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Statistics for Data Analysis Using Python

Name: Statistics for Data Analysis Using Python
Rating: 4.6 (1559 reviews)

Learn Python from Basics • Descriptive, Inferential Statistics • Plots for Data Visualization • Data Science

Created bySandeep Kumar Quality Gurus Inc., Abhin Chhabra

Last updated 9/2021

English

What you'll learn

Learn Python from the basics with no prior knowledge required, making this course accessible to everyone.
Understand statistics from the ground up, with no prior knowledge needed, ensuring a solid foundation in both Python and statistics.
Start with basic statistical concepts and progressively apply these concepts using Python for a comprehensive learning experience.
Enjoy a balanced combination of theory and practice, enhancing your understanding and application of statistical methods.
Master descriptive statistics, including mean, mode, median, standard deviation, variance, and interquartile range, using Python.
Dive into inferential statistics with one and two-sample z-tests, t-tests, Chi-Square tests, F-tests, ANOVA, and more, gaining practical skills.
Explore various probability distributions, such as normal, binomial, and Poisson, and learn to implement these in Python.
Understand how to visualize data effectively using Python libraries, creating insightful graphs and charts.
Enhance your resume with valuable skills in Python and statistics, making you a competitive candidate in data-driven fields.
Gain confidence in your ability to perform statistical analyses and interpret results using Python, boosting your career prospects.

Coding Exercises

This course includes our updated coding exercises so you can practice your skills as you learn.

Course content

9 sections • 137 lectures • 15h 58m total length

Installing Anaconda6:48
Getting started with Jupyter Notebook8:12
Launch Jupyter notebook via Anaconda Navigator, create and save notebooks in a Python folder, and master code and markdown cells plus shortcuts like shift-enter, a/b, and y/m.
Download Section 1 Resources and the Course Slides0:06
Getting started with Python13:14
Variables and Data Types6:10
An Introduction to Coding Excercises and Course Resources6:16
Introduction to coding exercises
Solution: Introduction to coding exercises0:01
Working with a List - Part 115:17
Select an element from the list
Solution: Select an element from the list
Working with a List - Part 26:00
Master Python lists by reading, adding, and removing items, counting length, and testing membership with in. Build lists from ranges using start, stop, and step to generate larger sequences.
A review of lists
Solution: A review of lists0:01
Working with a Dictionary6:54
Working with a Tuple3:27
Working with a Set3:12
Logical Operators3:52
Explore how Python uses logical operators to compare values and produce booleans, distinguishing assignment from equality and covering not equal, and order comparisons, including case sensitivity in strings.

Download Section 2 Resources0:02
[Theory] Measurement of Central Tendency7:50
[Theory] Measurement of Dispersion - Part 16:28
[Theory] Measurement of Dispersion - Part 25:52
Explore descriptive statistics by examining dispersion through quartiles, median, and interquartile range, and learn to visualize with box and whisker plots in Python.
Quiz: Descriptive Statistics
Descriptive Statistics Using Python13:14
Find the mean, mode, median and standard deviation
Solution: Find the mean, mode, median and standard deviation0:02
Find the Inter Quartile Range
Solution: Find the Inter Quartile Range0:02

Download Section 3 Resources0:02
NumPy - Part 114:16
Two Dimensional Array
Solution: Two Dimensional Array
Creating a Numpy array
Solution: Creating a Numpy array0:02
NumPy - Part 211:55
Filtering from an Array
Solution: Filtering from an Array
Select a subsection of an Array
Solution: Select a subsection of an Array
NumPy - Part 310:02
Divide the Array elements by 10
Solution: Divide the array elements by 10
[Theory] Basics of Probability - Part 17:35
[Theory] Basics of Probability - Part 211:26
Explore mutually exclusive, independent, and complementary events, and apply the rule of multiplication and addition to calculate probabilities using coin flips, jar draws, and dice.
[Theory] Basics of Probability - Part 38:24
Quiz: Basics of Probability
Generating Random Numbers to Simulate the Probability13:49
Use NumPy's random to simulate probability concepts from coin flips to die rolls, with seeds for reproducibility, and count outcomes. Visualize results with Matplotlib and Seaborn count plots.
A Sample Probability Question8:31
Generate Five Random Numbers
Solution: Generate Five Random Numbers0:01
[Theory] Probability Distributions - Introduction6:57
[Theory] Binomial Distribution13:24
Explore the binomial distribution for n independent trials with two outcomes and constant p. Use P(X=x)=C(n,x)p^x(1-p)^{n-x} to compute probabilities and the mean np and variance np(1-p).
Quiz: Binomial Distribution
Binomial Distribution Using NumPy12:16
Demonstrate binomial distribution with numpy by simulating coin flips (1, 2, and 10 coins), visualize results with seaborn, and compare simulated versus theoretical outcomes for 20-sample defects.
Introducing SciPy Package for Binomial Distributions16:44
Flipping a Coin
Solution: Flipping a Coin0:01
Let's Flip Again
Solution: Let's Flip Again0:03
Number of defectives in a selection
Solution: Number of defectives in a selection0:01
[Theory] Poisson Distribution5:59
Explore the Poisson distribution, where possibilities are infinite, as a counterpart to binomial for discrete data, emphasizing mu as the average number of successes and the probability of X successes.
[Theory] Poisson Distributions - An Example5:13
Poisson Distribution Using NumPy7:29
Poisson Distribution Using SciPy11:16
Receiving Phone Calls
Solution: Receiving Phone Calls
Probability of more than 6 calls
Solution: Probability of more than 6 calls
[Theory] Normal Distribution - Part 110:27
[Theory] Normal Distribution - Part 29:44
Learn to convert any normal distribution to the standard normal with z-scores, then read the z-table for probabilities and the 68/95/99.7 percent areas.
Quiz: Normal Distrubution
Normal Distribution Using NumPy10:51
Normal Distribution Using SciPy11:51
Area of curve between two values of z
Solution: Area of curve between two values of z0:09
Descriptive Statistics Using NumPy9:53
Examine descriptive statistics in Python, covering central tendency and dispersion with statistics, NumPy, and SciPy.stats. Learn axis-based means on 2d arrays and shape, with binomial, Poisson, and normal distributions.
Mean of Rows
Solution: Mean of Rows0:19

