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
Table of contents
- Chapter 1 : Python Fundamentals
- Chapter 2 : Numpy
- Chapter 3 : Pandas
- Chapter 4 : Some Fun With Maths
-
Chapter 5 : Inferential Statistics
- Inferential Statistics
- Probability Theory
- Probability Distribution
- Expected Values Part1
- Expected Values Part2
- Without Experiment
- Binomial Distribution
- Commulative Distribution
- Normal Distribution
- z Score
- Sampling
- Sampling Distribution
- Central Limit Theorem
- Confidence Interval Part1
- Confidence Interval Part2
- Chapter 6 : Hypothesis Testing
- Chapter 7 : Data Visualization
-
Chapter 8 : Exploratory Data Analysis
- Introduction
- Data Sourcing and Cleaning part1
- Data Sourcing and Cleaning part2
- Data Sourcing and Cleaning part3
- Data Sourcing and Cleaning part4
- Data Sourcing and Cleaning part5
- Data Sourcing and Cleaning part6
- Data Cleaning part1
- Data Cleaning part2
- Univariate Analysis Part1
- Univariate Analysis Part2
- Segmented Analysis
- Bivariate Analysis
- Derived Columns
- Chapter 9 : Simple Linear Regression
- Chapter 10 : Real World Problem - Investment Requirement Analysis for a Company
- Chapter 11 : Loan Analysis Project
Product information
- Title: Data Statistics with Full Stack Python
- Author(s):
- Release date: July 2019
- Publisher(s): Packt Publishing
- ISBN: 9781838986612
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