Statistics for Data Science and Business Analysis

4h 49m 30s
English
Paid

Unlock the Power of Statistics!Are you ready to dive into a career that leverages the power of data? Whether you're aiming to be a Marketing Analyst, Business Intelligence Analyst, Data Analyst, or Data Scientist, our course "Statistics for Data Science and Business Analysis" is your gateway to mastering statistical analysis with practical templates in Excel.

Course Overview

This course is designed to equip you with the fundamental skills necessary to understand complex statistical analyses and apply them in real-world scenarios. Here’s what makes our course stand out:

  • Easy to comprehend materials
  • Comprehensive and practical approach
  • Direct and to-the-point instructions
  • Rich with exercises and resources
  • Data-centric curriculum
  • Introduction to statistical and scientific terminology
  • Insights into data visualization
  • Understanding of quantitative research pillars

Unlike other courses, our program provides critical thinking skills necessary for analyzing why specific statistical tests are used, going beyond mere software automation.

Our Passion for Teaching

Teaching is our passion. We have meticulously crafted this course over several months to deliver maximum value. Expect an engaging experience filled with high-quality animations, comprehensive course materials, quizzes, handouts, and a glossary for new terms.

What Sets This Course Apart?

  • HD video and animations for an engaging learning experience
  • Guidance from a qualified instructor with international competition experience in mathematics and statistics
  • Complete coverage of major statistical topics essential for data-related careers
  • Extensive case studies to reinforce learning
  • Responsive support with questions answered within one business day
  • Dynamic pacing to make the most of your time

Why These Skills Matter

  1. Enhance your salary/income: Data science roles are highly sought after in today’s tech-driven business environments.
  2. Facilitate your promotions: Quantitative analysis skills empower you to substantiate business ideas, paving the way for career advancement.
  3. Secure your future: With the growing demand for data experts, you’ll be in a position of strength as automation progresses.
  4. Enjoy career growth: Encounter diverse challenges daily, expanding your knowledge and expertise continuously.

Requirements

  • No prior experience required—we start from the basics and build up your knowledge progressively.
  • A strong drive to learn and engage with practice exercises.

Who Should Enroll?

  • Individuals aspiring for a career in Data Science or Business Intelligence
  • Business analysts and executives
  • Enthusiasts passionate about numbers and quantitative analysis
  • Anyone eager to understand the nuances of Statistics in business applications
  • Beginners wanting to grasp the essentials of statistics

Learning Outcomes

  • Comprehend the fundamentals of statistics
  • Assort and visualize different types of data
  • Calculate measures of central tendency, asymmetry, and variability
  • Analyze correlation and covariance
  • Differentiate and handle various distribution types
  • Estimate confidence intervals and conduct hypothesis tests
  • Make informed, data-driven decisions
  • Understand and perform regression analysis
  • Utilize dummy variables effectively
  • Grasp concepts necessary for data science applications, including Python and R

About the Author: udemy

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By connecting students all over the world to the best instructors, Udemy is helping individuals reach their goals and pursue their dreams. Udemy is the leading global marketplace for teaching and learning, connecting millions of students to the skills they need to succeed. Udemy helps organizations of all kinds prepare for the ever-evolving future of work. Our curated collection of top-rated business and technical courses gives companies, governments, and nonprofits the power to develop in-house expertise and satisfy employees’ hunger for learning and development.

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#1: What does the course cover?
All Course Lessons (64)
#Lesson TitleDurationAccess
1
What does the course cover? Demo
03:55
2
Understanding the difference between a population and a sample
04:03
3
The various types of data we can work with
04:34
4
Levels of measurement
03:44
5
Categorical variables. Visualization techniques for categorical variables
04:53
6
Numerical variables. Using a frequency distribution table
03:10
7
Histogram charts
02:15
8
Cross tables and scatter plots
04:45
9
The main measures of central tendency: mean, median and mode
04:21
10
Measuring skewness
02:38
11
Measuring how data is spread out: calculating variance
05:56
12
Standard deviation and coefficient of variation
04:41
13
Calculating and understanding covariance
03:24
14
The correlation coefficient
03:18
15
Practical example
16:16
16
Introduction to inferential statistics
01:01
17
What is a distribution?
04:34
18
The Normal distribution
03:55
19
The standard normal distribution
03:31
20
Understanding the central limit theorem
04:21
21
Standard error
01:28
22
Working with estimators and estimates
03:08
23
Confidence intervals - an invaluable tool for decision making
02:42
24
Calculating confidence intervals within a population with a known variance
08:02
25
Confidence interval clarifications
04:39
26
Student's T distribution
03:23
27
Calculating confidence intervals within a population with an unknown variance
04:37
28
What is a margin of error and why is it important in Statistics?
04:53
29
Calculating confidence intervals for two means with dependent samples
06:05
30
Calculating confidence intervals for two means with independent samples (part 1)
04:32
31
Calculating confidence intervals for two means with independent samples (part 2)
03:58
32
Calculating confidence intervals for two means with independent samples (part 3)
01:28
33
Practical example: inferential statistics
10:07
34
The null and the alternative hypothesis
05:53
35
Establishing a rejection region and a significance level
07:06
36
Type I error vs Type II error
04:15
37
Test for the mean. Population variance known
06:35
38
What is the p-value and why is it one of the most useful tools for statisticians
04:14
39
Test for the mean. Population variance unknown
04:49
40
Test for the mean. Dependent samples
05:19
41
Test for the mean. Independent samples (Part 1)
04:23
42
Test for the mean. Independent samples (Part 2)
04:27
43
Practical example: hypothesis testing
07:17
44
Introduction to regression analysis
01:03
45
Correlation and causation
04:13
46
The linear regression model made easy
05:51
47
What is the difference between correlation and regression?
01:44
48
A geometrical representation of the linear regression model
01:26
49
A practical example - Reinforced learning
05:46
50
Decomposing the linear regression model - understanding its nuts and bolts
03:38
51
What is R-squared and how does it help us?
05:25
52
The ordinary least squares setting and its practical applications
02:24
53
Studying regression tables
04:55
54
The multiple linear regression model
02:56
55
The adjusted R-squared
05:25
56
What does the F-statistic show us and why do we need to understand it?
02:02
57
OLS assumptions
02:22
58
A1. Linearity
01:51
59
A2. No endogeneity
04:10
60
A3. Normality and homoscedasticity
05:48
61
A4. No autocorrelation
03:15
62
A5. No multicollinearity
03:27
63
Dummy variables
05:04
64
Practical example: regression analysis
14:10
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