Data Science with Python

Last Updated: 31 03 2025

Data science with Python course is designed to equip the skills required to excel in the Data science and analytics. This training includes everything from foundational concepts to advanced techniques, so that the good understanding of analytics can be ensured using Python. 
 
From the python programming fundamentals, and the course develops for Tabulation by using data manipulation with Pandas, Matplotlib, and Seaborn. Learners also find out advanced model such as Natural Language Processing and Deep Learning, gaining hands-on experience with real datasets. 
 
An important attraction to this Data science with python training is its practical approach, so that participants can work on industry-relevant projects. By applying theoretical concepts to real-world scenarios, learners gain confidence in effectively solving complex computational problems. 

 
After completing, participants acquire a Data science with Python certification, validates their expertise and increases the career opportunities. Whether you are an aspiring data scientist or a professional to upgrade your skill set, this course provides the knowledge and experience required to flourish in today's data -driven industry. 
 
With a structured course and practice on their hands, this course ensures that learners are job-ready, prepared to build predictive models, and capable of making data-driven decisions with Python. 
 

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Learning Options for You

  • Live Training (Duration : 32 Hours)
  • Per Participant

Fee: OnRequest

09:00 - 13:00 (IST)

(4 Hours/Day)

09:00 - 17:00 (IST)

(8 Hours/Day)

Course Prerequisites

  • Basic understanding of mathematics (algebra, statistics, probability)
  • No prior programming knowledge required (Python basics will be covered)
     

Learning Objectives

  • Python programming essentials for data science
  • Data manipulation and visualization using Pandas, NumPy, Matplotlib, and Seaborn
  • Exploratory Data Analysis (EDA) and feature engineering
  • Machine learning fundamentals (supervised and unsupervised learning)
  • Model evaluation techniques and basic deployment

Target Audience

  • Beginners in programming or data science
  • Analysts, engineers, and students who want to start a career in data science
  • Professionals from non-technical backgrounds looking to understand data-driven decision-making
     

*Excluding VAT and GST

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