Data Science Boot Camp by Shubham Goel

The Data Science Professional Boot Camp by Shubham Goel is a comprehensive online program designed to transform beginners into job-ready data professionals. This structured course covers Python programming, SQL, statistics, machine learning, time series analysis, and real-world projects.

The boot camp focuses on practical implementation rather than just theory. Students work with real datasets, perform exploratory data analysis (EDA), build machine learning models, and complete capstone projects that strengthen their portfolios for job interviews.

This course is ideal for students, working professionals, and career switchers aiming to enter the data science and analytics industry.

What Student Will Learn

Data Science: The Professional Boot Camp

Module 1: Foundations of Data Analysis (The Toolkit)

• Python for Data Science: Syntax, loops, functions, and list comprehensions.

• NumPy: Efficient numerical computation and array manipulation.

• Pandas: Mastery of DataFrames—merging, pivoting, and handling missing data.

• SQL for Data Analysis: Writing complex queries, Joins, Aggregations, and Window functions.

Module 2: Statistics & Exploratory Data Analysis (EDA)

• Statistics & Probability: Descriptive vs. Inferential statistics, Hypothesis Testing, and p-values.

• Data Visualization: Creating impactful stories using Matplotlib and Seaborn.

• Data Cleaning: Handling outliers, feature scaling, and encoding categorical variables.

Module 3: Supervised Machine Learning

Regression: Simple and Multiple Linear Regression, Polynomial Regression.

Classification: Logistic Regression, k-Nearest Neighbors (KNN), and Support Vector Machines (SVM).

Tree-based Models: Decision Trees, Random Forests, and Gradient Boosting (XGBoost/LightGBM).

Module 4: Unsupervised Learning & Advanced Topics

• Clustering: k-Means Clustering and Hierarchical Clustering.

• Dimensionality Reduction: Principal Component Analysis (PCA) to handle high-dimensional data.

• Time Series Analysis: Basics of forecasting and seasonality.

Module 5: Real-World Capstone & Interview Prep

• Project 1: Predicting Customer Churn using Classification.

• Project 2: Sales Forecasting using Regression.

• Interview Kit: Portfolio building on GitHub/Kaggle, Resume reviews, and Mock technical interviews.

Teaching Methodology

• Hands-on coding sessions
• Real industry datasets
• Step-by-step implementation
• Practical assignments after each module
• Doubt-solving sessions
• End-to-end project guidance

Who Should Join

• Beginners interested in Data Science
• Engineering and commerce students
• Working professionals planning a career switch
• Analysts wanting to upgrade to Machine Learning
• Anyone preparing for Data Science interviews

Learning Outcomes

• Strong foundation in Python and SQL
• Ability to clean, analyze, and visualize data
• Hands-on experience in Machine Learning models
• Real-world project portfolio
• Confidence to crack Data Science interviews
• Industry-ready analytical thinking