Data Science with Python

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Data Science with Python

    Module 1: Introduction to Data Science
    • What is Data Science?
    • What is Machine Learning?
    • What is Deep Learning?
    • What is AI?
    • Data Analytics & it’s types

    Module 2: Introduction to Python

    • What is Python?
    • Why Python?
    • Installing Python
    • Python IDEs
    • Jupyter Notebook Overview

    Module 3: Python Basics

    • Python Basic Data types
    • Lists
    • Slicing
    • IF statements
    • Loops
    • Dictionaries
    • Tuples
    • Functions
    • Array
    • Selection by position & Labels

    Module 4: Python Packages

    • Pandas
    • Numpy
    • Sci-kit Learn
    • Mat-plot library

    Module 5: Importing data

    • Reading CSV files
    • Saving in Python data
    • Loading Python data objects
    • Writing data to csv file

    Module 6: Manipulating Data

    • Selecting rows/observations
    • Rounding Number
    • Selecting columns/fields
    • Merging data
    • Data aggregation
    • Data munging techniques

    Module 7: Statistics Basics

    • Central Tendency

      Mean

      Median

      Mode

      Skewness

      Normal Distribution

    • Probability Basics

      What does mean by probability?

      Types of Probability

      ODDS Ratio?

    • Standard Deviation

      Data deviation & distribution

      Variance

    • Bias variance Trade off

      Underfitting

      Overfitting

    • Distance metrics

      Euclidean Distance

      Manhattan Distance

    • Outlier analysis

      What is an Outlier?

      Inter Quartile Range

      Box & whisker plot

      Upper Whisker

      Lower Whisker

      catter plot

      Cook’s Distance

    • Missing Value treatments

      What is a NA?

      Central Imputation

      KNN imputation

      Dummification

    • Correlation

      Pearson correlation

      Positive & Negative correlation

    • Error Metrics

      Classification

      Confusion Matrix

      Precision

      Recall

      Specificity

      F1 Score

    • Regression

      MSE

      RMSE

      MAPE

      Module 8: Machine Learning

        Module 9: Supervised Learning

        • Linear Regression

          Linear Equation

          Slope

          Intercept

          R square value

        • Logistic regression

          ODDS ratio

          Probability of success

          Probability of failure

          ROC curve

          Bias Variance Tradeoff

      Module 10: Unsupervised Learning

      • K-Means
      • K-Means ++
      • Hierarchical Clustering

      Module 11: Other Machine Learning algorithms

      • K – Nearest Neighbour
      • Naïve Bayes Classifier
      • Decision Tree – CART
      • Decision Tree – C50
      • Random Forest