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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

Sarbani Maiti’s Journey from Cobol to Cloud 

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Guest Blog Last Updated : 15 Mar, 2024
4 min read

At Analytics Vidhya, we’re celebrating the Women of Data Science by highlighting their remarkable journeys and achievements on our blog throughout March. We believe their stories can inspire, uplift, and empower others. Today, we have the privilege of featuring Sarbani Maiti. Let’s look at her inspiring story!

Sarbani Maiti'

Sarbani Maiti’s Journey in her own Words

My name is Sarbani Maiti; I am an AI/ML Specialist Solutions Architect in Databricks. Back in the year 2000, when the internet was just a new baby, I kickstarted my career as a Cobol/Unix developer. Being a BTech student of Electronics & Telecommunication, I knew almost nothing about computers. However, we had the luxury of having proper training periods where I learned about software engineering. From coding in Cobol to navigating through Java, Sybase, Teradata, Oracle, and finally landing in the fascinating world of Cloud, AI/ML, the journey has been nothing short of a roller coaster.

Family, Career, and Challenges

Balancing family, kid, and a demanding career has been one of my  most significant challenges. Late-night calls with USA customers  while trying to keep up with the daily hustle – it wasn’t easy. But, here’s the secret sauce – a robust support system. My family has  been my backbone, standing by me through thick and thin. The  challenges may be tough, but with the right support, you can turn  them into stepping stones towards success.  

Continuous Learning: The Key to Confidence and Success

Continuous learning has been my biggest strength. Embracing new  technologies and putting myself in challenging positions has been  the key to my confidence and success. I believe that learning is not  a destination but a journey, and it’s this journey that has allowed me  to secure a seat at the table in the ever-evolving tech industry. 

One crucial lesson I’ve learned is the power of leveraging old  knowledge. Moving from the world of Cobol to cloud computing and  now into data science, I didn’t unlearn my past; instead, I embraced  it. This fusion of old and new knowledge has enabled me to  understand enterprise challenges better and find innovative  solutions using cutting-edge technologies. 

Lessons from Wall Street

I have spent almost 10 yrs working with wall street banks. A  decade-long experience in the world of finance taught me  invaluable leadership and management skills. The focus was on running a tight ship, where failures and downtime were critical  factors to manage. However, embarking on new technologies and  adapting to changes were often slow and challenging. 

Now working with Silicon Valley … It’s a whole new ball game!  Here, it’s all about tech challenges, and every day feels like a crash  course in the latest innovations. The pace is relentless, and coping  with the daily influx of new technologies can be overwhelming. Yet,  it’s this very environment that fuels my passion for learning and  embracing the unknown. 

Silicon Valley: A New Ball Game

Transitioning from Wall Street to Silicon Valley has been a  transformative experience. The leadership skills learnt have proven  to be my guiding light in the tech chaos of Silicon Valley. It’s a  testament to the adaptability that every woman in tech possesses –  the ability to navigate different worlds and emerge stronger and  more resilient. 

Message for Women in Tech

Motherhood is an amazing experience of our life, we can not ignore the biological clock, unfortunately it clashes with our peak career timelines. Make the priority clear, outsource the work which does not add any value to our goals, we don’t have to be perfect in all the  areas. We can rebuilt our career even after 30…but we should not  loose track from whats happening in the industry, what is the trend  and very important build your professional network, mentor sponsor  who can help in your journey towards your goal. 

My suggestions to my fellow women in tech – embrace the  challenges, seek out support, and never stop learning. Your journey  may take unexpected turns, but remember, each experience adds a  layer to your expertise. The tech world is vast, and there’s a place  for your unique skills and perspectives. Build your thought  leadership skill and world is yours…

About Sarbani Maiti

As the leader of the Digital Transformation Program with 22 years of experience in large Investment Banks and the healthcare industry, Sarbani Maiti specializes in guiding businesses to translate their data into tangible value. Through robust data strategy, analytics, and AI/ML capabilities, she drives transformative change across organizations. Sarbani also shares her expertise through speaking engagements and articles, as well as contributes to open-source projects on GitHub.

Check out her LinkedIn profile here.

Summing-up

Sarbani Maiti’s journey from Cobol developer to AI/ML Specialist at Databricks exemplifies perseverance and adaptability. Balancing career and family, she embraced challenges, continuously learned, and leveraged past knowledge. Transitioning from finance to Silicon Valley showcased resilience. Her advice: prioritize, network, and never stop learning.

You can also share your story or nominate someone who has inspired you by filling out this form. 

Let’s continue to uplift and empower each other in the journey towards a more inclusive and innovative future.

International women's daySarbani Maitiwomen in AI
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Guest Blog

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