Introduction to Data Science

سرفصل های دوره

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.

Join Python trainer and data science expert Lavanya Vijayan as she shares what data science is and how it differs from other common data-related careers. Discover 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.

This course was created by Madecraft. We are pleased to host this training in our library.

01 - Introduction

01 - Beginning your data science exploration

02 - 1. Defining Data Science

01 - Demystifying data science

02 - The value of data science

03 - Defining the data science life cycle

03 - 2. Starting with Data Design

01 - Reducing bias with probability sampling

02 - Using non-probability sampling

04 - 3. Utilizing Computational Tools

01 - Comparing Python and R

02 - Setting up your Jupyter environment

05 - 4. Structuring Your Tabular Data

01 - Defining tabular data

02 - Reading tabular data

03 - Interpreting tabular data

04 - Gathering insights

05 - Answering specific questions

06 - 5. Using Exploratory Data Analysis

01 - Defining exploratory data analysis

02 - Recognizing statistical data types

03 - Distinguishing properties of data

07 - 6. Cleaning Your Data

01 - Explaining data cleaning

02 - Questions to guide data cleaning

08 - 7. Using Data Visualization

01 - Demystifying data visualization

02 - Visualizing your qualitative data

03 - Visualizing your quantitative data

09 - 8. Using Inference and Statistical Analysis

01 - Defining inference

02 - Designing a hypothesis test

03 - Creating a permutation

04 - Conducting a permutation test

05 - Bootstrapping a confidence interval

10 - 9. Using Prediction in Data Science

01 - Defining prediction for data science

02 - Navigating classification

03 - Recognizing the k-NN algorithm

04 - Implementing k-Nearest Neighbors

05 - Navigating regression

06 - Checking assumptions of regression

07 - Implementing linear regression

11 - Conclusion

01 - Next steps

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تولید کننده: LinkedIn Learning (Lynda)