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Certificate in Python for Data Science

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About Course

Data Science Course Overview:

Dive into the world of Data Science and unlock the power of data! In this course, you will learn the essentials of Data Science, including:

  • Data Analysis: Understand and interpret data trends, patterns, and insights.
  • Data Processing: Learn how to clean, prepare, and transform raw data for analysis.
  • Python Programming: Master popular tools and libraries for data manipulation and visualization.
  • Machine Learning: Build intelligent models that predict outcomes and make decisions.
  • Deep Learning and NLP: Explore advanced concepts for handling images, text, and unstructured data.

By the end of the course, you will have the skills to solve real-world problems using data-driven techniques, preparing them for exciting opportunities in technology and business.

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What Will You Learn?

  • By the end of the Data Science Course, students will learn:
  • Analyze Data Effectively: Use statistical techniques and tools to derive meaningful insights from raw data.
  • Master Data Visualization: Create impactful graphs and dashboards to communicate findings clearly.
  • Build Predictive Models: Apply machine learning algorithms to forecast outcomes and make data-driven decisions.
  • Understand Deep Learning Basics: Work with neural networks for advanced tasks like image recognition and natural language processing (NLP).
  • Program with Confidence: Develop proficiency in Python for data manipulation, analysis, and automation.
  • Solve Real-World Problems: Handle end-to-end data projects, including data cleaning, feature engineering, and model deployment.
  • Use Industry Tools: Work with libraries like Pandas, NumPy, Matplotlib, Scikit-learn, TensorFlow, and more.
  • Students will leave the course equipped to take on roles like Data Analyst, Data Scientist, or Machine Learning Engineer, ready to make an impact in the data-driven world.

Course Content

Python for Data Science
This course is designed to provide students with a strong foundation in Python programming and introduce them to the exciting fields of Artificial Intelligence (AI), Machine Learning (ML), Data Science, and Deep Learning (DL). By learning these in-demand technologies, students will develop problem-solving skills, logical thinking, and the ability to work with real-world data. This knowledge will not only enhance their academic growth but also open up a wide range of career opportunities in the tech industry and beyond.

  • Data Science
    01:09
  • Introduction to Colab Notebook
    03:06
  • Python Variables
    12:48
  • Python Data Types
    15:02
  • Python Functions
    15:01
  • User-Defined functions
    12:29
  • Python Conditional Statement
  • Python loops

Data Structures in Python
Data structures are a very important part of the entire course. In this lesson, students will learn about the different types of data structures available in Python and how to use them to store and organize data effectively.

Data Processing and Analysis
**Data Processing and Analysis** is a crucial step in the Data Science workflow. In this module, students will learn how to clean, transform, and organize raw data to make it suitable for analysis. They will also explore techniques for analyzing data to extract meaningful insights and support data-driven decision making.

Data Visualization using Matplotlib, Seaborn
**Data Visualization using Matplotlib and Seaborn** is a key component of Data Science. In this module, students will learn how to represent data visually to better understand patterns, trends, and relationships. They will explore various types of plots such as line charts, bar graphs, histograms, scatter plots, and heatmaps using Matplotlib and Seaborn, and learn how to customize these visualizations for clear and effective communication.

OOPs concept in Python
Object-Oriented Programming (OOPs) in Python is a fundamental concept that helps structure code for better organization, reusability, and scalability. In this module, students will learn:The core principles of OOP: Class, Object, Inheritance, Polymorphism, Encapsulation, and Abstraction

Working on House Price Dataset
In this lesson, the student will work on processing house price data by checking for missing values, exploring the data's information, performing data manipulation if required, and creating visualizations to gain insights.

Artificial Intelligence
In this lesson, the student will gain a basic understanding of Artificial Intelligence, including the differences between AI, Machine Learning (ML), Deep Learning (DL), and Data Science.

Deep Learning
Deep Learning is a key area of Artificial Intelligence that focuses on training neural networks with multiple layers to model complex patterns in large datasets. In this lesson, the student will learn the fundamentals of deep learning, how it differs from traditional machine learning, and how to build and train deep neural networks for various applications.

Capstone Project on Deep Learning
In this lesson, the student will apply the foundational concepts learned in the previous lessons to complete a hands-on project.

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