Data analysis is a critical skill in today’s data-driven world, used in business intelligence, research, and decision-making. This course provides a hands-on approach to working with data in Python, covering everything from data preparation to predictive modeling.
Learners will begin by understanding how to clean and preprocess data, handling missing values, formatting datasets, and normalizing information for analysis. The course then moves into exploratory data analysis (EDA), teaching how to summarize datasets, visualize distributions, and uncover meaningful patterns. Learners will also explore DataFrames in Pandas, perform correlation analysis, and create efficient data pipelines.
Finally, the course introduces regression models using the Scikit-learn library, enabling learners to build predictive models for real-world applications. By the end of this course, participants will be able to analyze and interpret datasets confidently, using Python for data-driven decision-making.
Sections Included:
- Introduction to Data Analysis – Understanding the data analysis process and setting up the Python environment.
- Data Cleaning and Preparation – Handling missing values, formatting, normalizing, and binning data.
- Exploratory Data Analysis (EDA) – Visualizing data distributions, summarizing datasets, and detecting patterns.
- Working with DataFrames in Pandas – Filtering, grouping, and transforming data efficiently.
- Statistical Analysis with NumPy and Scipy – Performing correlation analysis, hypothesis testing, and probability analysis.
- Building Data Pipelines – Automating data preparation and analysis workflows.
- Regression and Machine Learning Models – Implementing linear and multiple regression models using Scikit-learn.
Benefits Upon Completion:
- Learn to clean and prepare data for analysis using Python.
- Perform exploratory data analysis and summarize datasets effectively.
- Manipulate data using DataFrames and apply statistical techniques.
- Build and evaluate regression models for predictive analysis.
- Gain hands-on experience with industry-standard Python libraries.
