Course curriculum

  • 1

    Introduction to Data Science

    • Intro to Data science

    • Day 1 (13th January) Recording

    • Installing Jupyter in ubuntu

    • module1(introduction to data science).pdf
  • 2

    Python for Data Science

    • Day 3 (15th Jan) Recording

    • module2(python ppt).pdf

    • oprators

    • list

    • tuples

    • dictonary

    • 19th January Recording (Dictionary, Sets, Loops )

    • 20th January Recording (Loops , Functions, Packages)
  • 3

    Statistics

    • 21st Jan Recording (NumPy, Intro to Statistics)

    • module3(Statistics).pdf

    • Probability and Statistics for Engineering and the Sciences

    • 25th Jan Recording (Statistics, matplotlib)

    • 26th Jan Recording (matplotlib - various charts)
  • 4

    Numpy

    • module4 (numpy).pdf
  • 5

    Pandas

    • module5(pandas).pdf

    • 28th Jan Recording (pandas, numpy)

    • 29th Jan Recording (working with pandas)
  • 6

    EDA Case Study

    • module7(eda case study).pdf

    • 1st Feb Recording (EDA)
  • 7

    Introduction to Machine Learning

    • module8(Intro to ml).pdf
  • 8

    Simple Linear Regression

    • module9(simple linear regression).pdf

    • 5th Feb Recording
  • 9

    Multiple Linear Regression

    • module10(multiple linear regression and model building).pdf

    • 8th Feb Recording [Multiple and Polynomial Regression]
  • 10

    Classification Algorithm

    • module11(classification algorithm).pdf

    • 10th Feb Recording [KNN, Normalisation, Standardisation and various performance metrics]
  • 11

    Clustering Algorithm

    • module12(clustering technique).pdf

    • 16th Feb Recording

    • 17th Feb Recording

    • 18th Feb Recording [Recap]
  • 12

    Dimensionality Reduction

    • PCA(Principle Component and Analysis)
  • 13

    Doubt session

    • Doubts session
  • 14

    Artificial Neural Network

    • Nural network part 1

    • Neural Network part2
  • 15

    Final Assessment

    • Quiz

    • 1. HR Analytics Case Study

    • 2. Income Classification Model

    • 3. Black Friday Sales

    • Projecting Appraisal By evaluating Performance
  • 16

    Random forest/Flask deployment

    • Random forest and flask end to end ml deployment
  • 17

    Data Engineering

    • Day 2 Session Recording (S3)

    • day 3

    • day4

    • day5

    • day 6

    • day 7
  • 18

    Data science interview preparation question

    • Data science Complete interview preparation guide
  • 19

    Data engineering content

    • Aws Data engineering content

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