About Me
About me
Hello Everyone, I am Abhishek Hiremath.
I am a Data Science enthusiast with a curiosity to look beyond the known and a hunger for learning. I was introduced to data analytics in an Economics course in my under graduation where I got invested in data collection and analysis which was majorly concerned with customer behavior analysis, macro and micro market impacts. Since then I have completely submerged myself into this exciting and unending roller-coaster of learning and practicing concepts of Analytics.
I have worked in projects involving Data collection(web scraping), Database Management, Data wrangling, Data Analysis, Data cleaning, Data Visualization, Exploratory Data Analysis, Business Intelligence dashboards and Data Mining. They also involved Machine Learning concepts like Supervised and Unsupervised Modeling, Image and Face Recognition, Hyperparameter Tuning, Feature selection/engineering, Recommendation System etc.
These projects helped me grow my knowledge in tools and scripting languages like:
| Tools | Description |
|---|---|
| Databases | SQL, SSIS, MySQL, Hive, DynamoDB, Google Big Query ,MongoDB |
| Software | Tableau, PowerBI, Alteryx, OBI, QlikView, Weka, Visio, Google Analytics |
| Cloud Platforms | AWS , Azure, DynamoDB |
| Management Tools | Microsoft Office Suite, JIRA, Confluence, Airtable, Google Suite, Github , Google Ads , |
Link for my Resume
Masters of Science in Management of Technology New York University, New York, USA.
In two years of my MS I learned and grew in key technical areas such as:
Statistics for Data Analytics: Exploring underlying mathematical foundation of descriptive statistics, probability, and hypothesis testing, also covered regression analysis and time series analysis with an emphasis on model formulation and interpretation of results.
Business Intelligence Analysis: Concepts and best practices for corporate Dashboard reporting with use of BI tools like Advance excel, Power BI, Tableau and Alteryx.
Data Analytics with R and Python: Solving business problem case with making use of available data and best practices in Descriptive, Predictive, and Prescriptive analysis.
Data Visualization: Story telling with aesthetically pleasing Visualization and best practices to narrate a data story to varying audiences.
Data Mining: Machine Learning concepts and practices used in large organizations to make the best use of growing data availability.
Data Engineering: Theoretical and practical implementation of Database management and Graph Network theory with business cases in different domains ranging from economics and agriculture to stock market.
Corporate Finance and Accounting: Advance concepts of Finance and Accounting with financial analysis and modelling on real world case studies dealing with merger and acquisitions and forecast value evaluation. Case Study
Industrial Economics and strategy: Studied concepts and impacts of Macro & Micro economy, consumer behavior, economic value creation and Value-based strategies. Learned analysis techniques like Five Forces Model, Demand and Supply Analysis, Porter’s Competitive Strategy Framework PEST analysis and SWOT analysis. Netflix Research Paper
Business Courses: Project Management (SCRUM Practices and understanding Waterfall and Agile methodologies), Global Innovation (Strategies to manage and coordinate Global projects) Target Global Business Research Paper, Management of Information System, Organizational behavior.