Ashish Tammana's PortFolio

Who Am I

Hello, I am Ashish Tammana, and I am Passionate About Data Analysis!.

Welcome to my data analyst portfolio! I am an enthusiastic and dedicated data analyst with a deep fascination for transforming raw data into actionable insights. With a background in Master of Science in Information Technology Track: Data Management and Analytics , I possess a robust set of skills in data manipulation, statistical analysis, and data visualization. My journey into the world of data began with a curiosity to unravel the stories hidden within numbers, and today, I stand as a skilled data analyst ready to tackle complex challenges..

Analytical Mindset

I pride myself on my analytical mindset, which enables me to dissect complex datasets and derive meaningful conclusions. I have a keen eye for patterns and trends, allowing me to provide valuable insights that drive informed decision-making. My approach involves not just crunching numbers but understanding the context behind the data, ensuring that the analyses I provide are not only accurate but also relevant to the goals of the organization..

Technical Proficiency

Proficient in languages such as Python and R, I have a strong foundation in programming and data manipulation. My expertise extends to various data analysis libraries and tools, including Pandas, NumPy, and SQL. Moreover, I am adept at creating visually compelling and informative dashboards using visualization tools like Tableau and Power BI. I believe in the power of data visualization to convey complex information in a simple and engaging manner..

Problem-Solving Skills

I thrive on solving intricate problems using data-driven techniques. Whether it's optimizing business processes, predicting market trends, or understanding customer behavior, I approach each challenge with a curious mind and a determination to find innovative solutions. My problem-solving skills are complemented by my ability to communicate complex findings effectively, ensuring that stakeholders grasp the insights derived from the analyses..

Continuous Learner

The world of data is ever-evolving, and I am committed to continuous learning. I stay abreast of the latest developments in the field of data analysis and actively seek new opportunities to enhance my skills. My passion for learning drives me to explore new statistical models, machine learning algorithms, and data visualization techniques, ensuring that I remain at the forefront of the data analytics landscape..

Let's Dive into Data Together

Whether you're looking for actionable insights from your business data or seeking to optimize your processes using data-driven strategies, I am here to help. Let's embark on a data-driven journey together, where we transform raw data into meaningful narratives that propel your business forward..

Thank you for visiting my portfolio. I look forward to the opportunity to collaborate and contribute to your data-driven success!.

Warm regards,

tammanaashish0@gmail.com

+1 (510) 941-8827

linkedin.com/in/ashish-tammana

Work

2.Customer Personas 2.0 Revitalizing Marketing Campaigns through Clustering

In this data mining project, customer data from a Kaggle dataset is analyzed using Cluster Analysis, Hierarchical Dendrograms, and K-means Clustering for targeted marketing. The 'Recency' variable was removed for efficiency. Customers, evenly spread across income ranges, were clustered into groups. This approach has broad applications in sectors like supermarkets and corporations, optimizing marketing strategies by identifying customer clusters with similar traits.

In this analysis, Python was used for hierarchical and K-means clustering. Five distinct clusters were identified, each showcasing unique spending patterns and preferences. "Luxury Enthusiasts" prioritize high-end products like wine and gold without requiring discounts. "Online Luxury Shoppers" prefer digital channels, especially for luxury items. "Store Shoppers" have a preference for in-store experiences, while "Discount-Driven Shoppers" seek savings, making them receptive to discounts, especially on fruits. These findings offer valuable insights for targeted marketing strategies.

Work

2.Decoding Real Estate: Unveiling Market Dynamics through Data Analysis and Insights

Introduction to Real Estate Market Analysis Project

In the bustling landscape of today's real estate market, data-driven insights have become the bedrock of informed decision-making. Our data analysis project delves deep into the intricacies of the real estate market, leveraging cutting-edge technologies and tools to extract meaningful patterns from a sea of information. Using Python and Jupyter Notebook, in combination with powerful libraries such as Statsmodels, NumPy, Scikit-Learn, Seaborn, and Matplotlib, we embark on a comprehensive analysis journey. Our methodology encapsulates crucial stages: from meticulous data collection and rigorous data cleaning processes to unveiling the hidden narratives through exploratory data analysis (EDA) and culminating in insightful predictive analyses.

