Data Analysis: Developer 101 | A Step Forward with Flutter
A Crisp analysis over available data from students registrations and attendees information from GoToMeeting
Developer 101 | A Step Forward with Flutter
The following notebook is an analysis of an online webinar organised by Sathyabama Coding Club
- The data used here is private
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")
%matplotlib inline
reg_data = pd.read_excel("Developer 101 _ A step Forward with Flutter (Responses) (1).xlsx")
ui_data = pd.read_excel("Developer 101 _ A Step Forward with Flutter Attendees_Cleanded_data.xls")
SESSION_DURATION = ui_data['Time in Session (minutes)'].max()
print("Session Duration in Minutes: ", SESSION_DURATION)
no_of_regs = len(reg_data)
REG_COUNT = no_of_regs
print("No of Registrations : ", REG_COUNT)
reg_data.Batch.value_counts()
sns.countplot(x="Batch", data=reg_data)
plt.title("Barplot on Academic year participation")
sns.countplot(x="Have you ever built a web/mobile application?", data=reg_data)
plt.title("Barplot over the background of participants in web/mobile app devlopment")
web_mob_yes = reg_data["Have you ever built a web/mobile application?"].value_counts()[1]
web_mob_no = reg_data["Have you ever built a web/mobile application?"].value_counts()[0]
print("The percentage of people joined the webinar who has prior exprience on Web/Android Dev :",
(web_mob_yes/no_of_regs)*100)
print("The percentage of people joined the webinar who has no prior exprience on Web/Android Dev :",
(web_mob_no/no_of_regs)*100)
sns.countplot(x="Knowledge of Dart programming language ",
data=reg_data)
reg_data["Where do you wish to use Flutter?"].value_counts().plot(kind='barh')
Observation:
- Majority of the participants Wished for Learning Flutter for Mobile App Development
ui_data.head()
ATTENDEES_COUNT = len(ui_data['Name'].value_counts())
ATTENDEES_COUNT
len(ui_data.groupby(by=ui_data.Name, axis=1).sum())
ui_data.groupby(['Name', 'Time in Session (minutes)']).sum().iloc[:,:0]
(ui_data['Time in Session (minutes)'] == ui_data['Time in Session (minutes)'].iloc[0]).all()
def time_agg(group_series):
if (group_series==group_series.iloc[0]).all():
return group_series.iloc[0]
else:
return group_series.sum()
ui_data.groupby('Name', as_index=False).agg(time_agg)[['Name', 'Join Time', 'Leave Time', 'Time in Session (minutes)']]
atten_group_df = ui_data[['Name', 'Time in Session (minutes)', 'Email Address']].groupby('Name', as_index=False).agg(time_agg)
atten_group_df.sort_values(by=['Time in Session (minutes)'],ascending=False, inplace=True)
sns.factorplot(x="Name", y="Time in Session (minutes)",
data=atten_group_df, kind="bar",
size = 15, aspect=2,
palette = "muted")
# for value in plot:
# height = value.get_height()
# plt.text(value.get_x() + value.get_width()/2.,
# 1.002*height,'%d' % int(height), ha='center', va='bottom')
plt.xticks(rotation=45);
Individual time spent analysis of attendes
sns.factorplot(x="Name", y="Time in Session (minutes)",
data=atten_group_df[atten_group_df["Time in Session (minutes)"] >= SESSION_DURATION//2],
kind="bar",
size = 8, aspect=2,
palette = "muted")
plt.xticks(rotation=45);
atten_group_df[atten_group_df["Time in Session (minutes)"] >= SESSION_DURATION//2][['Name', 'Time in Session (minutes)']].set_index('Name')
atten_group_df
len(atten_group_df[atten_group_df["Time in Session (minutes)"] >= SESSION_DURATION//2].set_index('Name')['Time in Session (minutes)'])
registerd_attendes_ratio = (ATTENDEES_COUNT/REG_COUNT) * 100
print("Percentage of Students registered and attended the session {}".format(registerd_attendes_ratio))
Registration Data Analysis
- Name of the Event: Developer 101 | A Step Forward with Flutter
- No of registrations: 73
- Registration Count with Batch filter
- 2022 : 27
- 2021 : 22
- 2023 : 13
- Professional : 11
- No of registrations With out prior knowledge on web/app Development : 50 [68.4931506849315%]
- No of registrations With prior knowledge on web/app Development : 23 [31.506849315068493%]
- No of registrations who are Beginners in Dart Programming language : 61
- No of registrations who are Beginners in Dart Programming language : 12
- Registrations wish to use Flutter for
- Mobile app development | 38
- Web app development | 13
- Mobile app development, Web app development, Desktop app development | 12
- Mobile app development, Web app development | 9
- Mobile app development, Desktop app development | 1
Webinar Attendes Data Analysis
- No of Attendees: 45
- No of students spent more than half in the session: 27
- Percentage of Students registered and attended the session 61.64383561643836%
- Students spent more than 55 minutes in the session
Teja Kummarikuntla Sriram Krishna Suchismita Chakraborti You Know Who I Am reconnecting.... Rohit Ayush Shekhar neeraj jayaram Mohit Kumar Patnaik Abhishek Kumar Jiss Johnson Akash M Maithili Antariksh Pratham PALAKULA SAI TARUN Abhiram Swarnima Shishodia AJ soundharya Dewashish Kumar Aishwarya Pangarikar dhruv Sonal Agrawal HAMID HUSSAIN Vaibhav Mishra Surya Deepta Mazumdar