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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

9 best practices for analytics talent management

Kunal Jain
Kunal Jain Last Updated : 18 Apr, 2015
3 min read

Interviewing for Analytics positions is fun! I have conducted hundreds of interviews and I still grab most of the opportunities which come my way. However, right recruitment is only the start of the journey. The next most important thing is to manage this talent effectively. This article aims to lay out these practices for the benefit of new managers and leaders. These are practices, I have learned over the years through a lot of practice.

Business man shows success abstract flow chart

I will keep these best practices specific to anlaytics talent management and refrain from putting up any generic advice (e.g. Keep a healthy work life balance! ). You can search them elsewhere.

  1. Understand what they (analysts) want to do? Make sure they are not in the wrong place! – This is the most critical takeaway for any manager. Honest understanding and evaluation of the ambitions / motivation of the analyst can itself avoid most of the big management disasters. The evaluation, which, I try and do in the initial few days, typically results in one of the following
    1. Person is not capable of a technical role. Go back and check why was he hired in the first place. Also, do a favour by putting him into a more relevant role
    2. Try and see which profile does he / she fits in – Programmer, Statistician, Data Scientist, Consultant, BI programme manager, Developer, Engagement Manager
  2. Set aside time for regular mentoring and work catch up – probably daily in initial days; Feel free to do so in an informal environment, once in a while. The frequency can go down once both of you are comfortable with each other’s way of working.
  3. Ask them to surprise you, especially in the initial days! Typically a good performer will surprise you with their work, multiple times within first 90 days! If this does not happen, you are dealing with mediocre talent or low motivation. Try and get to the bottom and address quickly.
  4. Make them knowledge junkies: The best of analysts love learning new tools and techniques. Also, given the fast pace of changes in this industry, it is best to put aside time for structured learning and development.
  5. Give them more and more ownership: Over time, this is the best gift for your best analysts. Give this and they will love it. Tell them the business problem and leave them on their own. Help them with brainstorming! Mentor them! But, at the end of the day, give more and more ownership to them. Or, as one of my mentors says, “Ask them to get into bigger shoes!“
  6. Challenge them: Ask them to think more! Ask them to compete against the best of analysts across the globe through various data science competitions across the globe.
  7. Nothing beats an honest, clear, objective and unambiguous constructive feedback: Don’t mix up the words here. Communicate your feedback honestly and objectively in a constructive manner.
  8. Set up opportunities for getting mentored by the best analysts (with in / outside team): Learning from someone, who has been through similar situation some time back and has come out flying, can give completely fresh and independent perspective.
  9. Finally, see career progression by getting in the shoes of the analyst: You can only do justice to an analyst’s career, if you understand what he wants (remember practice # 1?). Whether your best talent is moving on from his current job or wants to take a sabbatical to try out something new, do the right thing in his interest (rather than yours). This might be difficult in the short term, but is the right thing to do!

What do you think about these best practices? Do you have more to add on? Do you find it difficult to implement some of these? What challenges did you face? I’ll be interested to hear your perspective.

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.

photo credit: ffaalumni via photopin cc

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

Anna
Anna

Hi Kunal, I agree with every single word in your blog post. My former manager followed all of these tips and even more. He put a very hard work into placing every analyst in what they were best suited for and what they enjoyed doing. We quickly gained more responsibility and ownership and took on challenging projects. In a matter of several months our daily work became full of opportunities for solving problems, pitching ideas to the best clients, learning new concepts, getting to know organizational processes and other people in the organization. He is a true mentor whom I was excited to work for. When I decided to move on to try something new, he supported my decision and provided me with some of the best advice I've ever received. Analysts are "knowledge junkies" and having access to mentorship opportunities and resources to learn can truly make us shine. One of the points I would want to add to your list is setting up opportunities for analysts with different personality traits and skills, such as helping the ones that are better at presenting their ideas to become great presenters and consultants and helping the ones that prefer to work on their own on long consuming problem statements get enough quiet time to find solutions.

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Talent Acquisition Consulting Services
Talent Acquisition Consulting Services

In this post given all information is very informative and useful. all analytic are useful for talent management if thank you for sharing such valuable information.

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