Ehijie Collins Agbadu

Data Analysed By: Ehijie Collins Agbadu Project Title: Data Analysis Report for Excelerate Internship Feedback Project Objective: To analyse the dataset received from past internships, focusing on key metrics, percentages, final observations, and potential improvements. Project Data Overview: The dataset was downloaded from the company website as instructed. It includes feedback from various internship programs conducted by the company. The dataset contains quantitative and qualitative data on different aspects of the internship experience, such as intern satisfaction, skill development, and overall program success. Exploration Data Analyst (EDA) After downloading the dataset from the company website in CSV format, it was loaded into Jupyter Notebook for Exploratory Data Analysis (EDA). Python, along with its libraries—NumPy, Pandas, Matplotlib, and Seaborn—was used throughout the analysis. The first step involved checking for missing values to determine if any data was missing. It was confirmed that there were no significant missing values that would affect the analysis. data.isna() # This checks for Nan values across each column in the dataset Secondly, the dataset was checked for duplicates to ensure that none would affect the analysis. It was confirmed that there were no duplicates that could have impacted the results. This indicates that our dataset is clean and ready for the intended analysis Quantitative Analysis: Key metrics: After a thorough review of the dataset, the following metrics were selected for analysis to enhance our findings: 'How likely are you to recommend this internship to a friend or colleague?', Age, Gender, 'Which internship have you completed?', 'Please describe your favorite aspects of this internship. Consider: Were there particular activities or resources that you found most helpful? What was most enjoyable for you? Did you learn something new?', and 'Please describe areas of this internship that you think can be improved. Consider: What do you think was missing? What was confusing? Was there any particular resource or material that you didn’t find helpful? Quantitative Analysis: Calculate the average satisfaction score: The Average satisfaction score was 8.36/ 10. The score was a pass mark for the internship. Barn Chart Distribution for Satisfaction Scores: A Lot of persons who participated in the internship showed that they would recommend the program to their friends. Calculate the Percentage of Interns Who Felt Their Skills Improved: Pie Chart for Skills Improvement: A Great number of the internship showed that their skills improved in participating in the internship. Determining the Age group that participated in the internship feedback: The age group that participated most in the internship was between 15 and 30, with 21 being the most common age among participants. Determining the Distribution of Gender in the internship feedback: The Male has the highest number of participants. Distribution of internship completion: From this chart, we can see that Data Visualization, Cyber Security & Defensive Hacking, and Project Management have the highest completion rates. Analyse Qualitative Data: Identify Common Themes in Open-ended Feedback: Output: Categorise feedback into strengths and areas for improvement. As we can see, from the analysis about categorizing the feedback through the counting of like words from areas of Strength and area improvement we that the company has slight area strength than the area improvement. This means the company has more work to do to improve its internship program. Summary: The analysis provides insights into participants' satisfaction and perceived skills improvement from the internship program. 1. Average Satisfaction Score: o Participants reported an overall average satisfaction score of 8.36 on a scale, indicating a generally high level of satisfaction with the program. 2. Skills Improvement: o 41.5% of participants felt that their skills improved "A great deal." o 35.5% reported that their skills improved "A lot." o 14.0% indicated "A moderate amount" of improvement. o 4.5% experienced a combination of "A great deal" and "A lot" of improvement. o Smaller percentages of participants reported less significant improvements:  2.5% experienced only "A little" improvement.  1.5% did not experience any improvement ("None at all").  0.5% reported a combination of "A lot" and "A moderate amount" of improvement Interpretation: The data suggests that the majority of participants (over 77%) experienced substantial skills improvement, with "A great deal" and "A lot" being the most common responses. The high average satisfaction score supports this positive feedback, indicating that the program was generally successful in enhancing participants' skills and overall experience. The small percentages of participants reporting minimal or no improvement suggest areas where the program might be refined to better meet all participants' needs.