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Applied Statistics for Data Scientists


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Self-Paced Online:
$395.00
Private Onsite Package

This course can be tailored to your needs for private, onsite delivery at your location.

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Professional Credits

IIBA (CDU)

ASPE is an IIBA Endorsed Education Provider of business analysis training. Select Project Delivery courses offer IIBA continuing development units (CDU) in accordance with IIBA standards.

NASBA (CPE)

NASBA continuing professional education credits (CPE) assist Certified Public Accountants in reaching their continuing education requirements.

PMI (PDU)

Select courses offer Leadership (PDU-L), Strategic (PDU-S) and Technical PMI professional development units that vary according to certification. Technical PDUs are available in the following types: ACP, PBA, PfMP, PMP/PgMP, RMP, and SP.

Certification
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. 

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Course Outline

Lesson 1 – Statistics and Statistical Terminology

Objectives:

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

Lesson 2 – Sampling; Descriptive vs. Inferential Statistics

Objectives:

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

Lesson 3 – Sampling and Statistical Bias

Objectives:

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

Lesson 4 – Measures of Central Tendency & Arithmetic Mean

Objectives:

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

Lesson 5 – Geometric Mean and Harmonic Mean

Objectives:

  1. Geometric mean
  2. Harmonic mean
  3. Examples

Lesson 6 – Median

Objectives:

1)         What is median

2)         Examples

Lesson 7 – Mode and Midrange

Objectives:

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

Lesson 8 – Measures of Dispersion

Objectives:

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

Lesson  – Measures of Dispersion

Objectives:

  1. Variance
  2. Standard deviation
  3. Examples

Lesson 10 – Some Examples

Lesson 11 – IQR (Interquartile range) and Outliers

Objectives:

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

Lesson 12 – Distributions

Objectives:

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

Lesson 13 – Working with Distributions

Lesson 14 – Different Types of Distributions

Objectives:

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

Lesson 15 – Continuous and Parametric Distributions

Objectives:

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

Lesson 16 – Inferential Statistics

Objectives:

  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

Lesson 17 – Hypothesis Testing for the Population Mean

Objectives:

  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

Lesson 18 – Difference Between Two Means

Objectives:

  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.

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