Build practical Python data analytics and predictive modeling skills through coding, statistics, data preparation, and regression analysis.
Progress from Python fundamentals to analyzing real datasets and predicting numerical outcomes with confidence.
This beginner-friendly Specialization provides a structured pathway for learners who want to use Python for data analysis and applied machine learning. You will begin by writing programs with conditions, loops, and reusable functions before working with NumPy, Pandas, CSV files, Series, and DataFrames.
You will develop the statistical knowledge needed to identify data types, interpret distributions, create meaningful visualizations, and apply sampling techniques. You will then prepare real-world datasets for predictive modeling by handling missing values, transforming features, encoding categorical variables, and validating data quality.
The final stage focuses on building, training, testing, and evaluating regression models for numerical predictions, including price analysis. Through practical exercises and realistic datasets, you will connect programming, analytics, statistics, and predictive modeling into a complete data workflow relevant to data analytics, business intelligence, and entry-level data science roles.
Applied Learning Project
Learners will complete hands-on projects involving Python programming, dataset cleaning, statistical visualization, and regression-based price prediction. Using realistic datasets, they will transform raw data, resolve missing values, analyze patterns, prepare model features, and evaluate predictions to solve authentic business and analytics problems.
