# Applied Statistics for Data Scientists

Self-Paced:
\$395.00
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Individual
Onsite
Overview

The statistical analysis techniques taught here form the foundation of any analytics or data science practice.

This self-paced class is an excellent hands-on walkthrough of the most foundational techniques used to understand and ask questions of datasets. The statistical techniques taught in this workshop underpin virtually every common method for analyzing data. They also provide a practical, useful introduction to contemporary data science techniques.

This program comprises roughly five hours of expert-led content, with basic assignments and exercises you can participate in between chapters.

Upcoming Dates and Locations
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Course Outline

# Part 1: Statistics and Statistical Terminology

1. What are statistics?
2. What is a statistical population?
3. Examples

# Part 2: Sampling; Descriptive vs. Inferential Statistics

1. What is sampling?
2. Population vs. sample
3. When do we sample?
4. Examples

# Part 3 – Sampling and Statistical Bias

1. Random sampling
2. What is bias?
3. Why we should avoid bias in samples
4. Examples

# Part 4:  Measures of Central Tendency & Arithmetic Mean

1. What are the Measures of Central Tendency
2. Arithmetic mean
3. Examples

# Part 5: Geometric Mean and Harmonic Mean

1. Geometric mean
2. Harmonic mean
3. Examples

# Part 6: Median

1. What is median
2. Examples

# Part 7: Mode and Midrange

1. What is mode
2. What is midrange
3. Examples
4. Review and Exercises

# Part 8: Measures of Dispersion

1. What are the measures of dispersion
2. Range
3. Mean absolute difference
4. Examples

# Part 9: Measures of Dispersion

1. Variance
2. Standard deviation
3. Examples

# Part 11: IQR (Interquartile range) and Outliers

1. What is IQR
2. 1.5 * IQR rule
3. Examples

# Part 12: Distributions

1. What is distribution
2. Histograms
3. Probability mass function and cumulative probability
4. Examples

# Part 14: Different Types of Distributions

1. Discrete distributions vs. continuous distributions
2. Introduction to Monte-Carlo simulations
3. Examples

# Part 15: Continuous and Parametric Distributions

1. Continuous distributions
2. Parametric distributions
3. Normal distribution
4. Examples

# Part 16: Inferential Statistics

1. When we use inferential statistics
2. Sample mean and sample variance
3. Central Limit Theorem
4. Estimation of population mean and confidence interval
5. Examples

# Part 17: Hypothesis Testing for the Population Mean

1. t-distribution
2. Estimation of population mean confidence interval
3. What is hypothesis testing
4. How to run a two-tailed test for a population mean
5. Examples

# Part 18: Difference Between Two Means

1. Problem formulation
2. Hypothesis testing
3. Examples
Who should attend
• Data Analysts
• Any data practitioner needing a statistics refresher
• Business Analysts and Project Managers who query data
• Data and sytem architects
• Application developers who wish to integrate data processes
• Analysts who want to grow their basic data science skills
Pre-Requisites

You will need a computer to view and participate in the class.