About This Course
Data is the new oil, but it's useless unless refined. This course takes you through the complete data science lifecycle. You will learn how to clean messy data, perform exploratory data analysis (EDA), and apply statistical methods to uncover hidden trends.
Beyond analysis, you will dive into the world of Machine Learning. Using powerful libraries like scikit-learn, you'll build, train, and evaluate algorithms capable of making predictions and automating complex decision-making processes.
Skills You Will Gain
Course Syllabus
Module 1: Intro to Data Science & Environments
Set up your analytical environment using Jupyter Notebooks and Anaconda. Learn the basics of NumPy arrays for high-performance numerical computing and vectorization.
Module 2: Data Wrangling with Pandas
Master the Pandas library. Learn how to load datasets (CSV, Excel, SQL), clean missing or messy data, filter rows, group variables, and merge multiple data frames.
Module 3: Exploratory Data Analysis & Visualization
Bring your data to life. Use Matplotlib and Seaborn to create histograms, scatter plots, heatmaps, and custom visualizations to discover patterns and outliers in your data.
Module 4: Foundations of Machine Learning
Understand the theory behind ML. Learn the difference between Supervised and Unsupervised learning, and how to split your data into training and testing sets to avoid overfitting.
Module 5: Predictive Modeling & Evaluation
Build real models using scikit-learn. Implement Linear Regression for continuous data and Logistic Regression/Decision Trees for classification. Learn to evaluate models using metrics like accuracy, precision, and RMSE.