AI and Data Science Excellence Program

About the Course

• Who is this course ideal for?
  Career Switchers, Students and freshers.

• Are there any pre-requisites to do this course?
  (Prior knowledge, skills, experience, or “no prior experience required”)
  • Basic knowledge of Python programming

  • Fundamental understanding of mathematics and statistics

  • Familiarity with common data formats like CSV, Excel, and JSON

  • Basic awareness of cloud computing and computer system concepts

  • Laptop with minimum technical specifications (8 GB RAM, i5/Ryzen 5 or above)

  • Willingness to commit 8–10 hours per week for practice and assignments

• What is the total course duration?

  3-5 Months.

• What is the mode of delivery?

  Offline / Classroom

• What is the session frequency?

  2 sessions per week.

• What is the average duration of each session?

  2.5 to 3 hours.

Course Structure & Delivery

• What is the course outline or key modules covered?
  • Python for Data Science
  Python fundamentals, NumPy, Pandas, data manipulation, and preprocessing for analytics.
  • Statistics & Mathematics for Data Science
  Descriptive statistics, probability, linear algebra basics, correlation, and hypothesis testing.
  • Data Visualization & Exploratory Data Analysis (EDA)
  Data storytelling, Matplotlib & Seaborn, handling missing values, outlier detection, feature analysis.
  • Machine Learning Fundamentals
  Supervised & unsupervised learning, ML lifecycle, model evaluation, algorithms like Linear & Logistic Regression, Decision Trees, Random Forest, KNN.
  • Unsupervised Learning Techniques
  Clustering (K-Means, Hierarchical), dimensionality reduction (PCA), real-world use cases.
  • Deep Learning & Neural Networks Basics
  Artificial Neural Networks, activation functions, optimizers, overfitting, TensorFlow/Keras introduction.
  • AI Concepts & Applications
  AI vs ML vs DL, NLP basics, Computer Vision basics, industry use cases.
  • Model Deployment & Capstone Project
  Model deployment using Flask/Streamlit, end-to-end project execution, presentation and evaluation.

• What key skills or knowledge will learners gain from this course?
  Key Skills Learners Will Gain After This Training
  
  After completing this AI & Data Science program, learners will gain a strong mix of technical, analytical, and professional skills, including:
  
  1. Technical Skills
  • Python programming for data analysis using NumPy and Pandas
  • Data cleaning, preprocessing, and feature engineering
  • Exploratory Data Analysis (EDA) and data visualisation
  • Statistical analysis and probability for data-driven decision-making
  • Building and evaluating machine learning models (supervised & unsupervised)
  • Clustering and dimensionality reduction techniques
  • Fundamentals of deep learning and neural networks
  • Understanding of AI applications such as NLP and Computer Vision
  • Model deployment using Flask/Streamlit
  • End-to-end AI/ML project execution
  
  2.Platform & Tools
  • Working with Microsoft Azure AI & Data Science services
  • Hands-on experience with real-world datasets and cloud-based workflows
  
  3.Professional & Career Skills
  • Problem-solving and analytical thinking
  • Translating data insights into business value
  • Presenting technical findings to non-technical stakeholders
  • Project documentation and teamwork
  • Overall, learners become industry-ready, project-capable, and certification-aligned AI & Data Science professionals.
  

• What measurable outcomes can learners expect after completing the course?
  • Ability to build, evaluate, and deploy ML models with measurable accuracy and performance metrics
  • Proficiency in data preparation and EDA, demonstrated through cleaned datasets and visual reports
  • Deployment of at least one AI/ML application using Flask or Streamlit
  • Certification readiness for Microsoft AI-102 and DP-100 (optional exam alignment)
  • Assessment-based validation through quizzes, assignments, and project evaluations
  • Top 20% learners eligible for internship opportunities at Credenca
  • Career readiness indicators, including project portfolios, presentation skills, and practical problem-solving capability

Curriculum & Learning Outcomes

• Will learners receive a certification upon completion?
  No.

• What is the name of the certification?
  N/A

• Are any tools or software required from the learner’s side?
  Laptop/ workstation requirements:
  • CPU: 6crore minimum,3GHz clock speed.
  • RAM: 16GB or higher.
  • GPU: Nvidia GPU with 4GB vRAM and CUDA support.
  • 512GB SSD (solid state drive) per workstation.
  • Network attached storage or cloud storage.
  
  Software Requirement
  • A recent version of windows.
  • Open-source platforms - VS code, Paycharm.
  • Advanced server for high computing power
  • Other requirements- High speed internet
  connection, UPS, Electricity supply.

Certification & Tools