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Welcome to your personalized data journey! This notebook is designed to help you analyze your DataCamp progress over the past year, so you can set a goal, build a habit, and take actionable next steps for 2026. Dive into your learning data, including exercises completed, workbooks created, and more. Discover insights, track your habits, and set new goals for the upcoming year. Let's explore your year in data together!

🧭 Get familiar and explore your data by opening the context panel

In the View menu, click on Show workbook files to view the files context panel. Expand the 'data' folder to view all available files.

🎯 Challenge 1: Set a Goal

Set your XP milestones: Using the xp_earned data, find your biggest XP-earning days. Use this insight to set specific XP milestones for 2026 that keep you motivated.

1️⃣ Below you will find an example, run this cell to see what is possible...

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

# Read in the data
xp_earned = pd.read_csv('data/xp_earned.csv', parse_dates=['earned_at'])

# Ensure the 'date' column is in datetime format
xp_earned['date'] = pd.to_datetime(xp_earned['earned_at'])

# Create columns for week number and day of the week
xp_earned['week'] = xp_earned['earned_at'].dt.isocalendar().week
xp_earned['day'] = xp_earned['earned_at'].dt.dayofweek

# Create a DataFrame to ensure all days and weeks are represented
all_weeks = range(1, 53)  # Weeks 1 to 52
all_days = range(0, 7)    # Days 0 (Monday) to 6 (Sunday)
full_index = pd.MultiIndex.from_product([all_days, all_weeks], names=['day', 'week'])

# Sum XP earned for each day and week, reindex to fill missing values with 0
heatmap_data = xp_earned.pivot_table(index='day', columns='week', values='xp_amount', aggfunc='sum', fill_value=0)
heatmap_data = heatmap_data.reindex(index=all_days, columns=all_weeks, fill_value=0)

# Create the heatmap
plt.figure(figsize=(20, 4))
sns.heatmap(heatmap_data, cmap='Greens', linewidths=.5, linecolor='gray', cbar_kws={'label': 'XP Earned'})

# Set the labels
plt.title('XP Earned Activity Heatmap', loc='center')
plt.xlabel('Week of the Year')
plt.ylabel('Day of the Week')
plt.yticks(ticks=np.arange(7) + 0.5, labels=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'], rotation=0)

# Display the heatmap
plt.show()

2️⃣ Now make it your own!

xp_earned = pd.read_csv('data/xp_earned.csv', parse_dates=['earned_at'])
xp_earned.head()

## complete challenge 1, be ambitious

🗓️ Challenge 2: Track Your Habits

Track your learning streaks: Use the streaks data to find your longest streak in 2025. Reflect on the strategies that helped you stay consistent and apply them to maintain or extend streaks in 2026.

Fun fact, learners who extend their daily streak by just two days are 18x more likely to complete a Career/Skill Track.

streaks = pd.read_csv('data/streaks.csv', parse_dates=['streak_started_at', 'streak_ended_at'])
streaks.head()

## complete challenge 2, be curious

🐾 Challenge 3: Set Next Steps

Identify your most productive days: Analyze the learning_minutes data to find which day(s) of the week you dedicated the most time to learning. Consider setting goals for 2026 to make these days even more productive, or identify new days to focus on learning.

learning_minutes = pd.read_csv('data/learning_minutes.csv', parse_dates=['date'])
learning_minutes.head()

## complete challenge 3, be creative

Additional Challenges

Here are some examples of how you can utilize your learning data in this notebook to have a more productive 2026.

🎯 Set Goals

These challenges will help you reflect on your achievements and set meaningful goals for the upcoming year.

  • Explore Your Certification Achievements: Review the certifications earned in certifications and decide which certifications you want to pursue in 2026. Use this as a foundation for your learning goals.

  • Find Your Learning Peaks by Course Type: Analyze the courses_completed data to identify the types of courses (beginner, intermediate, advanced) you completed most often. Decide if 2026 will focus on exploring more advanced topics or mastering foundational skills.

  • Set New Track Goals Based on Completed Tracks: Use the tracks_completed data to identify complementary tracks you'd like to pursue in 2026. Focus on building depth or breadth in your learning journey.

  • Analyze Your Skill Development by Technology: Using technologies_learned, reflect on your expertise across different technologies. Set 2026 goals to deepen your knowledge or branch into new areas.

🗓️ Form Habits

Learn from your data to identify habits that have worked well and those you can improve to form better learning routines in 2026.

  • Track Your Learning Streaks: Use the streaks data to find your longest streak in 2025. Reflect on the strategies that helped you stay consistent and apply them to maintain or extend streaks in 2026.

  • Uncover Patterns in Learning Minutes: Visualize your learning_minutes over the year to identify periods of increased or decreased study time. Adjust your schedule to create a more balanced learning routine in 2026.

  • Calculate Your Average Learning Time per Session: From learning_minutes, calculate your average session duration. Decide if you want to increase, maintain, or adjust this time for optimal learning in 2026.

🐾 Take Next Steps

Use these challenges to extract actionable insights from your data and plan concrete steps for improvement.

  • Monthly XP Growth Tracking: Use xp_earned to analyze monthly XP growth. Identify what contributed to high-growth months and plan to replicate these strategies consistently in 2026.

  • Evaluate Your Progress Across Courses and Chapters: Track the number of courses_completed and chapters_completed each month. Identify productive periods and plan how to maintain or increase completion rates next year.

  • Identify Your Most Engaged Workbook Creations: Analyze workbooks_created and workbook_upvotes to find your most successful creations. Reflect on what worked well and use these insights to create impactful projects in 2026.

  • Analyze Your Progress Across Technologies: Review the technologies_learned data to identify which areas saw the most progress. Plan specific courses or tracks to strengthen your expertise in these areas.

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