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.
Enroll now: Essential Statistics for Data Science
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
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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
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