Naveen Kannan’s Data Analytics Skills

Python

Proficient in the Python programming language and related packages such as pandas, NumPy, and scipy for data manipulation, exploration, and analysis.
Experienced in statistical analysis and modeling using Python, including regression analysis, time series analysis, clustering, and classification using packages such as statsmodels and scikit-learn.
Skilled in machine learning techniques such as decision trees, random forests, gradient boosting, and neural networks using packages such as TensorFlow, Keras, and PyTorch.
Experienced in using visualization tools such as Matplotlib, Seaborn, and Plotly for creating static and interactive visualizations.
Proficient in data preparation and cleaning using pandas and related packages, and able to handle missing values, outliers, and data transformations.
Skilled in feature engineering using techniques such as one-hot encoding, scaling, and feature selection.
Experienced in using scikit-learn for model selection, hyperparameter tuning, and evaluation using techniques such as cross-validation and ensemble methods.
Comfortable with using Jupyter notebooks for interactive data analysis and reporting.

Proficient in the R programming language and related packages such as tidyverse, ggplot2, dplyr, and data.table for data manipulation, exploration, and visualization.
Experienced in statistical analysis and modeling using R, including regression analysis, time series analysis, clustering, and classification.
Experienced in using visualization tools such as ggplot2 and Shiny for creating interactive dashboards and reports to communicate insights to stakeholders.

Familiar with ggplot2, a popular data visualization package in R, and able to create a wide range of visualizations such as scatter plots, box plots, and density plots using the Grammar of Graphics framework.
Experienced in creating publication-quality plots and charts using Matplotlib and related libraries such as Seaborn and Plotly, as well as ggplot in R.
Experienced in creating publication-quality tables for scientific journals.

Proficient in PySpark, a Python library for Apache Spark, for large-scale data processing and machine learning tasks, including data transformation, feature engineering, and model training.
Experienced in working with Hadoop distributed computing system and related technologies such as HDFS.
Skilled in writing and optimizing SQL queries for data extraction and analysis, using various database management systems.

Proficient in Linux operating system and command-line interface, including shell scripting, file system management, package installation, and system administration.
Experienced in managing Linux-based servers for hosting data science applications, using technologies such as Docker, and Apache web server.
Comfortable with programming languages commonly used in Linux environment such as Python, R, and Bash scripting.