ABOUT

Hi, I'm Chirag Malhan

PROFILE SUMMARY

With over three years of experience in data and business analysis, I strive to deliver meaningful results in every project I undertake. I value clear communication and teamwork, which helps me approach complex challenges with a combination of analytical thinking, careful decision-making, and creative problem-solving.

Currently, I am pursuing a post-graduate certification in data science and AI to expand my knowledge and transition into the field of data science.

TECHNICAL SKILLS

• Machine Learning: Fundamentals of ML, Supervised and Unsupervised Learning. Training of algorithms for Linear Regression, Logistic Regression, Decision Tree, Random Forest, and Time Series Forecasting.

• Deep Learning: Fundamentals of DL, Computer Vison, and Natural Language Processing (NLP). Training of networks like FCNN, CNN, RNN, LSTM, and pretrained models.

• Generative AI: Fundamentals of Encoder-Decoder, Transformer, and Retrieval-Augmented Generation (RAG) Architectures.

• Python for Data Science: Using libraries like NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, SciPy, and OpenCV for tasks such as ETL, EDA, data preprocessing, and data visualization. Can use R for similar tasks.

• Power BI: Fundamentals of Power BI, Data Analysis Expressions (DAX), and data visualization with interactive dashboards. Can use Tableau for similar tasks.

• SQL: Fundamentals of SQL, advanced data wrangling, user-defined functions, stored procedures, transactions, triggers, and common table expressions (CTE).

• MS-Excel for Data Science: Fundamentals of Excel, data analytics, data visualization, power tools, classification, information measure, regression, and Visual Basic for Applications (VBA). Can use Google Sheets for similar tasks.

SOFT SKILLS

• Presentations: Created and presented operational data analysis for management.

• Collaboration: Performed intra and inter-LOB collaborations for business development.

• Adaptability: Worked seamlessly under multiple managers and teams to achieve expected goals.

• Data Ethics: Adhered to ethical standards in data analysis, ensuring integrity and compliance.

• Problem Solving: Resolved operational and analytical blockers for both team and individual tasks.

• Structured Thinking: Developed a habit of breaking down complex problems into simpler steps.

• Learning Attitude: Maintained a keen and constant interest in learning new skills and enhancing existing ones.