Statistics for Data Science using Python - by IBM

I am sharing sharing the solved Exercises & Project of Statistics for Data Science using Python course by IBM on Coursera which I have solved into my journey of Data Science.

Prerequisite: Data Analyst Roadmap ⌛, Python Lessons 📑 & Python Libraries for Data Science 🗂️

About this Course

This Statistics for Data Science course is designed to introduce you to the basic principles of statistical methods and procedures used for data analysis. After completing this course you will have practical knowledge of crucial topics in statistics including - data gathering, summarizing data using descriptive statistics, displaying and visualizing data, examining relationships between variables, probability distributions, expected values, hypothesis testing, introduction to ANOVA (analysis of variance), regression and correlation analysis. You will take a hands-on approach to statistical analysis using Python and Jupyter Notebooks – the tools of choice for Data Scientists and Data Analysts.

At the end of the course, you will complete a project to apply various concepts in the course to a Data Science problem involving a real-life inspired scenario and demonstrate an understanding of the foundational statistical thinking and reasoning. The focus is on developing a clear understanding of the different approaches for different data types, developing an intuitive understanding, making appropriate assessments of the proposed methods, using Python to analyze our data, and interpreting the output accurately.

This course is suitable for a variety of professionals and students intending to start their journey in data and statistics-driven roles such as Data Scientists, Data Analysts, Business Analysts, Statisticians, and Researchers. It does not require any computer science or statistics background. We strongly recommend taking the Python for Data Science course before starting this course to get familiar with the Python programming language, Jupyter notebooks, and libraries. An optional refresher on Python is also provided.

After completing this course, you will be able to:

✔ Calculate and apply measures of central tendency and measures of dispersion to grouped and ungrouped data.

✔ Summarize, present, and visualize data in a way that is clear, concise, and provides a practical insight for non-statisticians needing the results.

✔ Identify appropriate hypothesis tests to use for common data sets.

✔ Conduct hypothesis tests, correlation tests, and regression analysis.

✔ Demonstrate proficiency in statistical analysis using Python and Jupyter Notebooks.

Technologies used ⚙️

Python Libraries : Pandas | NumPy | Matplotlib | Seaborn

Certifications 📜 🎓 ✔️

Data Analysis with Python - by IBM
Data Visualization with Python - by IBM
Pandas - by Kaggle
Numpy & Matplotlib - by Great Learning
Databases and SQL for Data Science with Python - by IBM
Statistics for Data Science with Python - by IBM

Exercises - Statistics for Data Science using Python - by IBM

Sr.No. 🔢	Exercises 👨‍💻	Links 🔗
1	Introduction to probability distribution	Exercise 1
2	Visualizing Data	Exercise 2
3	Descriptive Stats	Exercise 3
4	Regression Analysis	Exercise 4
5	Hypothesis Testing	Exercise 5
6	Statistics for Data Science with Python	Exercise 6

Project - Boston Housing Data Analysis using Python 👨‍💻

My IBM Cloud Project Link 🔗

About Project - Boston Housing Data Analysis using Python

Each record in the database describes a Boston suburb or town. The data was drawn from the Boston Standard Metropolitan Statistical Area (SMSA) in 1970. The attributes are deﬁned as follows (taken from the UCI Machine Learning Repository1): CRIM: per capita crime rate by town

ZN: proportion of residential land zoned for lots over 25,000 sq.ft.

INDUS: proportion of non-retail business acres per town

CHAS: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)

NOX: nitric oxides concentration (parts per 10 million)

1https://archive.ics.uci.edu/ml/datasets/Housing

123

20.2. Load the Dataset 124

RM: average number of rooms per dwelling

AGE: proportion of owner-occupied units built prior to 1940

DIS: weighted distances to ﬁve Boston employment centers

RAD: index of accessibility to radial highways

TAX: full-value property-tax rate per $10,000