Introduction to Data Science

Course details

The world of data science is reshaping every business, regardless of industry, location, or role. And there’s never been a better time to get up to speed and learn the basics of this booming field. In this course, designed specifically for beginners, explore the world of data science, its opportunities and innovations, and the fundamental skills required for success.

Discover the essentials of data science and how it differs from other common data-related careers. Learn about some of the most important tools used in the trade to develop your understanding of data libraries and data manipulation. Along the way, get an introduction to exploratory data analysis, data cleaning, data visualization, sampling, testing, estimating, and more. By the end of this course, you’ll know how to use inference and statistical analysis to make more reliable predictions for your business.

Concepts

Introduction

Beginning your data science exploration

Defining Data Science

Demystifying data science
Defining the data science lifecycle

Starting with Data Design

Reducing bias with probability sampling
Using non-probability sampling

Utilizing Computational Tools

Comparing Python and R
Setting up your Jupyter environment

Structuring Your Tabular Data

Defining tabular data
Reading tabular data
Interpreting tabular data
Gathering insights
Answering specific questions

Using Exploratory Data Analysis

Defining exploratory data analysis
Recognizing statistical data types
Distinguishing properties of data

Cleaning Your Data

Explaining data cleaning
Questions to guide data cleaning

Using Data Visualization

Demystifying data visualization
Visualizing your qualitative data
Visualizing your quantitative data

Using Inference and Statistical Analysis

Defining inference
Designing a hypothesis test
Creating a permutation
Conducting a permutation test
Bootstrapping a confidence interval

Using Prediction in Data Science

Defining prediction for data science
Navigating classification
Recognizing the k-NN algorithm
Implementing the k-NN algorithm
Navigating regression
Checking assumptions of regression
Implementing linear regression

Conclusion

Next steps