Introduction to Data Science

Introduction to Data Science
Introduction to Data Science

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 48m | 891 MB
eLearning | Skill level: Intermediate


The world of data science is reshaping every business. There is no better time to learn it than now. In this Madecraft course, Python trainer and data scientist Lavanya Vijayan shares what data science is and how it differs from other information-focused disciplines. She then dives into the workflow—the life cycle of data science—and introduces the data scientist’s toolset, from programming languages and specialized libraries to productivity tools like Jupyter Notebooks. In the following chapters, Lavanya focuses on practical techniques such as exploratory data analysis, data cleaning, and data visualization. Finally, learn about sampling, testing, and classification. By the end of the course, you will have the knowledge you need to perform basic data analysis and reporting, and unlock opportunities to accelerate your career in this exciting field.

Topics include:

  • What is data science?
  • Data science life cycle
  • Python vs. R for data science
  • Reading tabular data
  • Exploratory data analysis
  • Cleaning data
  • Visualizing data
  • Inference
  • Classification for machine learning
+ Table of Contents

Introduction
1 Beginning your data science exploration

Defining Data Science
2 What is data science
3 Why data science

Data Science Life Cycle
4 What is the data science life cycle

Data Design
5 Probability sampling

Computational Tools
6 Python vs. R
7 Set up the environment Jupyter

Tabular Data
8 What is tabular data
9 Reading tabular data
10 Gathering insights
11 Answering specific questions

Exploratory Data Analysis
12 What is exploratory data analysis
13 Statistical data types
14 Properties of data

Data Cleaning
15 What is data cleaning
16 Questions to ask before cleaning

Data Visualization
17 What is data visualization
18 Visualize quantitative data
19 Visualize qualitative data

Inference
20 What is inference
21 Design a hypothesis test
22 Conduct a permutation test
23 Bootstrap a confidence interval

Classification
24 What is classification
25 Intro to k-Nearest Neighbor algorithm

Conclusion
26 Next steps