Data Science with Python

Overview

This Hands-on practical training program on Data Science takes participants through the basics of Python and Statistics before starting and exploring Data Science in depth. This training also takes participants through exploratory as well as Real time scenarios in Data Science and also gives introduction to Machine Learning.
Duration
3 Days

Pre-Requisites
  • Basic understanding in Python Programming
  • Knowledge in Mathematics & Statistics would be helpful

    • Course Outline

    • Data Science Introduction
    • Data Science Toolkit
    • Job outlook
    • Prerequisite, Target Audience
    • Data Science Project Lifecycle – CRISP-DM Model
    • Statistics Concepts
    • Random variable
    • Type of Random variables
    • Central Tendencies 
      • Mean
      • Mode
      • Median
      • Probability
      • Probability Distribution of Random variables, PMF, PDF, CDF
    • Type of RV 
      • Nominal
      • Ordinal
      • Interval
      • Ratio
      • Variance
      • Standard Deviation
    • Normal Distribution,
    • Standard Normal Distribution
    • Binomial Distribution
    • Poisson Distribution
    • Sampling
    • Inferential Statistics
    • Sampling Distribution
    • Central Limit Theorem
    • Simulation
    • Null and Alternative Hypothesis
    • Hypothesis Testing
    • 1 tail test and 2 tail test
    • type I and Type II error
    • z test & t test
    • Introduction to Python
    • Anaconda & Spyder
    • Installation & Configuration
    • Data Structures in Python
      • List
      • Tuples
      • Array in NumPy
      • Matrices
      • Data frame in Pandas
    • Control Structure & Functions 
      • If-Else
      • For loop
      • While loop
      • Slicing
      • dicing 
      • filter operations
    • Graphics and Data Visualization libraries in Python
    • Plotly
    • Matplotlib
    • Seaborn
    • other useful packages/functions in Python
    • Exploratory Data Analysis Exercise in Python
    • Introduction to Machine Learning
    • Supervised and Unsupervised ML
    • Parametric/Non-parametric Machine
    • Learning Algorithms
    • Machine Learning Models
      • Linear Regression
      • Logistic Regression
      • Classification & KNN
      • Decision trees
      • Random Forest
      • Clustering K Means & hierarchical Clustering,
      • Time Series Analysis
    • ARIMA Models,
    • Support Vector Machine
    • Model Validation/Cross-validation techniques
    • Parameter tuning,
    • Model evaluation metrics
    • MSE
    • RMSE
    • R square
    • Adjusted R Square
    • Confusion Matrix
    • Bias and Variance
    • Underfitting
    • over Fitting

    ML Case Studies on

      • Regression
      • Classification
      • Decision Tree
      • Random Forest
      • Clustering
      • Time Series Analysis

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