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Hands-on Data Analysis with Pandas Course
Description
Hands-on Data Analysis with Pandas Introduction
Welcome to the “Hands-on Data Analysis with Pandas” course, designed specifically for data professionals who already possess a foundational knowledge of Python scripting. As a pivotal part of our Python Journey series, this course aims to enhance your data analysis capabilities using Python’s most influential libraries.
This course centers on pandas, a cornerstone of the Python data science framework, providing comprehensive training on data manipulation techniques essential for real-world challenges. You will gain proficiency in handling complex data wrangling tasks such as reshaping, cleaning, and aggregating datasets to make them ready for analysis.
As you progress, the course will introduce exploratory data analysis (EDA), where you will sharpen your skills in generating insights through summary statistics and advanced data visualization techniques using matplotlib and seaborn. Furthermore, you will dive into practical machine learning applications using Python’s scikit-learn, exploring techniques like anomaly detection, regression, clustering, and classification to predict outcomes based on historical data. T
his practical, hands-on approach ensures that you not only learn theoretical concepts but are also well-prepared to apply these techniques effectively in your data science career.
Hands-on Data Analysis with Pandas Course Objectives
- Master data manipulation and cleaning techniques using pandas to prepare datasets for analysis.
- Develop skills in exploratory data analysis (EDA) with pandas, matplotlib, and seaborn to derive actionable insights from data.
- Learn to apply machine learning models for anomaly detection, regression, clustering, and classification using scikit-learn.
- Gain hands-on experience through practical examples and projects that simulate real-world data analysis scenarios.
- Build a robust foundation in data analysis that supports continued professional growth in the field of data science.
Prerequisites
Students should have skills at least equivalent to the following course(s) or should have attended as a pre-requisite:
- Data Science Primer | Technologies, Tools & Modern Roles in the Data-Driven Enterprise
- Introduction to Python Programming | Python Programming Basics
Audience
- This course is geared for Python-experienced attendees who wish to be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.
Hands-on Data Analysis with Pandas Outline
Introduction to Data Analysis
- Fundamentals of data analysis
- Statistical foundations
- Setting up a virtual environment
Working with Pandas DataFrames
- Pandas data structures
- Bringing data into a pandas DataFrame
- Inspecting a DataFrame object
- Grabbing subsets of the data
- Adding and removing data
Data Wrangling with Pandas
- What is data wrangling?
- Collecting temperature data
- Cleaning up the data
- Restructuring the data
- Handling duplicate, missing, or invalid data
Aggregating Pandas DataFrames
- Database-style operations on DataFrames
- DataFrame operations
- Aggregations with pandas and numpy
- Time series
Visualizing Data with Pandas and Matplotlib
- An introduction to matplotlib
- Plotting with pandas
- The pandas.plotting subpackage
Plotting with Seaborn and Customization Techniques
- Utilizing seaborn for advanced plotting
- Formatting
- Customizing visualizations
Financial Analysis – Bitcoin and the Stock Market
- Building a Python package
- Data extraction with pandas
- Exploratory data analysis
- Technical analysis of financial instruments
- Modeling performance
Rule-Based Anomaly Detection
- Simulating login attempts
- Exploratory data analysis
- Rule-based anomaly detection
Getting Started with Machine Learning in Python
- Learning the lingo
- Exploratory data analysis
- Preprocessing data
- Clustering
- Regression
- Classification
Making Better Predictions – Optimizing Models
- Hyperparameter tuning with grid search
- Feature engineering
- Ensemble methods
- Inspecting classification prediction confidence
- Addressing class imbalance
- Regularization
Machine Learning Anomaly Detection
- Exploring the data
- Unsupervised methods
- Supervised methods
- Online learning
The Road Ahead
- Data resources
- Practicing working with data
- Python practice
$2195.00
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3 Days Course |