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Statistics Probability for Data Science

Course Line

Our "Statistics Probability for Data Science" course is designed for individuals eager to thrive in the data-driven landscape. Covering fundamental and advanced statistical concepts, this course equips you with the skills needed to analyze, interpret, and derive insights from data. Join us to unlock the power of statistics in the realm of data science.

Statistics Probability for Data Science
Course Price:
Original price was: £194.00.Current price is: £19.99.
Course Duration:
18 hours, 33 minutes
Total Lectures:
125
Total Students:
41
Average Rating:
4.5
Embark on a transformative journey into the heart of data science with our comprehensive "Statistics Probability for Data Science" course. This program covers foundational statistical principles and extends into advanced machine learning applications, providing a holistic understanding of statistical concepts in a data-driven world.

What Will You Learn?

  • Foundation of Statistics: Establish a robust understanding of statistical fundamentals, laying the groundwork for data analysis.
  • Exploratory Data Analysis: Master techniques to explore and interpret datasets, uncovering patterns and trends crucial for decision-making.
  • Probability: Dive into the principles of probability, a key element in making informed and data-driven decisions.
  • Inferential Statistics: Learn techniques to draw meaningful inferences from data, a critical skill for decision-making under uncertainty.
  • Linear Regression: Explore the foundational concepts of linear regression, a powerful tool for understanding and predicting relationships within data.
  • Logistic Regression: Understand logistic regression, a key method for modeling binary outcomes in various data science applications.
  • Miscellaneous Stats Concepts in Machine Learning Areas: Gain insights into various statistical concepts relevant to diverse machine learning domains.

Who Should Take The Course?

  • Aspiring data scientists and analysts seeking a comprehensive understanding of statistical concepts.
  • Professionals in business, finance, healthcare, or any field where data analysis is essential for decision-making.

Requirements

  • Basic knowledge of mathematics and algebra.
  • Access to a computer or mobile device with internet connectivity.
  • Eagerness to explore and apply statistical concepts in real-world data scenarios.

