Data Analysis with Python
For Working Professionals | WEEKEND Sessions
8-Session Live Online Bootcamp for Complete Beginners
No Need to Pay the Actual Price of $1045 - Save $500
✅ Live Interactive Sessions | ✅ Personal Guidance | ✅ Lifetime Access to Recordings | ✅ Batch size of 7
Why This Bootcamp Will Transform Your Career
Start Coding Immediately
No boring theory! You'll build your first working program in the first session itself. Our unique "code-first" approach means you learn by actually building real applications that solve real problems.
Build a Professional Portfolio
Walk away with functional programs including a personal finance dashboard, data visualizer, and more. These aren't toy projects - they're portfolio pieces that impress employers.
Industry-Ready Skills Fast
Master everything from basic syntax to advanced concepts like APIs, data analysis, and web scraping in just 18 hours. We compress years of learning into an intensive, results-focused experience.
Live Expert Guidance
Learn directly from senior Python developers with 10+ years of experience. Get real-time feedback, ask questions, and avoid the pitfalls that trip up self-taught programmers.
Immediate ROI
Python developers earn $95K+ annually. Our graduates report salary increases within 3-6 months. This bootcamp pays for itself with your first freelance project or job offer.
Lifetime Access to Resources
Lifetime access to all recordings, bonus materials, private community, and future updates. Plus newsletter subscription to support your ongoing Python journey.
Your Learning Journey: 8 Intensive Sessions
🗓️ Perfect Schedule for Working Professionals & Students
( 8pm to 10pm IST )
First Steps - Interactive exploration of Netflix movies/TV shows dataset
Session Date : 20 Sept 2025
Python Concepts Learned Through Doing
1. Environment Setup: Jupyter Notebook basics, running cells
2. Data Import: import pandas as pd, pd.read_csv()
3. First Look: .head(), .tail(), .info(), .shape
4. Basic Exploration: .describe(), .value_counts(), column selection
5.
Simple Questions: "How many movies vs. TV shows?", "Which countries produce most content?".
(e.g., "Comedy is the most common genre with X titles")
Data Cleaning - Clean, Analysis-Ready Dataset from Messy Student Performance Data
Session Date : 21 Sept 2025
Python Concepts Learned Through Doing
1. Missing Data Reality: .isnull(), .isna(), visualizing gaps with .info()
2. Data Cleaning: .dropna(), .fillna(), handling duplicates
3. Data Types: Converting columns, understanding
4. Basic Filtering: df[df['score'] > 70], multiple conditions with AND, OR
5.
Column Operations: Creating new columns, .drop() unwanted ones
(e.g., "Student Performance Analysis: What Really Matters for Success?")
Finding Patterns with Numbers
- Statistical analysis report of World Happiness data
Session Date : 27 Sept 2025
Python Concepts Learned Through Doing
1. Statistical Summaries: .mean(), .median(), .std(), .min(), .max()
2. Grouping Data: .groupby() for regional analysis
3. Correlations: .corr() to find relationships between variables
4. Sorting: .sort_values() to rank countries
5. Index Operations: .reset_index(), basic understanding of pandas indexing
(e.g., "Investigate correlations between happiness and GDP, social support, freedom")
Data visualization - Multiple visualization types showing different aspects of NYC crime
Session Date : 28 Sept 2025
Python Concepts Learned Through Doing
1. Plotting Basics: import matplotlib.pyplot as plt, plt.show()
2. Chart Types: plt.bar(), plt.hist(), plt.scatter(), plt.plot()
3. Customization: plt.xlabel(), plt.ylabel(), plt.title(), NumPy colors and styles
4. Seaborn Introduction: import seaborn as sns, sns.countplot(), sns.boxplot()
5. Figure Management: plt.figure(figsize=()), saving plots with plt.savefig()
(e.g., "Build different chart types to answer specific questions")
Statistical analysis testing - Comparison of different employee groups with statistical significance testing
Session Date : 04 Oct 2025
Python Concepts Learned Through Doing
1. Hypothesis Formation: Turning business questions into testable hypotheses
2. Group Comparisons: Comparing means between different employee segments
3. Statistical Testing: Basic t-tests using scipy.stats
4. P-values and Significance: Understanding what statistical significance means practically
5. Advanced Grouping: .groupby() with multiple variables, .pivot_table()
(e.g., "Perform comparisons between groups, interpret results")
Machine Learning Model - House Price Prediction using your first ML Model
Session Date : 05 Oct 2025
Python Concepts Learned Through Doing
1. Machine Learning Concept: Prediction vs. description, features vs. target
2. Train/Test Split: train_test_split() for model validation
3. Linear Regression: LinearRegression() from scikit-learn
4. Model Evaluation: Mean squared error, R-squared score, prediction accuracy
5. Feature Importance: Understanding which variables matter most for predictions
(e.g., "Discover which home features drive prices most")
Comprehensive Analysis Combining Customer, Sales, and Support Data | Integrated dashboard with multiple data perspectives
Session Date : 11 Oct 2025
Python Concepts Learned Through Doing
1. Data Merging: .merge(), .join() to combine multiple datasets
2. Advanced Visualization: plotly for interactive charts, Plotly subplot arrangements
3. Time Series Basics: Working with dates, .datetime operations, time-based grouping
4. Complex Filtering: Multiple conditions, .query() method for complex selection
5. Data Pipeline: End-to-end workflow from raw data to insights
(e.g., "Cross-dataset patterns, customer segmentation, journey mapping")
Capstone Project Showcase - Student Choice Comprehensive Analysis
Session Date : 12 Oct 2025
What Students Will Build
1. Independent Analysis: Student selects dataset and research question
2. Complete Workflow: Data cleaning → EDA → statistical analysis → visualization → ML
3. Professional Presentation: Executive summary, methodology, findings, recommendations
4. Portfolio Piece: Job-ready project demonstrating all bootcamp skillsn
(e.g., "E-commerce customer segmentation and sales optimization, Marketing campaign performance analysis with ROI calculations, Supply chain efficiency analysis using logistics data")
Success Stories from Our Graduates
Transform Your Career Today
Early Bird Special - Save $500!
- 8 Live Interactive Sessions
- Build Real Python Applications
- Lifetime Access to All Recordings
- Complete Source Code for All Projects
- Private Student Community Lifetime Access
- Live Q&A during the Sessions
- Networking Opportunities with Classmates
Cohort starts on Sept 20 2025 | Only 4 seats remaining
Frequently Asked Questions
A. Absolutely not! This bootcamp is designed for complete beginners. You'll start coding from minute one, and we'll guide you through every step.▼
Absolutely not! This bootcamp is designed for complete beginners. You'll start coding from minute one, and we'll guide you through every step.
A. All sessions are recorded and available immediately after each class. You'll have lifetime access to all recordings and materials. ▼
All sessions are recorded and available immediately after each class. You'll have lifetime access to all recordings and materials.
A. We recommend 2-3 hours per week for practice and review. All learning happens during the live sessions - homework is optional but encouraged ▼
We recommend 2-3 hours per week for practice and review. All learning happens during the live sessions - homework is optional but encouraged.
A. Just a computer with internet access! We'll show you how to set up Python and all necessary tools in the first session. ▼
Just a computer with internet access! We'll show you how to set up Python and all necessary tools in the first session.
A. Yes! Classes are exclusively on Tuesdays and Thursdays from 10:30 AM to 12:30 PM EST, designed specifically for working professionals who want to learn without disrupting their careers. ▼
Yes! Classes are exclusively on Tuesdays and Thursdays from 10:30 AM to 12:30 PM EST, designed specifically for working professionals who want to learn without disrupting their careers.
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