Data Science with Python Table of Contents


Table of Content
 

 

1. Introduction to Data Science

  • Introduction to data science
  • What is Big Data
  • Using Data Wisely
  • Application Examples
  • Data Science Process Flow

2. Python Essentials for Data Science

  • Installing python and text editor
  • Introduction to python
  • Python Objects
  • Exercise 1
  • Indexing & Slicing
  • Python lists, tuples & dictionaries
  • Mutable & Immutable Objects
  • User-defined Functions
  • Built-ins & Methods
  • Control Flow Tools
  • Modules, Libraries & Packages
  • Introduction to Jupyter Notebook
  • Essential Data Science Packages

3. Data ScienceToolBox - 1

  • Introduction to Numpy
  • Creating a Numpy Array
  • More Array Creation techniques
  • Exercise 1
  • Indexing, Slicing& Iterating with Numpy Arrays
  • Exercise 2
  • Introduction to Matplotlib
  • Plotting with the plot function
  • Histogram
  • Scatter Plot
  • Box Plot
  • Adding a Legend
  • Creating Subplots
  • Lambda Functions
  • List Comprehensions

4. Data Science ToolBox - 2

  • Introduction to Pandas
  • Pandas Series
  • Pandas DataFrames
  • Pandas DataFrames 2
  • Input-Output Tools
  • Start your first Case Study
  • Iterators
  • Generators

5. Importing & Cleaning Data

  • Importing Flat Files
  • Pickled Files
  • Importing from a URL
  • Using APIs & JSONs
  • Inspecting Data Types
  • Inspecting Outliers
  • Finding Correlation & Duplicates
  • Handling Missing Data
  • Regular Expressions

6. Data Visualization with Statistical Thinking

  • Introduction to Seaborn Package
  • SeabornDistplot
  • SeabornDistplot 2
  • SeabornJointplot
  • SeabornHexplot
  • SeabornPairplot
  • Seaborn Boxplot
  • SeabornViolinplot&Swarmplot
  • SeabornCountplot
  • Heatmap
  • Exercise – Studying the feature correlation
  • Feature Selection
  • Feature Scaling
  • Applying Feature Scaling & Feature Extraction
  • Feature Extraction using PCA

7. Introduction to Machine Learning with Scikit-Learn

  • Introduction to Machine Learning
  • Supervised & Unsupervised Machine Learning
  • Classification
  • Regression
  • Clustering
  • Scikit-Learn - Naïve Bayes Algorithm
  • Scikit-Learn - Decision trees
  • Scikit-Learn - Random Forest
  • Scikit-Learn - Linear Regression
  • Scikit-Learn - Logistic Regression
  • Scikit-Learn - Support Vector Machines
  • Scikit-Learn – kNN(K Nearest Neighbours)
  • Scikit-Learn – K Means

8. Practice with Case Studies


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