IANT
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Data Science

IANT is one of the oldest IT Preparing and Certificate Organization serving from almost last 20 years , preparing the students ready to kick-start in their career. The board Administrations (IMS), programming, Digital protection, and Problematic advancements are part of IANT curriculum. IANT is additionally the greatest TechEd Organization in the EdTech space.

Skilled certification Program in the field of Data Science, AI and computer based intelligence is a task prepared profession course, crafted by IANT to change the vocations of twelfth, Students, Graduates (Expressions, Business), Confirmation holders, and Designing understudies into universally skilled Information Science Subject matter specialists. The advance preparing module of this course offered by IANT begins with Information Science fundamentals and Python Programming. Measurements and Progressed Succeed has been added to the course specially by IANT so the students get the best out of this course. R programming, Information Science with R Programming, Information Science with man-made intelligence and ML and contextual investigations takes this course to the expert level.

Course Content

1. Introduction to Data Science
  • What is Data Science?
  • Role of a Data Scientist in business and industry.
  • Difference between Data Scientist, Data Analyst, and Machine Learning Engineer.
  • Overview of the Data Science process.
2. Data Collection & Preprocessing
  • Data Types (Structured, Unstructured, Semi-structured)
  • SQL Basics & NoSQL Overview
  • Data Cleaning & Handling Missing Values
  • Feature Scaling & Encoding Techniques
3. Exploratory Data Analysis (EDA) & Visualization
  • Summary Statistics & Data Distribution
  • Visualization Tools: Matplotlib & Seaborn
  • Outlier Detection (Boxplots, Z-score, IQR)
4. Machine Learning Fundamentals
  • Supervised vs. Unsupervised Learning
  • Overfitting, Underfitting & Cross-validation
  • Model Evaluation Metrics (Accuracy, Precision, Recall)
5. Supervised Learning Models
  • Linear & Logistic Regression
  • K-Nearest Neighbors (KNN)
  • Decision Trees & Random Forest
6. Unsupervised Learning & Dimensionality Reduction
  • K-Means & Hierarchical Clustering
  • Principal Component Analysis (PCA)
7. Advanced Topics: Time Series & NLP
  • Time Series Forecasting (ARIMA)
  • Natural Language Processing (NLP) Basics
  • Sentiment Analysis using Machine Learning
8. Capstone Project
  • Dataset Collection & Preprocessing
  • Model Building & Evaluation
  • Visualization & Presentation of Findings
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