Data science tools: Basic of Statistics

Data science tools: Basic of Statistics, available at $19.99, has an average rating of 4.95, with 32 lectures, based on 33 reviews, and has 1173 subscribers.

You will learn about Students will be able to analyze, explain and interpret the data They will understand the relationship and dependency between the data and how to make the prediction Students will understand different method of data analyses such as measure of central tendency (mean, median, mode), measure of dispersion (variance, standar Students will have basic understanding of probability and Bayes theorem They will come to know about rates, ratio, odd ration and screening test This course is ideal for individuals who are This course will help to build carrier in data science and machine learning specialization It is particularly useful for This course will help to build carrier in data science and machine learning specialization.

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Summary

Title: Data science tools: Basic of Statistics

Price: $19.99

Average Rating: 4.95

Number of Lectures: 32

Number of Published Lectures: 32

Number of Curriculum Items: 32

Number of Published Curriculum Objects: 32

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Students will be able to analyze, explain and interpret the data
They will understand the relationship and dependency between the data and how to make the prediction
Students will understand different method of data analyses such as measure of central tendency (mean, median, mode), measure of dispersion (variance, standar
Students will have basic understanding of probability and Bayes theorem
They will come to know about rates, ratio, odd ration and screening test

Who Should Attend

This course will help to build carrier in data science and machine learning specialization

Target Audiences

This course will help to build carrier in data science and machine learning specialization

· Students will gain knowledge about the basics of statistics

· They will have clear understanding about different types of data with examples which is very important to understand data analysis

· Students will be able to analyze, explain and interpret the data

· They will understand the relationship and dependency by learning Pearson’s correlation coefficient, scatter diagram and linear regression analysis between the variables and will be able to know make the prediction

· Students will understand different method of data analyses such as measure of central tendency (mean, median, mode), measure of dispersion (variance, standard deviation, coefficient of variation), how to calculate quartiles, skewness and box plot

· They will have clear understanding about the shape of data after learning skewness and box plot, which is an important part of data analysis

· Students will have basic understanding of probability and how to explain and understand Bayes theorem with the simplest example

· Students will have basic understanding of discrete probability distribution such as Binomial, Poisson and continuous probability distribution such as normal distribution with details example

· They will come to know about rates, ratio, odd ratio and screening test

· They will have clear knowledge about screening test and confusion matrix with details example

· They will gain a clear idea about fundamental of statistics

· Specially, who are interested to advance their carriers in data science and machine learning should complete the course

Course Curriculum

Chapter 1: Introduction :Data and Statistics

Lecture 1: Introduction

Lecture 2: Instructor

Lecture 3: What is Statistics?

Lecture 4: Data

Chapter 2: Summary measures: Central Tendency

Lecture 1: Mean

Lecture 2: Median

Lecture 3: Mode

Chapter 3: Summary measures:Measures of Dispersion

Lecture 1: Measures of Dispersion_variance_sd

Lecture 2: Coefficient_variation_CV

Chapter 4: Shape of data: Measures of Skewness

Lecture 1: Quartiles_quartile deviation

Lecture 2: Box plot

Lecture 3: Skewness_shape of the data

Lecture 4: Coefficient of Skewness

Chapter 5: Correlation and Regresson analysis

Lecture 1: correlation-coefficient

Lecture 2: correlation-Scatter-diagram

Lecture 3: Regression-analysis

Lecture 4: Regression-example

Chapter 6: Probability-Bayes-Theorem

Lecture 1: Basic of Probability

Lecture 2: Bayes theorem

Chapter 7: Discrete probability distribution

Lecture 1: Binomial distribution

Lecture 2: Poisson distribution

Chapter 8: Continious probability distributio:Normal distribution

Lecture 1: normal_distribution

Lecture 2: normal_examples

Chapter 9: Rates-Ratio-odd ratio-OR

Lecture 1: Rates_Ratio_incidence_prevalence

Lecture 2: Odds-Odd Ratio

Chapter 10: Screening_test_confusion_matrix

Lecture 1: Screening_test_confusion_matrix_1

Lecture 2: Screening_test_confusion_matrix_2_details

Lecture 3: Screening_test-example

Lecture 4: Screening_test-bayes-theorem

Chapter 11: Fundamental of statistical hypothesis tests

Lecture 1: defination of statistical hypothesis

Lecture 2: 11.2-Steps of hypothesis testing-null hypothesis and alternative hypothesis

Lecture 3: 11.3-continue-Steps of hypothesis testing-more-steps

Instructors

Mohammad Rafiqul Islam
Statistician and Datascientist

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3 stars: 1 votes
4 stars: 3 votes
5 stars: 29 votes

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