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Reading list

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
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 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

Lead Data Scientist – M.Paani – Mumbai (3-5 years of experience)

a
adithya 17 Feb, 2015
2 min read

Designation – Lead Data Scientist

Location – Mumbai

About employer – M.Paani

Job description:

Responsibilities

  • Build out the entire analytics and visualization suite to monitor and assess all aspects of our business
  • Use various machine learning strategies to model and forecast consumer dynamics as well as business performance indicators using both spatial and non-spatial data sources
  • Source, recruit, onboard and manage a world class data science team
  • Harness statistical, machine learning, and GIS methods to build decision support tools for everything from targeted marketing to site selection
  • Work with the Tech, Sales, Business Development, and Customer Service teams to establish a data collection and analysis pipeline that must scale gracefully to millions of users
  • Conceptualize and develop a consumer-grade analytics product and participate in pitching it to potential clients
  • Closely collaborate with tech team on full stack implementation, from back-end analytics to front-end dashboard creation
  • Determine high-level, strategic goals and subsequently establish and execute upon a data & analytics roadmap

Qualification and Skills Required

  • A Ph.D (preferably) or Masters in fields with a strong quantitative as well as geospatial focus such as GIS & Remote Sensing, Environmental Sciences, Econometrics, Computer Science, Applied Mathematics, and/or Engineering from a reputed University
  • At least 3-5 years of relevant work experience that included recruiting, building and managing a data science team
  • Extensive work experience with geospatial and time series data
  • Expertise in at least one statistical analysis tool such as R, MATLAB, Octave, IDL, Python (Stats Models, Pandas etc.) or some other statistical programming language
  • Expertise in at least one scripting language such as Python or Ruby
  • Knowledge of PostgreSQL or MySQL
  • Understanding and implementation of Big Data frameworks especially Hadoop
  • Previous work with cloud-based platforms such as AWS, Heroku, Google Compute Engine etc.
  • Knowledge of any GIS tool or platform: Arc GIS, Quantum GIS, ILWIS
  • Previous work with spatial databases such as Post GIS and Oracle Spatial
  • Strong know-how of at least one of the following: machine learning, spatio-temporal modelling, graphical models
  • Basic knowledge of web technologies and platforms including HTML/CSS, Javascript, AJAX, WebGL
  • Ability to communicate your findings and insights in a precise and actionable manner
  • Capacity to iterate and ship ideas quickly and cost-effectively
  • Desire to work in a small, goal-oriented, high-pressure, highly-rewarding environment with a focus on a socially motivated mission
  • No hesitation to communicate easily with others and ask questions
  • Solid understanding of web-based visualization libraries and frameworks: D3.js, Processing.js etc.
  • Previous work with web mapping and GIS frameworks such as Leaflet.js, MapBox, CartoDB, ArcGIS Online etc.
  • Previous work and implementation of spatial models and/or clustering such as crime mapping, geographical regression, interpolation etc.
  • Knowledge of NoSQL database technologies such as MongoDB, Accumulo
  • Previous work in a startup, especially a socially motivated one
  • Knowledge and passion for consumer marketing and loyalty programs especially for the mass markets
  • International education/work experience, and/or previous experience in working with multinational and multicultural teams
  • Familiarity with Indian data governance laws
  • Proficiency in Hindi and/or Marathi

Interested people can apply for this job can mail their CV to [email protected] with subject as Lead Data Scientist – M.Paani – Mumbai

If you want to stay updated on latest analytics jobs, follow our job postings on twitter or like our Careers in Analytics page on Facebook

AccumuloArcGISBig dataCartoDBdata scienceGraphical modelsHadoopIDLinterpolationmachine learningMapBoxMatlabmodellingMongoDBNoSQLOctavePostgreSQLpythonRregression
a
adithya 17 Feb, 2015

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