Statistics Bootcamp (with Python): Zero to Mastery

Why should you study Statistics using Python?

Believe it or not, statistics is all around you!

It encompasses probabilities and relationships, serving as both an art and a science focused on gathering, analyzing, interpreting, and presenting data relating to these probabilities and relationships.

The goal is to extract valuable insights and help guide decisions based on data while navigating through uncertainty and variability.

Proficiency in statistics is crucial for many careers.

Organizations, whether businesses or governments, rely on statistics to inform their choices, and using statistical tools allows predicting future trends, especially in sectors like finance, economics, and meteorology.

Statistics is vital for careers in technology such as:

• Data Analyst
• Data Scientist
• A.I. Machine Learning Engineer
• Economists and Financial Analysts!

The demand for individuals who excel at statistics is so high that positions exist solely for Statisticians tasked with creating surveys, interpreting data, and offering insights.

Unlike many online courses, you won't be alone in your learning journey.

Upon enrollment, you’ll join our vibrant live online community classroom, engaging with thousands of peers, mentors, TAs, and instructors.

You will be guided by an industry expert (Diogo) with substantial real-world experience in applying statistics in various roles. He will teach you the precise concepts, tools, and strategies he employs in his profession.

This course will continuously evolve with the field.

As advancements occur in tools and technologies, we’ll incorporate new lectures and resources, ensuring you remain at the forefront of your profession.

This course will be your ultimate resource for the latest statistics practices now and into the future.

What you’ll gain from this Statistics course:

The curriculum is very practical, explained step-by-step in an easy-to-understand way, so you’ll feel at ease even if you’re new to statistics or Python.

We’ll start at the very beginning, introducing fundamental concepts of statistics and Python.

But we’ll go far beyond that.

You’ll explore using tools like ChatGPT for data analysis, advanced statistical theories, creating projects based on probability, and tackling hands-on challenges to deepen your understanding of statistics and Python.

Here are some essential topics and skills you’ll acquire:

1. Introduction:

We start with a course overview, setting up a Google Account, downloading course materials, and understanding the importance of statistics through historical events like the Challenger Shuttle Disaster.

2. Python for Statistics - Basics:

Dive into Python fundamentals crucial for statistical analysis, covering concepts such as print and input functions, variable types, arithmetic operations, and control structures.

Engage in practical challenges to solidify these concepts.

3. Python for Statistics - Intermediate:

Expand your knowledge with intermediate Python topics, including loops, lists, dictionaries, and randomization.

Through practical exercises and challenges, apply these skills, culminating in creating efficient functions.

4. ChatGPT for Data Analysis:

Learn how tools like ChatGPT and other machine learning models have transformed the data field. While they won’t replace your role, knowing how to harness them is essential for success.

This course equips you with ChatGPT skills for effective statistics work, data manipulation, and comprehensive analysis.

5. Descriptive Statistics:

Dive into descriptive statistical techniques, studying different variable types, populations versus samples, and core measures of central tendency like mean, median, and mode, using Python for computation and visualization.

6. Confidence Intervals:

Explore constructing and interpreting confidence intervals that suggest plausible values for unknown parameters, involving concepts like standard errors and Z-scores, applied through Python.

7. Hypothesis Testing:

Delve into hypothesis testing, a cornerstone of inferential statistics. Gain insight into p-values, errors, various testing methods, and apply these principles with Python.

Real-world case studies, such as Tesla Production, make the learning practical and relatable.

8. Multilinear Regression:

Broaden your understanding beyond simple linear regression to multilinear regression with multiple predictors, exploring multicollinearity, model optimization, and the relevance of predictors through practical Python exercises.

9. Logistic Regression:

Shift focus to binary outcomes as you learn about logistic regression, crucial for predicting categorical responses. Understand underlying theories like the logit function and odds ratios, applying these concepts in Python to classify outcomes and assess model accuracy.

10. Cox Proportional Hazard Regression:

Address survival analysis with the Cox Proportional Hazard Regression model. Emphasizing the proportional hazards assumption, familiarize yourself with crucial concepts such as hazard ratios and censoring, using Python effectively for time-to-event datasets.

What’s the takeaway?

This course is not about passively watching without understanding principles, ending up unsure of what to do next after completing the course. No way!

This course will motivate and challenge you, taking you from a novice to a strong understanding of statistics and practical application in real-world careers 💪.

How can we be sure?

Thousands of Zero To Mastery graduates have gained employment with companies like Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, Shopify, and leading tech firms.

They come from diverse backgrounds and experiences. Many started as novices.

You can be one of them too.

There's no risk for you. Start learning today, and if the course doesn’t meet your expectations, we’ll offer a full refund within 30 days. No fuss, no questions asked.