Download Section 4 Resources0:02
Pandas Series11:05
Pandas DataFrame7:42
Create a DataFrame
Solution: Create a DataFrame0:12
Reading a .csv File (Importing External Data)11:37
Importing a CSV file
Solution: Importing a CSV file
DataFrame - Dealing with Columns9:55
Learn to manage pandas DataFrame columns: select single or multiple columns, create a volume column from length, width, height, and drop columns with axis.
DataFrame - Dealing with Rows24:26
What is the Temperature on Monday?
Solution: What is the Temperature on Monday0:11

Download Section 5 Resources0:02
Histogram using matplotlib.pyplot9:57
Box Plot using matplotlib.pyplot10:34
Line and Scatter Plots using matplotlib.pyplot8:27
Bar Plot using matplotlib.pyplot8:09
Learn to create bar plots with matplotlib.pyplot in Python from discrete data, using simple and advanced examples to count categories and visualize with plt.bar and plt.show.
Saving the plot as .png or .jpg4:08
Seaborn - Displot group of plots13:19
Seaborn - Catplot group of plots10:18
Seaborn - Relplot group of plots8:59
Visualizing the Titanic dataset15:45
Explore the Titanic dataset with Seaborn in Python to load data, inspect structure, and analyze age and survival outcomes using histograms, KDE plots, and categorical visuals.

Download Section 6 Resources0:02
[Theory] Population vs Samples7:46
[Theory] Central Limit Theorem10:00
Quiz: Population vs Samples + Central Limit Theorem
Central Limit Theorem - Using Python16:19
[Theory] Hypothesis Testing- Basics3:23
[Theory] Statistical and Practical Significance6:31
[Theory] Null and Alternate Hypothesis3:22
Hypothesis Testing - A Simple Introduction [Theory]12:22
[Theory] Types of Errors - Type I and Type II Errors11:27
[Theory] Types of Errors - Summary11:51
Hypothesis Testing - An Overview [Theory]12:13
[Theory] Z Tables - Finding the Critical Values8:24
[Theory] The p-Value4:24
Quiz: Hypothesis Testing Basics
Hypothesis Testing Using Python10:40
10% Area on the Right Tail
Solution: 10% Area on the Right tail0:18

Download Section 7 Resources0:02
[Theory] Tests for means, variances and proportions - Introduction8:40
[Theory] One Sample Z Test - Conditions7:31
[Theory] One Sample Z Test7:21
Understand a one sample z test through a perfume bottle example: test if the mean volume differs from 150 cc with n=100 and sigma=2 at 95% confidence, concluding no change.
One Sample Z Test - Examples5:25
One Sample Z Test Using Python13:40
practice performing a one-sample z test on bottle volume data using Python, pandas to read CSV files, and weightstats from statsmodels to obtain z- and p-values.
Torque Tightening of Perfume Bottles
Solution: Torque Tightening of Perfume Bottles0:08
One Sample t Test9:30
One Sample t Test Using Python4:25
[Theory] One Proportion Test12:29
One Proportion Test Using Python10:24
Explore conducting a one proportion test in Python using the binomial distribution with scipy.stats binom_test. Analyze smoking data, compare observed smokers to 0.21 expectation, and interpret p-values without normal approximation.
[Theory] One Variance Test - Introduction4:10
[Theory] One Variance Test - Example 17:05
[Theory] One Variance Test - Example 25:39
Learn a two-tailed one-variance test using chi-square to assess if the population variance changes from an established value, with df=50, sample sd 2.35, 90% confidence, and interpreting critical values.
One Variance Test Using Python8:47
Quiz: Hypothesis Testing Part 1

