See. Think. Do.

Unlock the power of data-driven insights

Revealing Insights From Complex Data

Explore a collection of data analytics and machine learning projects, paired with extensive documentation, detailed explanations of methodologies, and visualizations, to help you gain a deeper understanding of the data.

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Data Analysis Skillset

Python Utilizing Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn etc.
R Using ggplot2, diplr, tidyr, stringr etc.
SQL Executing Joins, Subqueries, CTE, Running Totals, Window Functions, DateTime Manipulation etc.
Excel Implementing Pivot Tables, VLOOKUP, HLOOKUP, XLOOKUP, Data Validation, etc.
Visualization Sythesizing insights into Dashboards, Reports, Presentations, etc.
Machine Learning Leveraging and optimizing Regression, Clustering, Classification Algorithms.

Data Analysis

Explore my how I analyze data using Python, R and SQL

Data Analysis With Python

Building and evaluating data models using Python

Data Analysis With R

Analyzing and visualizing data using R

Data Analysis With SQL

Investigating and examining datasets using SQL

Machine Learning

Leveraging ML algorithms to make predictions, forecasts and classifications

Machine Learning With Python

Demystifying complex data by deploying ML using Python

Methodology

A systematic approach to complex data

Exploring data is like a treasure hunt - you dive into the data, reveal connections, and uncover hidden insights that lead to actionable outcomes. Every analysis follows a general methodology that can be broken down into these steps:

5. Data Modelling

Next, we move on to more complex analytical techniques (like hypothesis testing, predictive modeling, etc.) to draw conclusions and make predictions based on our data. We implement a statistical or machine learning model to help answer the research question or solve the problem.

When using machine learning, a few extra steps are involved:

  • Test and Train the Model

    • Choose an appropriate machine learning model based on the data type and the desired prediction outcome. Next, train the model on a subset of the data and evaluate its performance against new data.

  • Model Optimization

    • Modify the model to enhance its predictive ability. If required, experiment with different models, test and train them to improve the result accuracy.

6. Draw Conclusions and Communicate Results

Finally, we summarize our findings into a cohesive narrative about the data. Then we present a compelling story to stakeholders, through reports, presentations, or dashboards, to communicate key insights, highlight potential outcomes, and provide actionable recommendations for decision-making.