Course Curriculum

    • Introduction to Statistics 00:17:00
    • Types of Statistical Analysis – Descriptive Statistics 00:11:00
    • Types of Statistical Analysis – Inferential Statistics 00:16:00
    • How Statistics and Machine Learning are Related 00:10:00
    • Understanding the Types of Data 00:17:00
    • Sampling Techniques 00:24:00
    • Descriptive Statistics – Measure of Central Tendency 00:13:00
    • Descriptive Statistics – Measures of Dispersion – Range _ Interquartile Range 00:13:00
    • Descriptive Statistics – Measures of Dispersion – Variance _ Standard Deviation 00:08:00
    • Hands On – Exercise with Python 00:12:00
    • Descriptive Statistics – Measures of Shape 00:17:00
    • Descriptive Statistics – Measures of Position 00:08:00
    • Descriptive Statistics – Standard Scores 00:10:00
    • Descriptive Statistics – Hands On 00:10:00
    • Problem Statement – Wine Reviews Data Set Analysis 00:02:00
    • Solution for Project 1 00:16:00
    • Project 2 – Customer Income Data Analysis 00:02:00
    • Solution for Project 2 00:10:00
    • Project 3 – US Arrests Dataset 00:02:00
    • Solution for Project 3 – US Arrests Dataset 00:14:00
    • Solution for Big Mart Data Analysis 00:13:00
    • Project 4 – BigMart Sales data analysis 00:03:00
    • Introduction to Exploratory Data Analysis 00:09:00
    • Types of Data Analysis 00:04:00
    • Univariate Non Graphical EDA _ Outlier Analysis 00:14:00
    • Univariate Graphical EDA _ Hands On 00:25:00
    • Multivariate Non Graphical EDA 00:17:00
    • Multi variate Graphical EDA 00:17:00
    • Steps in EDA 00:08:00
    • Summary of Graphical EDA Techniques 00:03:00
    • Hands On EDA on Titanic Data Set 01:08:00
    • Project 5 – Crimes in Boston City 00:03:00
    • Project 5 – Solution 00:15:00
    • Project 6 – PUBG Game Analysis 00:03:00
    • Project 6 – PUBG Game Analysis – Solution 00:38:00
    • Project 7 – FIFA Game Analysis 00:02:00
    • Project 7 – Solution 00:11:00
    • Project 8 – Covid19 Data Analysis 00:02:00
    • Project 8 Solution 00:18:00
    • Introduction to Probability 00:11:00
    • Key Terminology of Probability 00:09:00
    • Rules of Probability 00:06:00
    • Marginal Probability , Joint Probability 00:18:00
    • Disjoint Events and Non Disjoint events 00:06:00
    • Independent and Dependent events 00:05:00
    • Product Rule of Dependent _ Independent Events 00:11:00
    • Task with Manifold Bank and compute probability 00:23:00
    • Bayes Theorem 00:05:00
    • Bayes Theorem in Data Science 00:02:00
    • Hands On Bayes Algorithm in Python 00:17:00
    • Random Variables 00:07:00
    • Various Distribution functions 00:13:00
    • Central Limit Theorem and Hands On 00:09:00
    • Applications of Probability Distributions 00:07:00
    • Hands On Transform the data to get Normal Distribution curve 00:03:00
    • Example Problems for Probability 00:24:00
    • Project 9 – Cars Dataset _ Solution 00:09:00
    • Hands On – Bayes Theorem 00:09:00
    • Project 10 – Hands On – Normal Distribution _ CDF 00:07:00
    • Hypothesis Testing _ Steps of Hypothesis testing 00:08:00
    • ZTest and Example Problem 00:06:00
    • ZTest Solution Hands On 00:03:00
    • 1 Sample t-test 00:05:00
    • 1 sample t-test Hands On 00:03:00
    • 2 Sample t-test 00:04:00
    • 2 sample t-test Hands On 00:02:00
    • Paired Sample t-test 00:03:00
    • Hands On – Paired Sample t-test 00:01:00
    • Chi-Square Goodness of Fit 00:04:00
    • Hands On – Chi Square test 00:02:00
    • Anova 00:01:00
    • Hands On – Anova 00:04:00
    • Project 11 – Inferential Statistics – cars 00:02:00
    • Project 11 – Solution 00:07:00
    • Project 12 – Blood Pressure health dataset 00:01:00
    • Project 12 – Solution 00:04:00
    • Project 13 – Students admissions dataset 00:01:00
    • Project 13 – Solution 00:03:00
    • Introduction to Inferential Statistics 00:08:00
    • Key Terminology of Inferential Statistics 00:03:00
    • Hands On – Population – Sample 00:07:00
    • Types of Statistical Inference 00:07:00
    • Confidence Interval – Margin of Error – Confidence Interval Estimation 00:07:00
    • Demo – Margin of Error and Confidence Interval 00:06:00
    • Introduction to Regression , What , Why and Types of Problem we can solve 00:10:00
    • Assumptions of Linear Regression 00:03:00
    • Intuition of Linear Regression 00:07:00
    • Linear Regression with Normal Equation 00:10:00
    • Apply Linear Regression using Sklearn – Hands On 00:11:00
    • Checking Assumption of Linear Regression – Hands On 00:15:00
    • How Good is your fit 00:03:00
    • How Minimisation of Error is performed – Gradient Descent 00:18:00
    • Gradient Descent Hands On Part 1 00:19:00
    • Gradient Descent Hands On Part 2 00:12:00
    • Project 14 – Hands On – Implementation of Linear Regression using StatsModels 00:01:00
    • Project 14 – Solution 00:16:00
    • Project 15 – Salary Prediction Problem Statement 00:01:00
    • Project 15 – Solution 00:09:00
    • Project 16 – House Price Prediction Dataset 00:02:00
    • Project 16 – Solution 00:07:00
    • Project 17 – Medical Cost Prediction 00:02:00
    • Project 17 Solution 00:09:00
    • Project 18 – Company Profit prediction 00:02:00
    • Project 18 – Solution 00:08:00
    • Introduction to Logistic Regression 00:04:00
    • Hands On – Logistic Regression Plot 00:10:00
    • Assumptions of Logistic Regression 00:02:00
    • Logistic Regression from Scratch 00:15:00
    • Project 19 – Diabetes Prediction 00:01:00
    • Project 19 – Solution 00:10:00
    • Project 20 – Heart Disease Prediction 00:01:00
    • Project 20 – Solution 00:08:00
    • Project 21 – Titanic Survival Dataset 00:01:00
    • Project 21 – Solution 00:08:00
    • Project 22 – Nursery Student Dataset 00:01:00
    • Project 22 – Solution 00:04:00
    • Resampling Technique 00:05:00
    • Cross validation Techniques Hands On 00:23:00
    • Project 23 – Flight Price Prediction 00:02:00
    • Project 23 Solution 00:17:00
    • Project 24 – Concrete Compressive Strength 00:02:00
    • Project 24 – Solution 00:11:00
    • Project 25 – US Baseball Salary prediction 00:01:00
    • Project 25 – Solution 00:09:00
    • Order Certificate 00:05:00

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