Data Statistics with Full Stack Python

Video description

Master Data Science skills using Python and real time project and go from Beginner to Super Advance level

About This Video

  • Master complex theories, algorithms, and coding libraries in a very simple way
  • Master Data Science in Python

In Detail

Want to become a good Data Scientist? Then this is a right course for you.

This course was designed by IT professionals with a Master's in Mathematics and Data Science. We cover complex theories, algorithms, and coding libraries in a very simple way so they can be easily grasped by any beginner.

We walk you step-by-step through the World of Data science. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science from beginner to advanced level.

Publisher resources

Download Example Code

Table of contents

  1. Chapter 1 : Python Fundamentals
    1. Installation of Python and Anaconda
    2. Python Introduction
    3. Variables in Python
    4. Numeric Operations in Python
    5. Logical Operations
    6. If else Loop
    7. for while Loop
    8. Functions
    9. String Part1
    10. String Part2
    11. List Part1
    12. List Part2
    13. List Part3
    14. List Part4
    15. Tuples
    16. Sets
    17. Dictionaries
    18. Comprehensions
  2. Chapter 2 : Numpy
    1. Introduction
    2. Numpy Operations Part1
    3. Numpy Operations Part2
  3. Chapter 3 : Pandas
    1. Introduction
    2. Series
    3. DataFrame
    4. Operations Part1
    5. Operations Part2
    6. Indexes
    7. loc and iloc
    8. Reading CSV
    9. Merging Part 1
    10. groupby
    11. Merging Part2
    12. Pivot Table
  4. Chapter 4 : Some Fun With Maths
    1. Linear Algebra: Vectors
    2. Linear Algebra: Matrix Part1
    3. Linear Algebra: Matrix Part2
    4. Linear Algebra: Going From 2D to nD Part1
    5. Linear Algebra: 2D to nD Part2
  5. Chapter 5 : Inferential Statistics
    1. Inferential Statistics
    2. Probability Theory
    3. Probability Distribution
    4. Expected Values Part1
    5. Expected Values Part2
    6. Without Experiment
    7. Binomial Distribution
    8. Commulative Distribution
    9. PDF
    10. Normal Distribution
    11. z Score
    12. Sampling
    13. Sampling Distribution
    14. Central Limit Theorem
    15. Confidence Interval Part1
    16. Confidence Interval Part2
  6. Chapter 6 : Hypothesis Testing
    1. Introduction
    2. NULL and Alternate Hypothesis
    3. Examples
    4. One/Two Tailed Tests
    5. Critical Value Method
    6. z Table
    7. Examples
    8. More Examples
    9. p Value
    10. Types of Error
    11. t- distribution Part1
    12. t- distribution Part2
  7. Chapter 7 : Data Visualization
    1. Matplotlib
    2. Seaborn
    3. Case Study
    4. Seaborn on Time Series Data
  8. Chapter 8 : Exploratory Data Analysis
    1. Introduction
    2. Data Sourcing and Cleaning part1
    3. Data Sourcing and Cleaning part2
    4. Data Sourcing and Cleaning part3
    5. Data Sourcing and Cleaning part4
    6. Data Sourcing and Cleaning part5
    7. Data Sourcing and Cleaning part6
    8. Data Cleaning part1
    9. Data Cleaning part2
    10. Univariate Analysis Part1
    11. Univariate Analysis Part2
    12. Segmented Analysis
    13. Bivariate Analysis
    14. Derived Columns
  9. Chapter 9 : Simple Linear Regression
    1. Installing Anaconda using Jupyter Notebook
    2. Introduction to Machine Learning
    3. Types of Machine Learning
    4. Introduction to Linear Regression (LR)
    5. How LR Works?
    6. Some Fun With Maths Behind LR
    7. R Square
    8. LR Case Study Part1
    9. LR Case Study Part2
    10. LR Case Study Part3
    11. Residual Square Error (RSE)
  10. Chapter 10 : Real World Problem - Investment Requirement Analysis for a Company
    1. Investment Project Brief
    2. Investment Project_Data Cleaning Part 1
    3. Investment Project_Data Cleaning - Part 2
    4. Investment Project_Funding_Country_Sector Analysis Part 1
    5. Investment Project_Funding_Country_Sector Analysis Part 2
  11. Chapter 11 : Loan Analysis Project
    1. Problem Statement
    2. Lending Club Default Analysis - Data Understanding and Data Cleaning
    3. Data Analysis - Univariate Bivariate Analysis
    4. Segmented Univariate Analysis

Product information

  • Title: Data Statistics with Full Stack Python
  • Author(s): Geekshub Pvt. Ltd.
  • Release date: July 2019
  • Publisher(s): Packt Publishing
  • ISBN: 9781838986612