Video description
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.
What You Will Learn
- Master Data Science on Python
- Learn to use Numpy and Pandas for Data Analysis
- Learn all the math you need to understand Machine Learning algorithms
- Real-world case studies
- Learn to use Matplotlib for Python plotting
- Learn to use Seaborn for statistical plots
- Master end-to-end data science solutions
- Learn all statistical concepts you need to become a Machine Learning ninja
Audience
This course is for anyone who wants to become a Data Scientist.
About The Author
Geekshub Pvt. Ltd.: Geekshub is an online education company in the field of big data and analytics. Their aim as a team is to provide the best skill-set to their customers to make them job-ready and prepare them to crack any challenge. They have the best trainers for cutting-edge technologies such as machine learning, deep learning, Natural Language Processing (NLP), reinforcement learning, and data science. Their instructors are people who graduated from IIT, MIT and Standford. They are passionate about teaching the topics using curated real-world case studies that calibrate the learning experience of students.
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
You might also like
video
Basic Statistics and Regression for Machine Learning in Python
This course is for ML enthusiasts who want to understand basic statistics and regression for machine …
video
Learning Python Data Analysis
Python features numerous numerical and mathematical toolkits such as: Numpy, Scipy, Scikit learn and SciKit, all …
video
Python Machine Learning in 7 Days
Machine learning is one of the most sought-after skills in the market. But have you ever …
video
Data Cleansing Master Class in Python
Data preparation may be the most important part of a machine learning project. It is the …