Statistics for Data Science & Business Analytics in Python

Welcome to our comprehensive course on Statistics for Data Science & Business Analytics using Python. If you're looking to gain a deep understanding of Statistics for Data Science & Business Analytics and develop the skills necessary to excel in this field, you've come to the right place. With over 10 hours of engaging video content, 75+ informative lectures and 16 thought-provoking quizzes, this course is designed to take you on a transformative learning journey. Whether you're a novice looking to build a solid foundation or an experienced professional aiming to refine your expertise, this course promises to equip you with the knowledge and tools you need to succeed.

In today's fast-paced world, staying competitive and relevant in your chosen field is more crucial than ever. This course aims to empower you with a comprehensive understanding of Statistics for Data Science & Business Analytics, covering a wide range of topics and concepts to ensure you're well-prepared for any challenges that come your way. From the fundamentals to advanced techniques, we've carefully curated the content to provide you with a holistic learning experience.

About the Instructor:

This course will be taught by Farzan Sajahan, who has an executive MBA from Rotterdam School of management with over 18 years of experience in data analytics and management consulting. He has worked extensively in data analytics and operations management. He has been teaching data science for the last 4 years to over 60,000 students. He is running a management consulting firm based out of India.

What to Expect from This Course:

1. In-Depth Video Content: Our course boasts more than 10 hours of meticulously crafted video lessons. These videos are designed to make complex topics accessible and engaging. You'll have the opportunity to learn from expert in the field who will guide you through each concept, ensuring that you not only understand the theory but also its practical applications.

2. Interactive Quizzes: Learning is most effective when it's interactive. To reinforce your understanding, we've included 80 quiz questions throughout the course. These quizzes are strategically placed to test your knowledge and help you gauge your progress. Don't worry; they're not just for assessment purposes—they're also fun.

3. Comprehensive Lecture Series: The 75+ lectures included in this course provide a deep dive into the subject matter. You'll explore the intricacies of Statistics for Data Science & Business Analytics, gaining insights and practical tips that are valuable for both beginners and experienced professionals. Our lecturers are passionate about the topic, and their enthusiasm will inspire and motivate you.

4. Real-World Applications: We understand that theory alone is not enough. That's why we emphasize real-world applications throughout the course. You'll learn how to put your newfound knowledge into practice, enabling you to excel in your current job or prepare for future opportunities.

5. Access to Resources: As a student in this course, you'll have access to a wealth of resources, including python notebooks and datasets. These resources are designed to enhance your learning experience and provide you with valuable references for future use.

6. Lifetime Access: Once you enroll in this course, you'll have lifetime access to all the materials. You can revisit the content whenever you need a refresher or want to explore more advanced topics. Your learning journey doesn't have an expiration date.

This course on Statistics for Data Science & Business Analytics using Python is your gateway to becoming a proficient and confident Statistics practitioner. Whether you're seeking personal growth, career advancement, or simply looking to satisfy your curiosity, we're here to guide you every step of the way. So, let's embark on this exciting journey together, unlock your potential, and discover the limitless possibilities that await you in the world of Statistics for Data Science & Business Analytics. Enroll today and let's get started.

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Learning objectives

Foundational understanding of python to analyze data using numpy and pandas, and use statistical packages such as scipy and statsmodels.
Analyzing and visualizing data using python using line charts, bar charts, pie charts, histogram and box plots.
Conducting univariate and bivariate analysis using one-way tables, two-way tables.
Descriptive statistics for univariate and bivariate analysis - mean, median, mode, range, iqr, variance, standard deviation, covariance and correlation.
Data distributions, including mean, variance, and standard deviation, t-distribution and normal distributions and z-scores.

Probability, including union vs. intersection and independent and dependent events and bayes' theorem.
Sampling distribution, central limit theorem and intuition behind using central limit theorem in hypothesis testing.
Hypothesis testing, including inferential statistics, significance level, type i and ii errors, test statistics, and p-values. test of proportions and chi-squar
Simple linear regression using manual method as well as using ols package in python, multiple linear regression, and predicting using the regression model.

Foundational understanding of python to analyze data using numpy and pandas, and use statistical packages such as scipy and statsmodels.
Analyzing and visualizing data using python using line charts, bar charts, pie charts, histogram and box plots.
Conducting univariate and bivariate analysis using one-way tables, two-way tables.
Descriptive statistics for univariate and bivariate analysis - mean, median, mode, range, iqr, variance, standard deviation, covariance and correlation.
Data distributions, including mean, variance, and standard deviation, t-distribution and normal distributions and z-scores.
Probability, including union vs. intersection and independent and dependent events and bayes' theorem.
Sampling distribution, central limit theorem and intuition behind using central limit theorem in hypothesis testing.
Hypothesis testing, including inferential statistics, significance level, type i and ii errors, test statistics, and p-values. test of proportions and chi-squar
Simple linear regression using manual method as well as using ols package in python, multiple linear regression, and predicting using the regression model.

Syllabus

Getting started

Welcome to the course on Statistics for Data Science & Business Analytics in Python!

I am so excited to see you inside the course.

This is going to be a lot of fun as well as lots and lots of learning.

To make it easy for you I have zipped all the material - to be precise: 39 python notebooks and 15 datasets - available for you to download from the link below.

Unzip all the files, organize the code and datasets separately for easy access during each video.

Let's rock and roll!

Quick note!

Python basics

Installing anaconda distribution and Jupyter

Tour of Jupyter notebook

Calculations in Python

Variables in python

Collection data types in python - List

Collection data types in python continued - Tuples, Sets and Dictionaries

Welcome to the Python Basics quiz!

This short quiz is designed to test your knowledge gained from this section.

All the best!

Core programming in Python

Conditional and logical statements

Here is a quick quiz on conditional and logical statements.

There are 3 multiple choice questions to refresh your learning.

Enjoy!

For and While loops

Functions

Here is a quick quiz to refresh your learning.

Enjoy!

Arrays, Matrices and data frames

Numpy arrays

ndarrays in numpy

Access values from a matrix

Great progress so far. Here is a simple quiz to test your understanding on Numpy.

All the best!

Pandas Series

Pandas Data Frames

Data frame manipulation

Fantastic! Here is a simple quiz to test your understanding on pandas.

You may need to run code so keep Jupyter open.

All the best!

Introduction to Statistical Data Analysis

Variables in Statistical Data Analysis

Population Vs Samples

Great progress so far! Here is a simple quiz to test your understanding on introduction to statistical data analysis.

All the best!

Data visualization in python

One way tables

Line Charts and Bar charts

Pie Charts

Two way cross tables

Heat maps

Great job in completing data visualization section!

In this quiz you will be asked to analyze the datasets using python.

Keep the data set ready and Jupyter notebook open.

All the best!

Univariate data analysis

Central tendency measures: mean, median, mode

Dispersion measures: range and interquartile range

Histogram

Box plot

Outliers

Variance and Standard deviation

Univariate hands-on exercise

Congratulations on completing the univariate analysis section.

Here is your quiz for this section.

All the best!

Bivariate data analysis

Introduction to Bivariate analysis

Covariance and Correlation

Bivariate hands-on exercise

Fantastic! Congrats on completing Bivariate data analysis.

Here is a small quiz to assess your progress.

All the best!

Probability

Probability theory

Estimating simple probabilities - single independent event

Estimating probability in case of two or more events

Conditional Probability

Review the Multiplication law of probability

Bayes theorem

Great job so far. I am sure you are enjoying the course.

Here is a quick quiz to test your understanding on probability theory.

Good luck!

Random Distributions

Random Variables and Probability Distribution

Using Probability Distribution to Estimate Probabilities

Normal distribution

Normal Distribution Hands-on

T-distribution

Finding actual values from the probability

Congratulations on your progress!

Here is a small test on random distributions. All the best!

Sampling Distribution

Central limit theorem hands-on

Congratulations on your progress so far.

Here is a quick quiz to test your understanding on sampling distributions and central limit theorem.

All the best!

Hypothesis Testing - Test of Means

Introduction to Inferential statistics and hypothesis testing

Introduction to test of means

Steps for conducting test of means

You have made great progress -- fantastic job!

Here is a small quiz to test your understanding of hypothesis testing.

Good luck with your quiz!

One sample, right tail test

One sample, left tail test

One sample, two tail T test

Two sample, unpaired T test

Two sample, paired T test

Errors in hypothesis testing

I hope that you are enjoying the lectures on hypothesis testing. Here we have some more questions to test your knowledge on hypothesis testing.

All the best!

Analysis of Variance (ANOVA)

ANOVA Introduction

ANOVA Intuition

One Way ANOVA manual computation

One Way ANOVA using python

Congratulations on your progress so far.

Here is your quiz on one-way ANOVA.

All the best!

Two Way ANOVA - case 1 (Diet Plan)

Two Way ANOVA - case 2 (Movies analysis)

Congratulations on completing ANOVA!

Here is a small quiz to test your understanding of two-way ANOVA.

All the best!

Test of proportions

Introduction to test of proportions and independence using Chi-square test

Chi square test hands-on

Congratulations - You have made such a great progress.

Here is a small quiz to test your understanding on test of proportions.

All the best!

Simple Linear Regression

Introduction to Linear Regression

Goodness of fit

Condition for linear regression

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops a foundational understanding of Python, essential for data science and analytics roles

Emphasizes practical applications, equipping learners for real-world scenarios in data science and business analytics

Taught by an experienced professional with over 18 years in data analytics and management consulting

Provides lifetime access to materials, enabling ongoing learning and reference

Requires prior knowledge of Python, which may pose a barrier for complete beginners

Lacks explicit prerequisites, which may lead to learners entering with insufficient background

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Statistics for Data Science & Business Analytics in Python with these activities:

Python Crash Course

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Learn the basics of programming which will be utilized for analyzing data

Browse courses on Python

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Review the basics of Python syntax
Practice writing Python code

Review Calculus

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Refresh your calculus skills to enhance your understanding of statistical concepts

Browse courses on Calculus

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Review the basics of calculus, including derivatives and integrals

Data Manipulation Exercises

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Enhance your ability to work with data in Python

Browse courses on Data Manipulation

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Complete 10 practice exercises on data manipulation

Four other activities

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Show all seven activities

Data Science Workshop

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Gain hands-on experience with data science techniques and tools

Browse courses on Data Science

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Attend a data science workshop
Participate in hands-on exercises and discussions

Study Group for Hypothesis Testing

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Strengthen your understanding of hypothesis testing through peer discussion

Browse courses on Hypothesis Testing

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Meet with a study group to discuss hypothesis testing concepts
Work together to solve problems and answer questions

Data Visualization Dashboard

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Apply your data visualization skills to create a meaningful dashboard

Browse courses on Data Visualization

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Choose a dataset and identify key insights
Design and create a data visualization dashboard using appropriate tools

Contribute to Scikit-learn

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Deepen your understanding of machine learning by contributing to an open-source project

Browse courses on Machine Learning

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Identify a small issue or feature to work on
Create a pull request with your proposed changes

Career center

Learners who complete Statistics for Data Science & Business Analytics in Python will develop knowledge and skills that may be useful to these careers: