Data Science Course in Kolkata

Data science with Python

Develop Data Scientist skills with the help of the most effortless programming language Python.

Course curriculum

  • Introduction to Data
  • Data Warehousing and Real time Analytics - Theory
  • Introduction to Python
  • Object Oriented Programming - 1
  • Object Oriented Programming - 2 (OOP extension)
  • Numerical Data Science with Numpy
  • Advance Numpy and Exploratory Data Analysis theory
  • EDA with pandas
  • Applied statistics (Hypothesis Testing)
  • Applied statistics (Chi-square Test, Anova & Correlation)
  • Cleaning methodology
  • Visualization - Matplotlib and seaborn (Functional and OOP approach)
  • Feature Engineering-1 (Best Feature Selection, PCA, ICA)
  • Feature Engineering-2 (practical)
  • Apply supervised algorithm-theory
  • Apply supervised algorithm-practical
  • Apply non-supervised algorithm-theory
  • Apply non-supervised algorithm-practical
  • Time series & forecasting
  • Deployment class-I
  • Deployment class-II
  • Final class
  • Capstone Project

Learning Outcome

  • Industrial knowledge of data warehousing
  • Real time data analytics
  • Basic python learning
  • Object oriented python
  • Implementation of python libraries(numpy, pandas, matplotlib, seaborn)
  • Descriptive statistics knowledge
  • Supervised & unsupervised model implementation
  • Deployment with popular framework

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Data science with R

Introduce data scientist with full of data science skills with R

Course curriculum

  • Introduction to R and its basics
  • Hypothesis testing
  • Data wrangling and Data manipulation
  • Object Oriented Programming - 1
  • Data visualization
  • Feature engineering
  • Linear regression
  • Logistics regression
  • Decision tree
  • Random forest
  • Cluster analysis using K-means
  • K-Nearest Neighbour
  • Dimentinality reduction using PCA
  • Time series analysis
  • Time series forecasting
  • Introduction to Shiny
  • Dashboard development
  • Deployment of an ML model in Shiny

Learning Outcome

  • Industrial knowledge of data analysis
  • Descriptive analysis of data
  • Predictive analytics of data
  • Supervised & unsupervised model implementation
  • Deployment of ML model
  • Industrial dashboard development

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