Sithara Sreeramachandra

• Currently holding 3.94/4 Grade Point Average (GPA)
• Completed the below courses -

1. Data Management for Business Leaders (SCH-MGMT 601)- Coursework - Use of MS Excel and learn to load and wrangle data using basic IF's and aggregate functions, Lookup formulas, Pivot tables, Power Query and Power Pivot, VBA-Macros.

2. Business Intelligence & Analytics (SCH-MGMT 602) - Coursework - Use of MS Access to understand SQL and data normalization.

3. Data Science for Business Leaders (SCH-MGMT 657) - Coursework - Predictive Modeling(Supervised Segmentation & Logistic Regression), Classification, Clustering, Model Performance Analysis, Exploratory Data Analysis along with case studies.

4. Statistics for Business (SCH-MGMT 650) - Coursework - Probability Distributions, A/B testing case examples, Hypothesis Testing, Sampling, Normal Distributions, ANOVA, Simple & Multiple Linear Regression

5. Project Management (SCH-MGMT 656) - Project management techniques, terminology and trends, define project success
measures and methods, analyze quality, cost and efficiency with the project management process. Case study on being a hypothetical project manager, how would I handle the situation - using Microsoft Project software.

6. Data Mining (SCH-MGMT 655) - Coursework - Data retrieval, manipulation and visualization. Machine learning techniques like classification, prediction, time-series forecasting, cluster analysis, kNN, decision trees. Neural Networks. Use of statistical analysis in predictive modeling and hands on case study for each topic.

7. Business Application Development (SCH-MGMT 609) - Coursework - Introduction to python - lists, tuples, conditional statements and loops, json requests to extract data from web, Querying databases using python, Cleaning and manipulating data with NumPy and Pandas, Data visualization using Matplotlib and Seaborn libraries. Linear Modeling with scipy library and statsmodels using Simple Regression, Multiple Regression.

8. Web-Analytics (SCH-MGMT 662) - Coursework - Fundamentals of digital marketing and web analytics, case studies on understanding search engine optimization (SEO), search
engine marketing (SEM), and display advertising. Understand and implement KPIs in marketing campaigns.

• Professional Development: "Career Pro & Development" course that prepares us with importance of resume building, cover letter, framing LinkedIn profiles and networking activities and team building.
• Relevant Academic Projects (Master's Coursework) -

1. Built a manual entry macro in MS Excel using VBA functionality where we enter the details of a location with its latitudes and longitudes and solve traveling salesman problem in calculating shortest and efficient route.

2. Analyzed Adidas sales dataset to identify the particulars of which products contribute to 20% of sales that give 80% of profits using pareto analysis considering parameters like gender, product category, location, discount percentage. Evaluated seasonal trends and performed time series forecasting to predict sales of each product category in major cities.

3. Developed a forecasting model utilizing Bayesian probabilistic concepts and regression analysis to predict the chances of surpassing the Dow Jones Industrial Average stock rate beyond 40,000.

4. Utilized classification analysis techniques, including K-Means clustering, Random Forest, and Naive Bayesian models, to comprehensively assess various approaches for automating the evaluation of customer eligibility, aiming to predict loan approvals and denials with precision and efficiency.

5. Use of Google Analytics and Google Ads to measure website and ad campaign performance.

6. Crafted a comprehensive project spanning from conception to execution for an AI Marketing tool launch, meticulously planning and distributing resources across planning, design, business, sales, and software development over a one-year timeline, utilizing Microsoft Project for resource and budget allocation, while also creating project charters and interim closeout reports.

7. Deployed the Zomato restaurant dataset within the R Studio environment and SAP Analytics Cloud to predict the average cost incurred by two individuals dining at a restaurant. This predictive analysis factored in various parameters including location, cuisine type, rating, and the number of reviews, enabling a nuanced understanding of dining expenditure trends and customer behavior patterns.

• Secured 8.77/10 Cumulative Grade Point Average (CGPA)
• Most relevant Analytical Industry based Coursework:

1. Production Technology

2. Industrial Production & Engineering

3. Statistics for Engineers

4. Management & Entrepreneurship

5. Operation Research (Basic - Intermediate - Advanced)

6. Quality Assurance & Control Checks

7. Engineering Economy

8. Product Design & Development

9. Product Management

10. Simulation Modelling & Analytics

11. Financial Account & Costing

12. Productions & Operations Management

13. Supply Chain & Logistics Management

14. Decision Modeling Lab

15. Enterprise Resource Planning (ERP)

16. Total Quality Management

17. Industrial Design & Ergonomics

18. Lean Manufacturing - Six Sigma

19. Project Management

• Most relevant industry related projects -

1. Enhanced the efficiency of a vegetable vendor's supply chain by leveraging WhatsApp as a platform to connect with daily customers, facilitating continuous orders and ensuring consistent sales, while also establishing a network with customers to foster better relationships. Additionally, utilizing WhatsApp reduced physical strain by minimizing time spent pushing carts. Moreover, this method facilitated informed procurement decisions by providing insights into supply and demand dynamics, while also offering detailed data on WhatsApp regarding quantities, prices, and payments, streamlining overall operations.

2. Created and engineered an innovative groundnut pod plucking device. Successfully constructed a functional prototype, demonstrating a remarkable efficiency rate of 92% in reducing time consumption. Conducted in-depth analysis of the product development life cycle, ensuring alignment with customer needs and preferences. Employed cutting-edge technologies and materials to optimize performance and durability, resulting in a highly competitive and marketable solution.