Description

Empower your team with data-driven thinking to make better decisions and solve complex problems. This Python for data science course provides a practical introduction to data science concepts and tools, covering key topics such as data manipulation, exploratory data analysis, and predictive AI. Participants learn how to use Python libraries like Pandas and scikit-learn to analyze data, build predictive models, and evaluate their performance. Hands-on labs expose them to realistic challenges and helpful solutions. By the end of the course, they are equipped to apply data science techniques to real-world challenges.

Skills Gained

By the end of this course, participants will be able to:

Use standard data science libraries to increase code reuse and portability
Clean and preprocess data to uncover actionable business insights
Visualize data to effectively communicate strategic trends and patterns
Develop AI models to improve operational efficiency and reduce costs
Explore advanced AI techniques to gain a competitive advantage in the market

Audience

Data Scientists & Analysts
Software Developers

Course Overview

Python for Data Science

Introduction to Python for Data Science
Overview of Data Science Tools
Setting Up the Environment
Jupyter Notebooks
Practical Work with Pandas Series and DataFrames

Data Science Fundamentals

What is Data Science?
Data Science Workflow
Introduction to Machine Learning
Ethical Considerations in Data Science
Data Manipulation with Pandas
Data Types and Structures
Feature Engineering

Data Wrangling & Visualization

Exploratory Data Analysis (EDA) with Pandas
Data Cleaning
Summarizing Data
Visual Exploratory Data Analysis (EDA) with Pandas
Combining Data Wrangling with Visual EDA
Multivariate Plots
Univariate Plots

Applied Data Science with Pandas & Scikit-Learn

Introduction to Data Science
Numpy Refresher
Steps in the Basic ML Pipeline
Further Refinement of ML Predictions
Model Selection
Evaluation Metrics

Basics of Predictive AI

Defining Machine Learning, AI, Predictive AI, and Generative AI
Overview of Neural Networks
The Machine Learning Workflow
Data Collection, Exploration, and Preprocessing
Model Training, Evaluation, and Selection
Model Deployment and Monitoring
Applications of Predictive AI
Challenges in Machine Learning

Predictive AI with Scikit-Learn

Getting Started with Scikit-Learn for Predictive AI
Important Algorithms in Scikit-Learn
Data Preparation for Scikit-Learn
Evaluating Models in Scikit-Learn
Deploying Predictive Models with Scikit-Learn
Using Cross-Validation
Tuning Hyperparameters

Applied Data Science Concepts with Python Virtual Classroom Live July 16, 2025

Price: $1,500

Enroll today to reserve your spot!

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