Download Section 8 Resources0:02
[Theory] Two Sample z Test - Introduction5:58
[Theory] Two Sample z Test - Example5:37
Two Sample z Test Using Python10:37
Two Sample z Test Using Python - tips Dataset8:10
[Theory] Two Sample t Test - Conditions and Calculations9:31
[Theory] Two Sample t Test - Equal Variance8:36
[Theory] Two Sample t Test - Unequal Variance11:07
[Theory] Two Sample t Test - Visual Interpretation5:21
Two Sample t Test Using Python5:36
[Theory] Paired t Test9:53
Paired t Test Using Python4:18
[Theory] Two Proportions Test - Introduction6:21
[Theory] Two Proportions Test - Pooled vs Unpooled Method4:09
[Theory] Two Proportions Test - An Example of Pooled Method8:17
Two Proportions Test Using Python6:59
Perform a two proportions test in python, comparing 30/200 vs 10/100 defectives, using statsmodels proportions with score method to reflect the p-value result.
[Theory] Two Variances Test12:17
Two Variances Test Using Python10:28
ANOVA [Theory]6:20
[Theory] Why ANOVA?5:48
[Theory] ANOVA - Conceptual Understanding7:48
[Theory] Statistics behind ANOVA7:15
[Theory] Performing ANOVA by Manual Calculations12:50
Conducting ANOVA Using Python6:26
Conducting ANOVA Using Python - mpg Dataset11:48
Post Hoc Test (Tuckey's HSD)12:07
[Theory] Goodness of Fit11:11
Goodness of Fit Test Using Python8:53
[Theory] Contingency Tables9:32
Use contingency tables and chi-square to test relationships between two discrete variables, with null and alternative hypotheses, observed versus expected counts, and practical examples like gender and smoking.
Quiz: Hypothesis Testing Part 2
Contingency Tables Using Python13:26

Requirements

Basic school level mathematics will be helpful.
You will need to download and install Python on your PC or laptop.

Description

Perform simple or complex statistical calculations using Python! - You don't need to be a programmer for this :)

You are not expected to have any prior knowledge of Python. I will start with the basics. Coding exercises are provided to test your learnings.

The course not only explains, how to conduct statistical tests using Python but also explains in detail, how to perform these using a calculator (as if, it was the 1960s). This will help you in gaining the real intuition behind these tests.

Learn statistics, and apply these concepts in your workplace using Python.

The course will teach you the basic concepts related to Statistics and Data Analysis, and help you in applying these concepts. Various examples and data-sets are used to explain the application.

I will explain the basic theory first, and then I will show you how to use Python to perform these calculations.

The following areas of statistics are covered:

Descriptive Statistics - Mean, Mode, Median, Quartile, Range, Inter Quartile Range, Standard Deviation.

Data Visualization - Commonly used plots such as Histogram, Box and Whisker Plot and Scatter Plot, using the Matplotlib.pyplot and Seaborn libraries.

Probability - Basic Concepts, Permutations, Combinations

Population and Sampling - Basic concepts

Probability Distributions - Normal, Binomial and Poisson Distributions

Hypothesis Testing - One Sample and Two Samples - z Test, t-Test, F Test and Chi-Square Test

ANOVA - Perform Analysis of Variance (ANOVA) step by step doing the manual calculation and by using Python.

The Goodness of Fit and the Contingency Tables.

Who this course is for:

Anyone who want to use statistics to make fact based decisions.
Anyone who wants to learn Python for career in data science.
Anyone who thinks Statistics is confusing and wants to learn it in plain and simple language.

Statistics for Data Analysis Using Python

What you'll learn

Explore related topics

Coding Exercises

Course content

Introduction15 lectures • 1hr 20min

Descriptive Statistics7 lectures • 34min

NumPy Package, Probability Distributions and an Introduction to SciPy Package35 lectures • 3hr 39min

Pandas Package9 lectures • 1hr 5min

Data Visualization Using Matplotlib.pyplot and Seaborn Libraries10 lectures • 1hr 30min

Inferential Statistics and Hypothesis Testing - Introduction15 lectures • 1hr 59min

Hypothesis Testing Part 1 (One Sample Tests)15 lectures • 1hr 45min

Hypothesis Testing Part 2 (Two Sample Tests, ANOVA, GoF & Contingency Tables)30 lectures • 4hr 7min

Bonus Section1 lecture • 1min

Requirements

Description

Who this course is for: