Essential Statistics for Data Science

Essential Statistics for Data Science, available at Free, has an average rating of 4.34, with 12 lectures, based on 258 reviews, and has 5761 subscribers.

You will learn about Understand Statistics Basics Statistics – Data Types and Application Harnessing Data – Sampling Techniques Exploratory Data Analysis This course is ideal for individuals who are Data Science Aspirants, who want to get good foundation of Statistics It is particularly useful for Data Science Aspirants, who want to get good foundation of Statistics.

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Summary

Title: Essential Statistics for Data Science

Price: Free

Average Rating: 4.34

Number of Lectures: 12

Number of Published Lectures: 12

Number of Curriculum Items: 12

Number of Published Curriculum Objects: 12

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Understand Statistics Basics

Statistics – Data Types and Application

Harnessing Data – Sampling Techniques

Exploratory Data Analysis

Who Should Attend

Data Science Aspirants, who want to get good foundation of Statistics

Target Audiences

Data Science Aspirants, who want to get good foundation of Statistics

Data Science is an inter disciplinary fields combining Statistics, Programming, Machine Learning and Business Knowledge.

Statistics is the key field in analyzing the data to extract insights for business decisions.  Though, Statistics as a field is vast, a limited concepts involving quantitative methods are useful in data science.

The science of collecting, describing, and interpreting data is popularly known as Statistical leveraging in Data Science

Two areas of Statistics in Data Science:

Descriptive statistics– Methods of organizing, summarizing, and presenting data in an informative way

Inferential statistics– The methods used to determine something about a population on the basis of a sample

A strong statistics foundation is mandatory for  data science professionals, as statistics is basis for any data analysis.

Statistics is also predominantly used in Machine Learning for feature engineering.

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This is an introductory course on Statistics for Data Science for Beginners.

There are no hard prerequisites for this course. Anyone interested can pursue.

The goal of this course is to provide a statistics with simple examples and learning the learners to get comfortable with Statistics as they move on to more advanced statistical methods.

Curriculum

INTRODUCTION   

1. Statistics  Overview – Introduction

2. Statistics Basic Terminology

3. Types of Data

HARNESSING DATA

1. Introduction –  Sampling Methods

2. Sampling Methods

3. Cluster Sampling

4. Systematic Sampling

5. Biased Sampling

6. Sampling Error

EXPLORATORY DATA ANALYSIS

1. EDA – Central Tendencies

2. EDA – Variability

3. EDA – Histogram, Z-Value, Normal Distribution

Happy Learning

Team DataMites

Course Curriculum

Chapter 1: Introduction

Lecture 1: Statistics Overview – Introduction

Lecture 2: Statistics Basic Terminology

Lecture 3: Types of Data

Chapter 2: Harnessing Data

Lecture 1: Introduction – Sampling Methods

Lecture 2: Sampling Methods

Lecture 3: Cluster Sampling

Lecture 4: Systematic Sampling

Lecture 5: Biased Sampling

Lecture 6: Sampling Error

Chapter 3: Exploratory Data Analysis (EDA)

Lecture 1: EDA – Central Tendencies

Lecture 2: EDA – Variability

Lecture 3: EDA – Histogram, Z-Value, Normal Distribution

Instructors

DataMites Solutions
A Global institute for Data Science

Rating Distribution

1 stars: 5 votes

2 stars: 7 votes

3 stars: 48 votes

4 stars: 87 votes

5 stars: 111 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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