Python-Powered Real Estate Insights: Navigating Market Trends with Data Science Mastery

In this endeavor, Python stands as the cornerstone of our project, providing the flexibility and computational prowess essential for processing vast datasets. Jupyter Notebook, our interactive computing environment, empowers us to blend code, visualizations, and textual explanations seamlessly, fostering a holistic understanding of the real estate dynamics. Through the adept utilization of Statsmodels, we dive into statistical modeling, discerning intricate relationships between variables. NumPy, the fundamental package for scientific computing, lends its efficiency to array operations and mathematical functions, amplifying the precision of our analyses. Scikit-Learn, a robust machine learning library, becomes instrumental in predictive modeling, allowing us to build and evaluate sophisticated algorithms. Seaborn and Matplotlib, with their captivating visualizations, transform raw numbers into compelling stories, enabling stakeholders to grasp the market trends intuitively. Our journey traverses the realms of data collection's meticulousness, data cleaning's precision, EDA's revelations, and predictive analysis's foresight, painting a comprehensive portrait of the real estate market's landscape

Work

3.Automating Crypto Website API Pull Using Python | Data Analysis Project

Introduction

In the world of cryptocurrencies, staying updated with real-time data is crucial for informed decision-making. This report outlines a data analyst project where we automate the process of pulling cryptocurrency data from a website using Python and relevant APIs. The project aims to provide a streamlined solution for data analysts and enthusiasts to access up-to-the-minute information on various cryptocurrencies.

Objectives

Automate the process of pulling cryptocurrency data. Extract and store real-time data using APIs. Visualize cryptocurrency market trends. Create a user-friendly tool for data analysis. Methods: Python Programming: Python is a versatile programming language used to automate tasks, including data collection and analysis. APIs: Application Programming Interfaces (APIs) are used to retrieve data from websites. In this project, we will be using APIs provided by cryptocurrency websites to collect data. Data Storage: Data collected will be stored in a suitable format, such as CSV, to facilitate analysis. Data Visualization: Data analysis and visualization libraries such as Pandas, Matplotlib, and Seaborn will be employed to create insightful visualizations.

Work

4.Airbnb Seattle Data Analysis: Building a Comprehensive Tableau Dashboard

Project Overview

Certainly! This project aims to create an interactive and informative Tableau dashboard using the Airbnb Seattle dataset. The dataset contains detailed information about Airbnb listings, reviews, and booking calendars, providing rich data for analysis. The primary objective is to develop an attractive dashboard that highlights insights into Seattle's Airbnb market. Although beginner-friendly, the project covers essential concepts like data joining, visualization creation, and interactive dashboard design. Participants are encouraged to watch previous tutorial videos to understand fundamental Tableau concepts. Despite its simplicity, the project provides a strong basis for building more intricate visualizations and analyses in the future.

Work

5.Walmart Sales Data Analysis

Project Overview

This project aims to explore the Walmart Sales data to understand top performing branches and products, sales trend of of different products, customer behaviour. The aims is to study how sales strategies can be improved and optimized. The dataset was obtained from the Kaggle Walmart Sales Forecasting Competition.

"In this recruiting competition, job-seekers are provided with historical sales data for 45 Walmart stores located in different regions. Each store contains many departments, and participants must project the sales for each department in each store. To add to the challenge, selected holiday markdown events are included in the dataset. These markdowns are known to affect sales, but it is challenging to predict which departments are affected and the extent of the impact."

Work

6.Analyzing Income and Bike Purchase Patterns: Key Insights for the Bike Industr

This report analyzes income and bike purchase patterns based on a dataset encompassing income, gender, age groups, and preferred bike purchase distances. The key findings provide valuable insights for businesses in the bike industry, aiding in marketing, product placement, and pricing decisions..

Income Analysis: The data reveals that males generally have slightly higher incomes than females, with an overall average income of approximately $56,360. This data can guide pricing and marketing strategies based on income demographics.

Bike Purchase by Distance: The majority of bike purchases (366 out of 1000) occur within a 0-1 mile distance, indicating a preference for proximity. Businesses can consider this when deciding store or distribution center locations.

Bike Purchase by Age Group: Middle-aged individuals (25-65 years) dominate bike purchases, making up 719 out of 1000 purchases. This data informs product and marketing strategies for specific age groups.

Bike Purchase by Age: The late 20s and early 30s age group shows the highest bike purchases, peaking at age 32. Purchases decrease with age, particularly among older individuals. Businesses can use this information to target their products and marketing effectively.

Conclusion: This data-driven analysis offers actionable insights for businesses in the bike industry. It helps in tailoring strategies for income groups, location choices, and age-specific marketing. However, it's important to consider that further research and additional variables may be required for a comprehensive understanding of customer behavior. For in-depth analysis and specific business goals, additional data and advanced statistical methods may be necessary.

Contact

• linkedin
• Github

Elements

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