Transform into a job-ready data scientist. This program teaches you to analyse data, create visualizations, and build machine learning models using Python, Pandas, SQL, and other industry-standard tools. Learn through daily structured lessons, weekend data challenges, and real-world projects. Start as a complete beginner and graduate with a strong portfolio of data science projects.
Transform into a job-ready data scientist. This program teaches you to analyse data, create visualizations, and build machine learning models using Python, Pandas, SQL, and other industry-standard tools. Learn through daily structured lessons, weekend data challenges, and real-world projects. Start as a complete beginner and graduate with a strong portfolio of data science projects.
Explore the complete learning path organized by phases, weeks, and days
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Master Python programming from scratch with focus on data science applications. Learn variables, data types, control flow, functions, and object-oriented programming. Build a strong foundation in Python essentials needed for data analysis and machine learning.
Build essential statistical and mathematical foundations for data science. Master descriptive statistics, probability, distributions, hypothesis testing, and correlation analysis. Learn the math behind machine learning algorithms and data analysis techniques.
Master data manipulation and analysis using Pandas and NumPy. Learn to clean messy data, handle missing values, merge datasets, perform aggregations, and prepare data for analysis. Work with real-world datasets to develop practical data wrangling skills.
Create compelling data visualizations and conduct exploratory data analysis. Master Matplotlib, Seaborn, and Plotly to communicate insights visually. Learn to identify patterns, trends, and outliers in data through effective visualization techniques.
Learn to work with databases and extract data using SQL. Master data querying, aggregation, joins, and subqueries. Understand database design, normalization, and how to integrate SQL with Python for comprehensive data analysis workflows.
Build and deploy machine learning models using scikit-learn. Learn supervised learning (regression, classification), unsupervised learning (clustering), model evaluation, and hyperparameter tuning. Understand when to use different algorithms and how to improve model performance.
Complete an original, comprehensive data science project demonstrating all skills learned. Choose a real-world problem, collect or find data, perform analysis, build models, create visualizations, and present insights. Build a portfolio-ready project with professional documentation.
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