Statistics for Data Analysis Using Python
What you'll learn
- Python from basics - No prior knowledge required
- Statistics from basics - No prior knowledge required
- You will first learn the basic statistical concepts, followed by application of these concepts using Python. This course is a nice combination of theory and practice.
- Inferential Statistics - One and two sample z, t, Chi Square, F Tests, ANOVA and more.
- Descriptive Statistics - Mean, Mode, Median, Standard Deviation, Variance and Interquartile Range
- Probability Distributions - Normal, Binomial and Poisson
- Basic school level mathematics will be helpful.
- You will need to download and install Python on your PC or laptop.
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.
ASQ ConnEx Expert, PMI-PMP, IRCA Registered Lead Auditor, ASQ - CSSBB, CQA, CQE, CMQ/OE, IIA - CIA
Sandeep Kumar has more than 35 years of Quality Management experience. He has worked as Quality Director on a number of projects, including Power, Oil and Gas and Infrastructure projects.
In addition, he provides coaching and consulting services to implement Lean Six Sigma to improve performance.
After successful completion of ASQ vetting, Sandeep Kumar has been designated as a genuine and authorized ASQConnEx expert. ASQConnEx is an education delivery system and network that vets, designates, and connects quality subject matter experts with organizations to advance their excellence journey.
His areas of specialization include Quality Assurance, ISO 9001:2015, Lean, Six Sigma, Risk Management, QMS Audits, Supplier Quality Surveillance, Supplier Pre-qualification, Construction Quality, Mechanical Inspection and Quality Training.
His professional qualifications/certifications include:
• Authorized ASQ ConnEx Expert
• ASQ-CSSBB, Certified Six Sigma Black Belt
• ASQ-CMQ/OE Certified Manager of Quality/Organizational Excellence
• PMI-PMP Certified Project Management Professional
• IRCA Registered Lead Auditor (QMS-2015)
• IIA-CIA Certified Internal Auditor
• ASQ-CSSGB, Certified Six Sigma Green Belt
• ASQ-CQA Certified Quality Auditor
• ASQ-CQE Certified Quality Engineer
• ASQ-CSQP Certified Supplier Quality Professional
I've been writing software for a living for around 10 years. I have a wide variety of experiences in the software industry. Most recently, I was a Senior Machine Learning engineer for an AI company. In the past, I've been a Backend Engineer at Atlassian, working on Bitbucket Cloud.
I care deeply about learning the tools of my trade in detail and for finding interesting ways of teaching them to people.