”]2 simple tips to make more impact from your work:[/stextbox]

We come across these situations on day to day basis, where we fall short of creating the right impact because we undermine the importance of providing context. So next time when you are preparing / presenting something to your users, keep these simple tips in mind. They might look simple on the surface, but are very empowering:

  1. Start with the user in mind. If you follow this practice alone, you will be good in most of the cases.What are your users interested in? How aware are they? Do they have specific pain points you are aware of? Which product are they focusing on? What was the marketing campaign last month? What is the opportunity size for them? Each of these question impact what you present and how you present. So think carefully about these aspects, identify and address the needs of the customer
  2. Present as if you are narrating a story to a child – Visualize as if you are narrating a story to a child. What details will you include? How will you make it exciting? Include only the relevant details and pack it into a story / structure. Emphasize on aspects you want them to take away. If they are analysts, technical details will excite them more. If they are business users, details about implementation of your solution will matter more.

That’s it. Keep these simple tips in your mind and practice them every time you are talking to your business users.

What do you think about the importance of context? Do you have tips which can help to make more impact from our work? If yes, please share them through the comments below.

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.

Business AnalyticsBusiness IntelligenceDashboardmodelingpresentationrepresentation
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

pranay
pranay

Well explained sir........ you are the only reason why i am inclined towards Analytics and undergoing BA training using SAS HOPE I will be able to .......................succeed

123

shiv
shiv

if i am not wrong , what i concluded your whole post: Single line: tell Them only what matters to them and How to improve it. they would be more interested to listen A business owner is interested only in revenue .. So Give them Figure in RS(Money) not total visitors A Marketing Manager would more interested in how to improve CTR and Lower Advertising Cost

123

Kunal Jain
Kunal Jain

Shiv, In addition to what you say, tell them (what they are interested in) in story format and in a way that people appreciate and get confidence on the hard work which has gone in. Thanks, Kunal

123
santosh
santosh

Agree Kunal. Most of the times we ( analytics professionals ) tend to get carried away with the technology side of analytics while customers/internal audiences are interested mainly with the " business outcomes" perspective.

123

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