What will you learn in this Data Analysis with Python Course
Data Collection & Importing: Learn to gather data from various sources and import it into Python for analysis.
Data Cleaning & Preparation: Master techniques to clean, format, and prepare data for analysis, including handling missing values and normalizing data.
Data Manipulation: Utilize Pandas and NumPy libraries to manipulate data frames, summarize data, and understand data distributions.
Exploratory Data Analysis (EDA): Perform EDA to uncover patterns, spot anomalies, and test hypotheses using statistical summaries and visualizations.
Regression Modeling: Build and evaluate regression models using scikit-learn to predict future trends and make data-driven decisions.
Data Pipelines: Create efficient data pipelines to streamline the data analysis process.
Program Overview
Importing Data Sets
- Understand different data formats and how to import them into Python.
Cleaning and Preparing the Data
- Learn techniques to clean and prepare data for analysis.
Summarizing the Data Frame
- Summarize data using descriptive statistics and visualization tools.
Model Development
- Develop regression models to analyze relationships between variables.
Model Evaluation
- Evaluate model performance using various metrics and refine models for better accuracy.
Model Refinement
- Enhance model performance through techniques like cross-validation and parameter tuning.
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Job Outlook
Equips learners for roles such as Data Analyst, Data Scientist, and Business Analyst.
Provides foundational skills applicable in industries like finance, healthcare, marketing, and technology.
Enhances employability by teaching practical skills in data analysis and machine learning.
Specification: Data Analysis with Python
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