Ritima Malhotra, Phd Email & Phone Number

My first rendezvous with in-depth data analysis & powerful visualizations happened during my PhD. Having completed an empirical study for my thesis where I researched the oft-overlooked yet indispensable trait of Emotional Intelligence, I fell in love with all things data. Be it retrieving data, cleansing data, encoding categorical data, treating missing values in data, deciding between applying a test based on the study hypotheses from a gamut of available statistical tools/methods & finally, making sense out of the data, i.e., interpreting test results & deriving actionable insights from data to resolve the underlying research/business problem. My research introduced me to the world of statistical models and packages including SPSS, Stata & Tableau, but it was Python that particularly piqued my interest in data science.What started as a tête-à-tête with statistical programming over a decade ago, graduated into a growing desire to apply my research & analytics acumen to real-world business problems & eventually led me into perusing a PGP in Data Science & Business Analytics that entailed hands-on Python programming for data analysis & machine learning.As a data professional, I always attempt to blend storytelling & experiential learning with empathic concern to build transformative relationships with my stakeholders. I believe that beyond fancy visualizations & advanced modelling, at the heart of data science lies collaborating with diverse teams, brainstorming & communicating meaningful insights in a visually absorbing yet easily comprehensible format. I endeavour to deliver just that!-- Skills: Tools/Languages: Python (NumPy, Pandas, Matplotlib, Statsmodels, Scikit-learn), MS Excel, SPSS Statistical Methods: Predictive Analysis, Hypothesis Testing & Statistical Inference, a/b Testing, Principal Component Analysis & Dimensionality Reduction Machine Learning: Supervised & unsupervised Models - Classification, Linear & Logistic Regression, L1 & L2 Regularization, Clustering (K-Means, Hierarchical), Random Forest, Decision Tree, Bagging, Boosting  Data Visualization: Seaborn, Plotly, Tableau Source Code: Github Other Tools: SAFe Agilist, Waterfall, AgileWhen I’m not on the job, I love baking my way through every healthy recipe I can get my hands on, or getting to learn anything new, which currently happens to be exploring different data visualization tools!