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https://github.com/ayushverma135/cognizant-artificial-intelligence

The Cognizant AI Job Simulation provided hands-on experience in data analysis and machine learning, simulating real-world tasks from Cognizant’s Data Science team to derive and present business insights.
https://github.com/ayushverma135/cognizant-artificial-intelligence

cognizant forage python sckiit-learn

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The Cognizant AI Job Simulation provided hands-on experience in data analysis and machine learning, simulating real-world tasks from Cognizant’s Data Science team to derive and present business insights.

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# Cognizant Artificial Intelligence Job Simulation - September 2023



## Overview

The Cognizant Artificial Intelligence Job Simulation was a practical exercise designed to provide participants with hands-on experience in data analysis and machine learning. The simulation aimed to mimic real-world tasks that might be encountered as part of Cognizant’s Data Science team. The focus was on applying analytical skills to derive insights from data and presenting those insights in a business context.



## Project Summary

The project involved working with a dataset provided by one of Cognizant’s technology-led clients, Gala Groceries. The task was to conduct exploratory data analysis (EDA), build a machine learning model, and communicate the findings effectively.



## Key Responsibilities and Deliverables



1. **Exploratory Data Analysis (EDA)**  

   - Utilized Python and Google Colab to explore and analyze the provided dataset.

   - Conducted data cleaning, transformation, and visualization to identify key trends and patterns.

   - Analyzed features and relationships within the data, identifying any anomalies or outliers that might affect the model's performance.



2. **Machine Learning Model Development**  

   - Developed a Python module that encapsulated the entire machine learning workflow, from data preprocessing to model training and evaluation.

   - Selected appropriate machine learning algorithms based on the data characteristics and project requirements.

   - Trained the model and evaluated its performance using relevant metrics such as accuracy, precision, recall, and F1 score.



3. **Performance Metrics and Evaluation**  

   - Assessed the model's performance through rigorous testing and validation.

   - Documented the performance metrics, including a detailed explanation of the chosen metrics and their significance in the context of the project.



4. **Communication of Findings**  

   - Created a PowerPoint presentation to summarize the findings and insights derived from the analysis.

   - The presentation included visualizations, key metrics, and actionable insights that could inform business decisions for Gala Groceries.

   - Emphasized the business implications of the data insights and suggested potential next steps for leveraging the model in a real-world scenario.



## Steps to Acheive this Simulation

1. **Task One**: Exploratory Data Analysis
 (*Exploring customer data to identify next steps*)




2. **Task Two**: Data Modeling
 (*Understanding relational data and framing a problem statement*)




3. **Task Three**: Model Building and Interpretation
 (*Building a predictive model and interpreting the results back to the business*)




4. **Task Four**: Machine Learning Production
 (*Developing machine learning algorithms for production*)




5. **Task Five**: Quality Assurance
 (*Evaluating the production machine learning model to ensure quality results*)




## Technical Skills and Tools Utilized



- **Programming Languages:** Python

- **Data Analysis Tools:** Pandas, NumPy, Matplotlib, Seaborn

- **Machine Learning Libraries:** Scikit-learn

- **Development Environment:** Google Colab

- **Visualization Tools:** PowerPoint, Matplotlib, Seaborn



## Key Learnings and Takeaways

- Gained practical experience in handling real-world datasets and conducting exploratory data analysis.

- Developed a comprehensive understanding of the end-to-end machine learning pipeline, from data preprocessing to model evaluation.

- Enhanced skills in communicating technical findings to a non-technical audience, emphasizing the importance of clear and concise reporting.

- Learned to tailor machine learning solutions to meet specific business needs and objectives.



## Add to your resume

```

Cognizant Artificial Intelligence Job Simulation on Forage - September 2023

- Completed a job simulation focused on AI for Cognizant’s Data Science team.

- Conducted exploratory data analysis using Python and Google Colab for one of Cognizant’s technology-led clients, Gala Groceries.

- Prepare a Python module that contains code to train a model and output the performance metrics for the Machine Learning engineering team.

- Communicated findings and analysis in the form of a PowerPoint slide to present the results back to the business.

```

## Add skills to your resume

```

COMMUNICATION

DATA ANALYSIS

DATA MODELING

DATA VISUALIZATION

DEVELOPMENT

EVALUATION

MACHINE LEARNING

MACHINE LEARNING ENGINEERING

MODEL INTERPRETATION

PROBLEM STATEMENT

PYTHON

QUALITY ASSURANCE

```

## Interview tip

```

“Why are you interested in this role?”

I recently participated in Cognizant’s job simulation on the Forage platform, and it was incredibly useful to understand what it might be like to participate in a Data Science team, to work with Python and Google Colab in a realistic context and to produce, evaluate and improve a production machine learning model. 



Through this program I realized that I really enjoy researching and improving the performance of machine learning models and would love to apply what I've learned in Cognizant’s Data Science team. 

```

### Conclusion

The Cognizant Artificial Intelligence Job Simulation provided a valuable opportunity to apply theoretical knowledge to practical problems. The experience honed both technical and communication skills, preparing participants for real-world challenges in the field of data science and artificial intelligence.