Data Science

Quick Java Course

Rating: 4
5624 people enrolled
Duration : 12 weeks
Lessons : 25
Course Instructor: kranthikunta
Last Updated : Jan. 3, 2025, 1:06 p.m.
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₹ 15000/-

₹ 5000/-

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  • Level: Intermediate

  • Duration: 12 weeks

  • Lessons: 25

  • Lifetime Access

  • Access From Any Computer, Tablet or Mobile

About the course

Our Data Science with Python internship provides an exceptional opportunity to gain practical experience in one of the most in-demand fields today. You’ll master Python and its key data science libraries like Pandas, NumPy, and Matplotlib, allowing you to effectively analyze, visualize, and interpret data. This hands-on learning experience will equip you with the technical expertise and problem-solving skills that employers highly value for roles such as Data Analyst, Data Scientist, and Machine Learning Engineer. Through this internship, you’ll develop a strong foundation in statistical analysis, data visualization, and machine learning techniques, all crucial for making data-driven decisions. With mentorship from industry experts and exposure to real-world projects, you’ll be fully prepared to tackle complex challenges and advance your career in the rapidly growing field of data science. This internship is the perfect stepping stone to unlocking exciting career opportunities and becoming a sought-after professional in data-driven industries.

Course Content

  • Course Introduction
  • Python Basics and Data Structures

  • Data Collection and Sources
  • Descriptive Stats and Data Visualization
  • Math for Data Science
  • Data Preprocessing and Feature Engineering
  • Data Collection and Preprocessing Details

  • Supervised Learning Overview
  • Supervised Learning Algorithms
  • Unsupervised Learning Overview
  • Clustering Techniques

  • Case Study
  • Time Series Analysis
  • ARIMA and SARIMA Models

  • Text Preprocessing and Mining
  • Sentiment Analysis - Part 1
  • Sentiment Analysis - Part 2

  • Neural Networks Basics
  • Deep Learning Introduction
  • Neural Networks Architectures
  • Deep Learning and Reinforcement Learning - Part 1
  • Deep Learning and Reinforcement Learning - Part 2
  • Deep Learning and Reinforcement Learning - Part 3
  • Deep Learning and Reinforcement Learning - Part 4

  • Ethics in Data Handling
  • Data Visualization and Communication
    • Admission Process

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