**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

Resolve the captcha to access the links!