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
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.
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. There are certification bundles available with this training. This course forms part of Learning Tree's Certified Decision Scientist program.
There are no formal prerequisites for attending this course.
Anyone who is required to draw conclusions from numbers.
No prior knowledge of statistics or software packages is required. An inquisitive nature and an interest in using numbers to solve problems are essential.
Fundamentals of Statistics for Data Science Training Delivery Methods
- Learning Tree end-of-course exam included
- After-course computing sandbox included
- After-course instructor coaching included
Fundamentals of Statistics for Data Science Training Course Benefits
- 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 it's 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
Fundamentals of Statistics for Data Science Course Outline