Key Points

Python for Data Science
Data Cleaning & Preprocessing
Exploratory Data Analysis (EDA)
Data Visualization with Matplotlib & Seaborn
Statistics & Probability
Machine Learning Foundations
SQL for Data Science
Pandas, NumPy & Scikit-learn
Capstone Project & Portfolio

Training Key Features

End-to-End Data Science Projects

Gain hands-on experience by working on full-cycle projects involving data collection, cleaning, analysis, and visualization.

Data Analysis & Visualization

Master tools like Pandas, Matplotlib, Seaborn, and Plotly to uncover insights and tell compelling stories with data.

Machine Learning Essentials

Understand core ML algorithms and implement supervised and unsupervised models for predictive insights.

Job-Ready Portfolio & Resume Support

Create impactful resumes and GitHub portfolios that highlight your data science capabilities and projects.

Mock Interviews & Career Guidance

Practice data-driven interview questions and receive expert feedback to excel in your job search.

Mentorship from Data Science Experts

Get personalized guidance from professionals in the data science field with real-world expertise.

Data Science Training Key Features

Prerequisite

No prior data science experience is required.

A basic understanding of Python programming, statistics, and analytical thinking is helpful but not essential.

This course is ideal for students (B.E/B.Tech/B.Sc/BCA/MCA), graduates, postgraduates, or working professionals aiming to pursue a career in Data Science, Business Analytics, or AI domains.

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+91 7887885052

  • Python Programming
  • Jupyter Notebook
  • NumPy & Pandas
  • Matplotlib / Seaborn / Plotly
  • SQL for Data Analysis
  • Excel for Data Science
  • Google Colab
  • Git & GitHub

  • Data Collection & Cleaning
  • Exploratory Data Analysis (EDA)
  • Data Wrangling & Preprocessing
  • Statistical Analysis
  • Hypothesis Testing
  • Data Visualization Techniques
  • Data Storytelling
  • Project Workflow & Pipelines

  • Supervised & Unsupervised Learning
  • Model Evaluation & Metrics
  • Feature Engineering
  • Regression & Classification Models
  • Clustering Techniques
  • Time Series Forecasting
  • Model Deployment with Streamlit/Flask
  • Capstone Project & Case Studies
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