Courses

Curriculum

  • Basics of Maths/Computer Programming
  • Statistics
  • Introduction to R
  • Introduction to Python (Basic Python, data science related libraries, like NumPy, Scikit etc.
  • Introduction to SQL/DataBase (RDBMS, Oracle, MySQL etc.)
  • Introduction to Big Data (Introduction to NoSQL/Spark/Hadoop/exadata etc.)
  • Data Mining
  • Visualisation (tableau, powerBI etc.)
  • Advance statistics
  • Machine Learning Algorithm Overview
  • Supervised Learning Algorithms
  • Regression: Polynomial
  • Classification: Binary
  • Classification: KNN
  • Classification: Multiclass
  • Classification: SVM
  • Classification: Decision Trees
  • Classification: Random Forest
  • Concepts: Gradient Descent, Train_Test_Split, K Fold Cross Validation, Feature selection,Feature Scaling etc.
  • Concepts: Overfiting, Confusion Matrix, ROC curve etc.
  • Classification: Neural Networks
  • Unsupervised Learning
  • KMA/Clustering
  • Recommendation Engine/Movie Ratings etc.
  • Reinforcement Learning
  • Capstone Project
  • ML Hackathon
  • Building blocks
  • Advanced Python Libraries (Tensorflow)
  • Data Science on Cloud (Google Colab, Microsoft Azure ML Studio)
  • NLP
  • Basics of NLP, cosine algorithm etc.
  • Chatbot
  • Image/Video Processing
  • CNN, RCNN, Faster-RCNN, Yolo
  • Face Recognition
  • Object Recognition
  • Hackathon
  • Time Series Forecasting
  • Web & Social Media Analytics (Industry Case study)
  • Finance & Risk Analytics (Industry Case study)
  • Market & Retail Analytics (Industry Case study)
  • Supply Chain & Logistics Analytics (Industry Case study)
  • Group Presentation
  • 50:50 on Theory/Lab.. As for every 4 hours block, 2 hour would be theory + 2 hour lab/handson
  • Home Assignments
  • MCQ quizes as part of Theory part
  • Two hackathons of 8 hours each
  • For each Algorithm, coaching flow would be as under:
    • 1. Related Mathematics
    • 2. Concept/Algorithm
    • 3. Intro to Python or R library, and actual implementation
  • Sl. No.
  • Chapter
  • Chapter - 1
  • Introduction to Data Science
  • Chapter - 2
  • Introduction to programming in Python
  • Chapter - 3
  • Introduction to SQL programming
  • Chapter - 4
  • Working On MY SQL
  • Chapter - 5
  • Exploratory Data Analysis,Data Cleaning,Data Manipulation
  • Chapter - 6
  • Statistical Methods Of Decision Making
  • Chapter - 7
  • Machine Learning-Supervised Learning:Regression
  • Chapter - 8
  • Machine Learning:Supervised Learning:classification
  • Chapter - 9
  • Unsupervised Learning:Clustering
  • Chapter - 10
  • Unsupervised Learning:PCA
  • Chapter - 11
  • Ensemble Techniques:Bagging,Boosting
  • Chapter - 12
  • Data Visualization
  • Chapter - 13
  • Data Science Applications
  • Chapter - 14
  • Time series Forcasting
  • Chapter - 15
  • Text Mining and Sentimental Analysis
  • Chapter - 16
  • Capstone project

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Masters in Data Science Engineering, this Course and batch also available in other locations View

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