Applications and Software

Data Science



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Course Overview

Our in-depth Data Science course empowers you with the skills and knowledge to analyze and interpret complex data. Explore statistical analysis, machine learning algorithms, and data visualization techniques. With hands-on projects and real-world examples, you’ll gain practical experience in extracting insights from data. Whether you’re a beginner or an experienced professional, our course will guide you to become a proficient Data Scientist

What You'll Learn?

  • 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐅𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥𝐬: Master essential data analysis techniques, including data cleaning, exploration, and visualization.
  • 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬: Learn popular machine learning algorithms like regression, classification, clustering, and deep learning.
  • 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Explore statistical concepts and methods to draw meaningful insights and make data-driven decisions.
  • 𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Create compelling visualizations to effectively communicate complex data patterns and trends.
  • 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Apply your skills to real-world projects, solving data-driven challenges and building a strong portfolio.
  • Basic understanding of programming in Python
  • Knowledge of statistics and probability
  • Familiarity with data structures and algorithms.
  • 24 Hours of Sessions
  • Flexible Schedules
  • 24/7 Lifetime Support
  • Certification Oriented Curriculum
  • FREE Demo on Request
  • One-on-One Doubt Clearing
  • Real-time Project Use cases

Course Content

  • Introduction to Data Science
    • What is data science?

    • Data science process and lifecycle

    • Ethical considerations in data science

    • Tools and technologies used in data science

  • Data Acquisition and Preprocessing
    • Data collection methods

    • Data cleaning and validation

    • Exploratory data analysis

    • Feature engineering and selection

  • Statistical Analysis
    • Descriptive statistics

    • Probability distributions

    • Hypothesis testing

    • Correlation and regression analysis

  • Machine Learning Basics
    • Introduction to machine learning

    • Supervised vs. unsupervised learning

    • Regression and classification algorithms

    • Model evaluation and validation

  • Advanced Machine Learning Techniques
    • Ensemble methods (bagging, boosting, stacking)

    • Dimensionality reduction (PCA, LDA, t-SNE)

    • Regularization techniques (L1, L2)

    • Recommendation systems

  • Deep Learning and Neural Networks
    • Introduction to deep learning

    • Artificial neural networks

    • Convolutional neural networks (CNN)

    • Recurrent neural networks (RNN)

  • Natural Language Processing (NLP)
    • Introduction to NLP

    • Text preprocessing techniques

    • Sentiment analysis

    • Named Entity Recognition (NER)

  • Data Visualization
    • Principles of effective data visualization

    • Data visualization tools (matplotlib, Tableau, etc.)

    • Visualizing relationships and trends in data

    • Interactive and dynamic visualizations

  • Project Work and Case Studies
    • Analyzing real-world datasets

    • Building predictive models

    • Developing recommendation systems

    • Presenting findings and insights

  • Capstone Project
    • Applying all concepts and techniques learned throughout the course

    • Solving a complex data science problem

    • Presenting the final project

  • Duration 01:00:00
  • Lessons 39
  • Language English, Hindi, Telugu
  • Skill Intermediate
  • Last Update December 23, 2023