Data Science Training Course Content


Data Science Overview

  • Introduction to Data Science
  • Different Sectors using Data Science
  • Python vs R
  • What is the role of a Data Analyst
  • What is the role of a Data Scientist
  • Opportunities in Data Science

Data Analysis Overview

  • Data Analysis Process
  • Knowledge Check
  • Exploratory Data Analysis
  • EDA - Quantitative Technique
  • Types of Variables
  • Types of Problems

Data Visualization Overview

  • Why Visualize everything ?
  • Importance of Visualization
  • Types of Visualization Strategies
  • Color Palettes
  • Types of Charts
  • When to Use Different Charts
  • Sub Plots
  • Plotting with Pandas
  • Matplotlib
  • Seaborn
  • Plotly - Advanced Data Visualization
  • Scientific Data Visualization
  • Driving Insights from Visualizations

Python Programming Language

  • History and Overview
  • Applications and Use Cases
  • Syntax, Comments, Variables
  • Data Types, Numbers, Casting
  • Strings, Booleans, Operators
  • Lists, Tuples
  • Sets, Dictionaries
  • Loops and Conditionals
  • Lambdas and Arrays
  • Classes and Objects
  • Inheritance
  • Iterators
  • Scope and Modules
  • Dates and Json
  • Regular Expressions
  • File Handling
  • Numpy
  • Pandas
  • Advanced Pandas

Statistical Analysis

  • Introduction to Statistics
  • Mean, Mode, Standard Deviation
  • Scales and Measures
  • Basic Terms and Terminologies
  • Use Cases and Problems
  • Summarizing Distribution
  • Graphing Distribution
  • Univariate Data
  • Bivariate Data
  • Multivariate Data
  • Normal Distributions
  • Probability
  • Sampling Distributions
  • Advanced Graphs
  • Estimation
  • Analysis of Variance
  • Standardization
  • Normalization

Supervised Machine Learning

  • Introduction
  • Types of Supervised Techniques
  • Regression Analysis
    • Types of Regression Algorithms
    • Linear Regression
    • Logistic Regression
    • Lasso Regression
    • Ridge Regression
    • Elastic Net Regression
    • Metrics for Regression Analysis
  • Classification Analysis
    • Decision Trees
    • Random Forest
    • Support Vector Machines
    • K Nearest Neighbors
    • Naive Bayes Theorem
    • AdaBoost Classifier
    • Metrics for Classification

PROJECT 1: Breast Cancer Dataset



PROJECT 2: Employee Dataset



PROJECT 3: Black Friday Dataset




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