Home
Courses
Fundamentals of Statistics for Data Science Training

Fundamentals of Statistics for Data Science Training

Course 1265

Duration: 2 days
Labs: Yes
Language: English
Level: Foundation

Statistics is the science in data science. Without it, your "data-driven" decision-making may be driving you off a cliff edge. A solid grasp of statistical reasoning ensures that you tease only valid insights from your data.

Many traps await the unwary data explorer. In this course, you will learn how to avoid embarrassing mistakes that could undermine months of work.

In this statistics training for data science, you will gain hands-on experience with the tools required to make sense of your data and draw the correct conclusions. The focus is on statistical thinking. Concepts will be introduced intuitively before being expanded on formally. You will learn how to think in terms of distributions — not single-point estimates. Statistical methods will be introduced in the context of how to use them to gain insight and solve problems. As a bonus, you will also get the chance to use the powerful, industry-standard R environment to do the number-crunching.

Introduction to Statistics is an essential course for anyone technical, managerial or administrative — interested in using data to inform their decision-making.

If you think that statistics has nothing to say about what you do, or how you could do it better, then you are either wrong or in need of a more interesting job.

— Stephen Senn

Fundamentals of Statistics for Data Science Training Delivery Methods

In-Person
Online

Statistics for Data Science Training Course Information

In this course, you will learn how to:

Visualize data.
Draw conclusions about the features and quality of data sets.
Summarize your data.
Determine correlation.
Think of numbers as distributions.
Understand sampling and its importance in statistic inference.
Use the power of computers to generate distributions for any problem.
Calculate confidence intervals and p-values.
Make valid statistic inferences using a range of hypothesis tests.
Critique statistical analyses.
Design and execute your own statistical projects.

Prerequisites

There are no formal prerequisites for attending this course. No prior knowledge of statistics or software packages is required. An inquisitive nature and an interest in using numbers to solve problems are essential.

Statistics for Data Science Course Outline

Introduction and Overview

Course philosophy
Software
Contents

What is Statistics?

Definition
Types of statistician
Variability
Probability
Let the die roll!
Die roll outcomes
Why is knowledge of statistics important?
Descriptive vs inferential statistics
Inferring population parameters
Quantitative data
Qualitative data
R statistical software
RStudio
Interactive exercise manual demo

Exploratory Data Analysis

What is exploratory data analysis (EDA)
Histograms and bar charts
Bar chart vs histogram
Central tendency and spread
Bin width is crucial
Right-skewed data
Outliers
Left-skewed data
Bimodal data
Separate subpopulations for analysis
Individual value plot
Subpopulation individual value plots
Benefits of boxplots
Boxplot
Boxplot vs histogram
Left-skewed boxplot
Compare subpopulations using boxplots
Swedish salaries by level of education
Measures of central tendency
Mean vs median
Mean vs median for skewed data
Mode
Measures of spread
Range and IQR
Standard deviation
Six figure summary
Central tendency and spread equations
Quantiles
Benefits of scatterplots
Scatterplot
Highlighting subgroups on scatterplot
What is correlation?
Correlation examples
Random data correlation
Literacy rate correlation
# children per woman correlation
Interpreting correlation coefficients
Correlation doesn’t imply causation
Causation doesn’t imply (linear) correlation

Probability Distributions

Numbers are mostly reckless estimates
Random variables
Male life expectancy in UK distribution
What’s the probability that a US man is 6’ or more?
What is a probability distribution?
Populations vs samples
Sampling the heights of 10 random American men
Sampling the heights of 100,000 random American men
Discrete probability distributions
Roll two dice and histogram the results
Poisson distribution
Binary probability distributions
Probability distribution for cars/household in the UK
Binomial distribution
Geometric distribution
Negative Binomial distribution
Continuous probability distributions
Uniform distribution
Triangular distribution
Normal distribution
Properties of the normal distribution
Distribution of IQ scores
Different means (same standard deviation)
Different standard deviations (same mean)
z-distribution
68–95–99.7 (empirical) rule
Quantile-Quantile (Q-Q) plot
Q-Q plot of non-normal data
Common probability distributions “family tree”

Sampling

Samples are proxies for the population of interest
Unfortunately, samples vary
Larger samples exhibition less variation
Statistics vs parameters
Distributions involved in statistical inference
Sampling distribution of mean IQ
Collecting more IQ samples
Sampling distribution of mean die roll
Sampling distribution of mean project duration
Create a sampling distribution
Central limit theorem
Implications of the central limit theorem
Standard error of the mean (SEM)
Impact of sample size on SEM
What is a confidence interval?
95% confidence interval
Bigger samples give greater precision
Smaller confidence levels result in tighter intervals
How should we interpret the confidence interval?
Random sampling
Simple random sampling
Stratified sampling
Cluster sampling
What is bootstrapping?
Estimating median life expectancy

