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What you'll get

  • Job Credibility
  • Certification Valid for Life
  • On-demand video*
  • E-Book
  • Self-Paced Learning
  • Certificate of Completion

Exam details

  • Mode of Exam : Online
  • Duration : 1 Hour
  • Multiple Choice Questions are asked
  • No. of Questions are asked : 50
  • Passing Marks : 25 (50%)
  • There is no negative marking

Welcome to this course "Data Statistics with Full Stack Python" where you can easily understand by the name that you are going to study data statistics with the tint touch of full stack Python. The topics are discussed in this course step by step. For this course, you will require no prior knowledge or experience, just a willingness to learn. 

In this course you are going to learn about the fundamentals of Python like:

  • Installation of Python and Anaconda
  • Python Introduction
  • Variables in Python
  • Numeric Operations in Python
  • Logical Operations
  • If else Loop
  • for while Loop
  • Functions
  • String 
  • List
  • Tuples
  • Sets
  • Dictionaries
  • Comprehensions

You are going to learn these topics in detail.  You will be introduced with some new topics like NumPy, Pandas, inferential statistics, z score, sampling, PDF, Probability theory, and some detailed discussion with maths. You are also going to learn hypothesis testing like null and alternative hypothesis, Matplotlib, Seaborn, Case Study, Seaborn on Time Series Data and many other topics. Moreover, you are also going to learn about data sourcing in a maximized manner, univariate analysis, bivariate analysis, simple linear regression and loan analysis project. 

If you want to make a career in data science, software developer or any programming enthusiast, you can learn this course.

Students who are pursuing Computer science, or interested in learning Pandas, NumPy can take this course too. 

This course discussed all the topics along with its practical implementation making you an expert in the domain.

Anyone from any background can take up this course as it is designed in such a way that even a newbie in the domain can learn the course without any barrier. You will be given lifetime access to this course and a certificate too. This course will provide you with a strong base and you will get many job opportunities as Data Statistics and Python both are highly demanded by these IT industries nowadays. So, what are you waiting for? Take this course and start learning.

Course Content

Total: 106 lectures
  • Installation of Python and Anaconda
  • Python Introduction
  • Variables in Python
  • Numeric Operations in Python
  • Logical Operations
  • If else Loop
  • for while Loop
  • Functions
  • String Part1
  • String Part2
  • List Part1
  • List Part2
  • List Part3
  • List Part4
  • Tuples
  • Sets
  • Dictionaries
  • Comprehensions
  • Introduction
  • Numpy Operations Part1
  • Numpy Operations Part2
  • Introduction
  • Series
  • DataFrame
  • Operations Part1
  • Operations Part2
  • Indexes
  • loc and iloc
  • Reading CSV
  • Merging Part 1
  • groupby
  • Merging Part2
  • Pivot Table
  • Linear Algebra: Vectors
  • Linear Algebra: Matrix Part1
  • Linear Algebra: Matrix Part2
  • Linear Algebra: Going From 2D to nD Part1
  • Linear Algebra: 2D to nD Part2
  • Inferential Statistics
  • Probability Theory
  • Probability Distribution
  • Expected Values Part1
  • Expected Values Part2
  • Without Experiment
  • Binomial Distribution
  • Commulative Distribution
  • PDF
  • Normal Distribution
  • z Score
  • Sampling
  • Sampling Distribution
  • Central Limit Theorem
  • Confidence Interval Part1
  • Confidence Interval Part2
  • Introduction
  • NULL and Alternate Hypothesis
  • Examples
  • One/Two Tailed Tests
  • Critical Value Method
  • z Table
  • Examples
  • More Examples
  • p Value
  • Types of Error
  • t- distribution Part1
  • t- distribution Part2
  • 7
  • Data Visualization
  • Matplotlib
  • Seaborn
  • Case Study
  • Seaborn on Time Series Data
  • 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
  • Installing Anaconda & using Jupyter Notebook
  • Introduction to Machine Learning
  • Types of Machine Learning
  • Introduction to Linear Regression (LR)
  • How LR Works?
  • Some Fun With Maths Behind LR
  • R Square
  • LR Case Study Part1
  • LR Case Study Part2
  • LR Case Study Part3
  • Residual Square Error (RSE)
  • Investment Project Brief
  • Investment Project_Data Cleaning Part 1
  • Investment Project_Data Cleaning - Part 2
  • Investment Project_Funding_Country_Sector Analysis Part 1
  • Investment Project_Funding_Country_Sector Analysis Part 2
  • Problem Statement
  • Lending Club Default Analysis - Data Understanding and Data Cleaning
  • Data Analysis - Univariate & Bivariate Analysis
  • Segmented Univariate Analysis


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