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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

5 things every data science manager should do before leading a team

Kunal Jain
Kunal Jain Last Updated : 17 Apr, 2015
4 min read

I took very different roles and responsibilities while I was doing my corporate data science jobs. They not only gave me a lot of exposure about data science, but also taught me how to manage my managers.

I remember one of the roles very vividly. I was reporting to a person who had never done a data science project or led a data science team before. He was a good person and a good manager in other set-ups, but in a wrong place for leading a data science team (at least temporarily). Majority of his experience before taking up this role was in Sales. Some of the ways of managing people which worked for him in past where not going to work in this setup.

For example, we used to have 2 team meetings in a week, where each one of us updating the rest of the team on what we are working on and what is the plan for rest of the week. Over and above these were individual catch-ups and project related catch-ups. Most of us failed to understand the objectives of those weekly chores.

blackboard-statistics

The situation I have described is definitely not unique. This would be happening in every Organization trying to set up a data science team or trying to move some of their good performers in other roles to lead data science units. Hence, I thought of sharing a few tips for people making these transitions. These tips should help you become a better data science manager, if you have not been in a data science setup before.

 

Be a part of complete analytics / data science project life-cycle:

Nothing beats this piece of advice. No matter of research can give you the experience you get by being part of the team doing the project. You will understand why selling analytics to a potential can be difficult at times and can take months before the customer gives you a fair chance. It will also help you understand why data cleaning can take, what seems like ages from the outside. Further, implementing an analytics solution can give you its own share of learning – what are the hurdles you can come across? Why do you need to be paranoid about getting the implementation right?

If there is one tip you want to take away from this article – just take this. You can’t lead a data science team effectively until you have spent time (preferably hands-on) working on a project yourself.

 

Understand Data Science landscape:

While the first tip helps you gain depth into the subject, you also need to understand the breadth of the subject. That is what differentiates an good manager from a brilliant analyst. As a manager, you need to understand which tool and solution is best for what kind of problem.

Does the data demand a big data solution? Or a traditional data science method does the job? Do you continue to automate reporting on Excel or move on to tools like QlikView or Tableau? These are some of the questions, you will face as a manager and the decisions you take, will impact your team members and how they spend their time.

 This article might be a good starting point

Become awesome at structured thinking:

This is almost a given – you can’t be a good analytics manager, if you are not good at structured thinking. As an analyst, you are expected to put structure to unstructured problems. As a manager, you are expected to excel at putting the structure in place – with in minutes. You would enter into meetings which would lack structure and can only benefit from them, if you are able to put a structure to the discussion.

These articles might help you become better at structured thinking: The art of structured thinking, Tools for improving structured thinking

Improve your (data based) story telling skills:

As a data science manager, you will be expected to tell data based stories. The base expectation is to be good at communicating your thoughts. A good manager should be able to visualize data effectively and present it in a manner that it narrates a coherent story. Here are a couple of examples to get your brain thinking:

  • If you have to understand the regional distributions of your product, what is the best way to understand it? Tabulate the penetration by regions? By clusters? Or just draw a heat map overlaying the geographical map?
  • What is the best way to show how Sales have changed in 15 clusters compared to last year?

The better you get at story telling, the better it would be for your team to sell the solutions.

 

Define your own learning plan and agenda and share it with your team:

If you are getting into a data science manager role, you have a lot to learn in coming days. The best way to do this is to create a learning plan and share it with your team. This will not only help them understand what you already know and what you don’t, but also communicate that you are willing to learn the subject.

You can also ask the team members to create their learning plan and share it with the group and have weekly knowledge sharing sessions with in the teams to share your learning / experience.

End Notes:

What do you think about these tips? Do they prepare you to become better at managing a team of analysts or data scientists? If you have any more tips from your experience, please feel free to share them through comments below.

Image Source: iNostix blog

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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

kv
kv

Dear Gunal, Excellent tips who can thinking like me to get in to the managerial role in analytics domain. Will follow the tips and update you. Regards kv

123

Vimal Kumar
Vimal Kumar

Awesome tips - adding to the above - have a customer centric mindset and help in simplifying output assuming every user is a novice

123

Bal
Bal

Excellent tips for top jobs for Data scientist. I'm research for data generated by big player e.g. Google , IBM , Facebook etc. what are benefits in the data ecology . When we talk about big data , are these are prominent player or it's just the notion the date been created. Bal

123

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