The Complete Data Science Bootcamp 2023

Reach unique mathemtical skills as a data analyst or data scientist

What you'll learn

Learn Unique mathematical skills for Data Science

powerful tools of statistics

Know the Great value of statistical analysis that R have over Python

know how to do reporting for your analysis and draw strong conclusion

Requirements

now the basic arithmetic

know some high school math

Description

According to the U.S. Bureau of Labor Statistics, employment of data scientists is projected to grow 36 percent from 2021 to 2031, which is much faster than the average for all occupations.Our course as unlike a lot of bootcamp on datascience that focus on soft skils of it rather than a hard skills my course is actually the oposite, we're gonna discover a long the way a power of statistics,linear algebra that help draw strong report and conclusions, make a successful predictions and optimize decision making we're gonna focus on two major software that statisticians used which is EXCEL AND R PROGRAMMING LANGUAGE, don't forget that you will miss a high value and practical course to shine on your career as a data analyst, data scientist and competitive advantage over the other data analyst or scientist, with my low price offered and interaction and explanation with pencil and paper if you are ambitious to go far that of skills you are in the right place to start so you can go well beyond what i teach on this course you will benefits from strong good fondation, so don't hesitate start this journey of learning and you will be surprised by the value this course will provide to you.

Overview

Section 1: EXCEL

Lecture 1 Mean/Mode/Median

Lecture 2 Quartile 25/Quartile 75

Lecture 3 Organizing the data in the table

Lecture 4 Fill the table

Lecture 5 Variance and standard deviation

Lecture 6 Lorenz curve and Gini Index 1

Lecture 7 Lorenz curve and Gini index 2

Lecture 8 Linear regression

Section 2: Linear algebra

Lecture 9 Arithmetic sequences

Lecture 10 Geometric Sequences

Lecture 11 Operation research

Section 3: R programming language and SQL database

Lecture 12 Confidence Interval of the mean

Lecture 13 Confidence interval of the proportion

Lecture 14 Confidence interval of the variance

Lecture 15 Confidence interval of the standard deviation

Lecture 16 Type 1 and 2 Error

Lecture 17 P-Hacking

Lecture 18 Testing Hypothesis of the mean

Lecture 19 testing hypothesis of the variance and standard deviation

Lecture 20 Testing Hypothesis of the proportion

Lecture 21 binomial distribution

Lecture 22 geometric distribution

Lecture 23 poisson distribution

Lecture 24 Conditional probability calculation

Lecture 25 Density function

Lecture 26 derivative interpretation

Lecture 27 exponential distribution

Lecture 28 Scientific Premise

Lecture 29 Let's summarize

Lecture 30 Reporting,decision making and successful predictions

Students,Professionals in the field of data science,Entrepreneurs,Stock market investors