Data Science with AI Training

Data Science with AI Training Details [Online ]

Training
Training Details
Next batch Date & Time
20th September 2025 – 10:00 AM IST
Training Modes
Online & Offline
Contact Us
info@Ramsinfosolutions.com
Course Duration
60 Days (Monday to Friday)
Demo Class Details:
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Key Features Of Data Science with AI Training

About Data Science with AI Training

The Data Science with AI Training program is designed to equip learners with the skills required to analyze data, build intelligent models, and apply Artificial Intelligence techniques to solve real-world problems. This course covers the complete data science lifecycle, including data collection, cleaning, visualization, machine learning, deep learning, and AI-driven decision-making.
Through live interactive sessions, hands-on projects, and case studies, you will gain expertise in Python programming, statistical analysis, predictive modeling, neural networks, and natural language processing. The training also emphasizes practical implementation, ensuring you can apply AI solutions across industries such as finance, healthcare, retail, and technology.
Whether you are a fresher, IT professional, or business analyst aspiring to transition into the AI and Data Science domain, this training prepares you for high-demand roles like Data Scientist, AI Engineer, Machine Learning Engineer, or Business Intelligence Analyst.
Enroll with Rams Info Solutions today and take the first step toward building a successful career in Data Science and Artificial Intelligence.

Data Science with AI Training Curriculum

  • What is Data Science?
  • The Data Science Lifecycle
  • Introduction to Artificial Intelligence (AI)
  • Differences and Overlaps Between AI and Data Science
  • Applications of Data Science and AI in Real Life
  • Python Installation
  • Working Jupyter Notebook
  • Python Basics: Syntax, Functions, and Libraries
  • Key Libraries for Data Science: NumPy, Pandas, Matplotlib, Seaborn
  • Importance of Statistical
  • Foundations in AI
  • Descriptive Statistics (Mean, Median,
  • Mode, Variance, etc.)
  • Probability Basics
  • Hypothesis Testing
  • Introduction to Machine Learning
  • Supervised vs. Unsupervised Learning
  • Key Algorithms: Linear Regression, Logistic Regression, Decision Trees, Boosting
  • Model Evaluation: Accuracy, Precision, Recall, F1-Score
  • Introduction to Regression
  • Types of Regression
  • Step by Step Model Building – data preparation, feature engineering, variable reduction,
    model building, model performance tracking
  • Introduction to Decision Tree
  • How they work: Splits, Gini Impurity, Information Gain
  • Introduction to Boosting
  • Types of Boosting
  • Step by Step Boosting Model Building
  • Working with real life Fraud Data
  • Objective: Solve a real-world problem using Data Science and AI.
  • Steps:
    1. Define the Problem
    2. Collect and Analyze Data
    3. Build and Evaluate a Model
    4. Visualize and Present Results
  • Hands-On Guidance with Mentors

Job Opportunities After Completing the Data Science with AI Course

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