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Python Programming for Data Science Training

  • Training Duration:
  • 5 Days
  • Training Environment:
  • On-site at the Institution
  • In DVA Classrooms
  • Online
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About the Training

The Python Programming for Data Science Training is highly valuable, especially for those aspiring to build a career in data science. Participants learn the fundamentals of the Python programming language and explore techniques for data analysis and machine learning. This knowledge is essential for extracting meaningful insights from large datasets.

The training focuses on data manipulation and cleaning. Participants learn to use libraries such as pandas and NumPy, which simplify the data processing workflow and accelerate data analysis tasks. This speed allows projects to progress more effectively.

Machine learning is a significant part of the training. Participants learn to work with the scikit-learn library, which demonstrates how to train and test algorithms. It also covers model evaluation techniques, which are crucial for improving the accuracy of models.

The training also includes visualization tools. Participants learn to create graphs using the matplotlib and seaborn libraries. These graphs make data analyses more comprehensible and serve as effective tools for presentations and reports, facilitating the sharing of analysis results.

The Python Programming for Data Science Training equips participants with practical skills that help them develop their own data science projects. The training provides guidance through every stage of the data science process, enabling participants to improve data-driven decision-making processes.

In conclusion, this training opens the door to the world of data science. Participants learn to work on data science projects using Python, helping them specialize in data analysis and machine learning. By the end of the training, participants will be capable of developing significant projects in the field of data science, greatly contributing to their professional growth.

What Will You Learn?

  • Python Programming Fundamentals: Variables, data types, loops, functions.
  • Python for Data Analysis: Data processing and analysis with Pandas and NumPy libraries.
  • Data Visualization: Techniques for data visualization using Matplotlib and Seaborn.
  • Machine Learning with Python: Developing machine learning models using Scikit-learn.
  • Data Cleaning and Preparation: Techniques for data preprocessing, cleaning, and transformation.
  • Advanced Python Libraries: Using advanced libraries like Keras and TensorFlow.
  • Project-Based Applications: Hands-on project work on real-world datasets.

Prerequisites

  • Familiarity with basic computer knowledge and programming concepts.
  • A solid foundation in mathematics and statistics is preferred.

Who Should Attend?

  • Anyone interested in Data Science.
  • Data Analysts, Engineers, and Scientists.
  • Software Developers and IT Professionals.
  • Academics and Researchers.

Outline

Introduction: Python and Data Science
  • The importance of Python and its role in data science.
  • The Python ecosystem for data science.
Python Programming Fundamentals
  • Python syntax, variables, and data types.
  • Control structures, loops, and functions.
Python for Data Analysis
  • Data processing with Pandas and NumPy.
  • Data analysis and exploratory data analysis techniques.
Data Visualization
  • Basic and advanced visualization with Matplotlib and Seaborn.
  • Visual techniques for understanding and interpreting datasets.
Machine Learning with Python
  • Machine learning algorithms with Scikit-learn.
  • Model training, evaluation, and improvement.
Data Cleaning and Preparation
  • Data preprocessing and cleaning techniques.
  • Data transformation and feature engineering.
Advanced Python Libraries
  • Advanced libraries like Keras and TensorFlow.
  • Deep learning models and applications.
Project-Based Applications
  • Project work on real-world datasets.
  • Applying data science to solve problems.

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