Statistical Inference

What is statistical inference?
Why must we use samples?
Why do we need to conduct hypothesis tests?
What is hypothesis testing?
Null hypothesis
Alternative hypothesis
Rejecting the null hypothesis
One- vs Two-tailed hypothesis tests
Choosing between one- and two-tailed tests
What are p-values?
Significance level (?)
Types of errors
Confidence levels vs significance levels
Performing hypothesis tests
p-value controversy
When to use a t-test
t-value
t-distribution
t-distributions
Slot machine observed ”Return to Player”
Are slot machine payouts within tolerance?
Preform a t-test on RTP data using R
Two-sample t-test
When to use a z-test
Conducting hypothesis tests using z-scores
When to use a 2 test
Education and Brexit vote
Brexit vote breakdown
2 value
2 distributions
Are education and Brexit vote related?
When to use a F-test
Conducting hypothesis tests using F-values
F-distributions
Height distribution by sex
Does height variation differ by sex?
When to use analysis of variance (ANOVA)
Determining the F-value
Are all diets the same?
All diets are apparently not the same
Normality hypothesis tests
Statistically significant treatments?
What is statistical power?
Calculating statistical power
Statistical power curve
Improving statistical power of hypothesis tests

Get This Course $2,050 GOV $1,610

2-day instructor led training course
After-course computing sandbox included
Learning Tree end-of-course exam included
After-course instructor coaching included

Get the Advantage $3,895

Take this course and gain unlimited access to more than 300 virtual instructor-led courses
Future-proof your career with more than 100 sought-after certifications in the market
Build real skills through hands-on learning in more than 180 virtual labs
Grow your skills and capabilities with more than one course at a time and save

Thank you for choosing subscriptions!

#1265

Feb 24 - 25 9:00 AM - 4:30 PM EST

Herndon, VA or Virtual
Apr 22 - 23 9:00 AM - 4:30 PM EDT

Herndon, VA or Virtual
Jun 17 - 18 9:00 AM - 4:30 PM EDT

Herndon, VA or Virtual
Aug 18 - 19 9:00 AM - 4:30 PM EDT

Herndon, VA or Virtual

2-day instructor led training course
After-course computing sandbox included
Learning Tree end-of-course exam included
After-course instructor coaching included

Get the Advantage $3,895

Take this course and gain unlimited access to more than 300 virtual instructor-led courses
Future-proof your career with more than 100 sought-after certifications in the market
Build real skills through hands-on learning in more than 180 virtual labs
Grow your skills and capabilities with more than one course at a time and save

Thank you for choosing subscriptions!

#1265

Feb 24 - 25 9:00 AM - 4:30 PM EST

Herndon, VA or Virtual
Apr 22 - 23 9:00 AM - 4:30 PM EDT

Herndon, VA or Virtual
Jun 17 - 18 9:00 AM - 4:30 PM EDT

Herndon, VA or Virtual
Aug 18 - 19 9:00 AM - 4:30 PM EDT

Herndon, VA or Virtual

Bring this or any training to your organization
Full-scale program development
Delivered when, where, and how you want it
Blended learning models
Tailored content
Expert team coaching

Get the Advantage $3,895

Take this course and gain unlimited access to more than 300 virtual instructor-led courses
Future-proof your career with more than 100 sought-after certifications in the market
Build real skills through hands-on learning in more than 180 virtual labs
Grow your skills and capabilities with more than one course at a time and save

Thank you for choosing subscriptions!

#1265

Questions about this course?

Customize Your Team Training Experience

Fill out the form below or call 888-843-8733

* Required Fields

Preferred method of contact:

Phone

Need Help Finding The Right Training Solution?

Our training advisors are here for you.

Statistics for Data Science Course FAQs

Statistics is the science of analyzing data, particularly in large quantities, and using it to draw more general conclusions.

Without statistics, conclusions drawn from data can be fatally flawed. As data becomes ubiquitous, the ability to analyze it responsibly is essential.

Participants be able to understand basic mathematical concepts (high-school level) and have a level of comfort with using computer software, such as Excel. We recommend this course for anyone who is required to draw conclusions from numbers.

Yes. There are various opportunities to conduct analyses throughout the period of the training.

Yes! We know your busy work schedule may prevent you from getting to one of our classrooms which is why we offer convenient online training to meet your needs wherever you want. This course is available online, in person, or as Private Team Training.

Fundamentals of Statistics for Data Science Training

Fundamentals of Statistics for Data Science Training Delivery Methods

Statistics for Data Science Training Course Information

Statistics for Data Science Course Outline

Introduction and Overview

What is Statistics?

Exploratory Data Analysis

Probability Distributions

Sampling

Statistical Inference

Upgrade your course experience with: Subscriptions

Get the Advantage $3,895

Thank you for choosing subscriptions!

Upgrade your course experience with: Subscriptions

Get the Advantage $3,895

Thank you for choosing subscriptions!

Upgrade your course experience with: Subscriptions

Get the Advantage $3,895

Thank you for choosing subscriptions!

Statistics for Data Science Course FAQs

What is statistics?

Why is a knowledge of statistics valuable in business?

What background do I need for this Introduction to Statistics course?

Does this include any practical, hands-on learning?

Can I take this training course online?

Related Courses