My passion is understanding the numbers and trends behind the Buisness Model using data.

Table of Contents

📜 Certifications

1. Google Data Analytics Specialization (Coursera)

  • Gained an immersive understanding of the practices and processes used by a junior or associate data analyst in their day-to-day job

  • Learnt key analytical skills (data cleaning, analysis, & visualization) and tools (spreadsheets, SQL, R programming, Tableau)

  • Understood how to clean and organize data for analysis, and complete analysis and calculations using spreadsheets, SQL and R programming

  • Learnt how to visualize and present data findings in dashboards, presentations and commonly used visualization platforms

GAC

2. Python Certification (Hackerrank)

  • Covers topics like Scalar Types, Operators and Control Flow, Strings, Collections and Iteration, Modularity, Objects and Types and Classes

HR

3. Basics of Data Science (UpGrad)

  • Worked on Real‑Life Case Studies & Complications during the course of Training.
  • Completed 3 Projects/Assignments on time during the span of 2 months.
  • Studied about Tableau Desktop and made Visualizations using the tool.
  • Learnt Data Analysis Using Microsoft Excel, Python

UG

4. Trainity Virtual Internship

  • Worked on Business Case Studies & Problems during the course of Training.
  • Completed 6 tasks and modules during the span of 8 weeks.
  • Segmented the data set provided and drew unique insights using visualization tools.
  • Learned Excel, Python in depth.

UG

5. Trainity Data Analytics Specialized

UG

6. The Data Science Course (Udemy)

UG

✏️ Skills

  • Programming: Python (Pandas, Numpy, sklearn, SciPy), SQL (MySQL), R
  • Data Visualization: Tableau Desktop, Matplotlib, Seaborn
  • IDEs: Jupyter Notebook, Visual Studio Code, RStudio
  • Applicable Software: Tableau Prep Builder, Microsoft Office (Word, Excel, PowerPoint)

💻 Portfolio Projects

1. Case Study: How Does a Bike-Share Navigate Speedy Success ? 🔗

  • This is Captone Project for Google Data Analytics Professional Certificate.
  • Analyzed a total of 16 Datasets of 1 GB.
  • Segmented analysis in 6 phases.
  • Cleaned & Prepared Datasets for Analysis using Python containing 6,044,091 entries.
  • Analyzed and Visualized Data in 6 Different Graphs with the help of Tableau Desktop.
  • Provided 5 Key-Takeaways and 3 Recommendations.
  • For project in detail, visit this project link.

2. Case Study: How Airbnb can attract Tourists in NYC ? 🔗

3. Movies Recommendation System 🔗

  • Built a Model using Python for Recommending Movies over a data of 5000 Movies.
  • Used “TMDB.com” API for requesting 5 Movie Posters for each movie recommendation search.
  • Deployed & Hosted the Application on “Heroku.com”.
  • For project in detail, visit this project link.

4. Laptop Price Predictor 🔗

  • Built a Model using Python for Predicting Prices of Laptops based on different user inputs.
  • Used this dataset containing information of 1300 different laptops.
  • Deployed & Hosted the Application on “Heroku.com”.
  • For project in detail, visit this project link.

5. Weather Application 🔗

  • Built an application for representing different weather related information based on user input.
  • Used Open Weather Map API for requesting weather related informations of different cities around the globe.
  • Deployed & Hosted the Application on “Heroku.com”.
  • Project Link - https://ssk-weather-app.herokuapp.com/

6. Credit Card Fraud Detection Model 🔗

  • Built a model on Kaggle for detecting whether a transaction is Legit or Fraud.
  • Analyzed a total of 2,84,807 transactions information.
  • Segmented analysis in 6 phases.
  • Cleaned & Prepared Datasets for model training which included under-sampling of dataset.
  • Analyzed and splitted the dataset into Train & Test data using sklearn library.
  • Checked for the accuracy score of Predicted data given by the model.
  • For project in detail, visit this project link.

7. Customer Segmentation using KMeans 🔗

  • Project to segment customers of a mall on the basis of their gender, age, annual income & spending score using KMeans.
  • Analyzed a total of 200 entries of dataset.
  • Segmented analysis in 5 phases.
  • Cleaned & Prepared the Dataset.
  • Analyzed and divided dataset into ranges and visualized it on graph.
  • Checking for relationships.
  • Finding the numbers of clusters needs to be created
  • Visualizing the clusters on graphs
  • For project in detail, visit this project link.