Statistical Reasoning
Master probability distributions, hypothesis testing, and A/B testing methodologies. Learn to validate assumptions and interpret p-values with rigor.
Course Detail
Data Science Fundamentals
Master Python, Statistics, and Machine Learning. Move from data analyst to data scientist with rigorous, project-based training.
Oct 12
Next Cohort
Learning Outcomes
Build the technical stack for modern analytics.
This course moves beyond theory. You will write production-level Python code, apply statistical methods to real business problems, and deploy machine learning models that drive decision-making.
Python Proficiency
Deep dive into NumPy for vectorization and Pandas for data manipulation. Write clean, efficient scripts to automate data pipelines.
Model Building
Implement supervised learning algorithms (Linear Regression, Logistic Regression, Decision Trees) using Scikit-learn. Focus on feature engineering and hyperparameter tuning.
Data Visualization
Communicate insights effectively. Create static and interactive visualizations using Matplotlib and Seaborn to tell compelling stories with data.
Curriculum
14 Weeks of Rigorous Training
A structured path from syntax to supervised learning, designed to fill the gap between academic statistics and practical engineering.
Module 01: Python Syntax & Data Types
Variables, operators, strings, lists, dictionaries, and sets. Building your first data scripts.
Module 02: Control Flow & Functions
Loops, conditionals, lambda functions, and writing reusable code modules.
Module 03: NumPy Arrays & Vectorization
Understanding multidimensional arrays, broadcasting, and array manipulation for performance.
Module 04: Pandas DataFrames
Loading, cleaning, and structuring messy data. Indexing, slicing, and merging datasets.
Module 05: Data Cleaning & Wrangling
Handling missing values, outliers, and data type conversions. Automating cleaning pipelines.
Module 06: Exploratory Data Analysis (EDA)
Univariate and bivariate analysis. Using visual intuition to guide feature selection.
Module 07: Probability & Statistics
Discrete and continuous probability. Mean, median, mode, variance, and standard deviation.
Module 08: Hypothesis Testing
Null and alternative hypotheses. T-tests, Chi-square tests, and p-value interpretation.
Module 09: Visualization (Matplotlib/Seaborn)
Creating static plots, heatmaps, and complex multi-plot figures for reports.
Module 10: Machine Learning Intro
The bias-variance tradeoff. Train-test splits and cross-validation strategies.
Module 11: Supervised Learning (Regression)
Linear and multiple regression. Evaluating model performance with R-squared and RMSE.
Module 12: Supervised Learning (Classification)
Logistic regression and decision trees. Binary and multi-class classification tasks.
Module 13: Feature Engineering
Encoding categorical variables, scaling data, and creating new predictive features.
Module 14: Final Capstone Project
End-to-end analysis of a real-world dataset. Model building, evaluation, and presentation.
Tools & Technologies
Python 3.11
Pandas
NumPy
Scikit-learn
Matplotlib
Jupyter Notebooks
Capstone Project
Real-World Dataset Analysis
In the final week, you will select a publicly available dataset (e.g., housing prices, consumer behavior, or healthcare records). You will clean the data, perform EDA, build a predictive model, and generate a comprehensive report.
Presentation Deliverable
You will present your methodology and findings to the cohort. This includes a slide deck summarizing key insights and a live demonstration of your Python code running the model.
Alumni Outcomes
Graduates are working at top companies.
Sarah Jenkins
Data Analyst
Stripe
Michael Chen
Data Scientist
Spotify
Priya Patel
BI Engineer
Amazon
Next Cohort — Starts Oct 12, 2025
Launch your data career.
Select your learning mode and secure your seat. Cohorts fill up 2 weeks before the start date.