Statistics for Data Science Business Analytics in Python

Statistics for Data Science & Business Analytics in Python, available at $49.99, has an average rating of 4.67, with 76 lectures, 19 quizzes, based on 9 reviews, and has 61 subscribers.

You will learn about 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. This course is ideal for individuals who are Anyone who wants to build a career in data science but lacks the foundational skills in statistics or Data analysts who are familiar with analyzing data but want to learn concepts in statistics to be able to do rigorous analysis or Masters and research students who would like to learn statistics or Data analysts who are familiar with data analysis in excel but want to learn statistics using python or Data visualization experts who would like to explore statistics using python It is particularly useful for Anyone who wants to build a career in data science but lacks the foundational skills in statistics or Data analysts who are familiar with analyzing data but want to learn concepts in statistics to be able to do rigorous analysis or Masters and research students who would like to learn statistics or Data analysts who are familiar with data analysis in excel but want to learn statistics using python or Data visualization experts who would like to explore statistics using python.

Enroll now: Statistics for Data Science & Business Analytics in Python

Summary

Title: Statistics for Data Science & Business Analytics in Python

Price: $49.99

Average Rating: 4.67

Number of Lectures: 76

Number of Quizzes: 19

Number of Published Lectures: 76

Number of Published Quizzes: 19

Number of Curriculum Items: 95

Number of Published Curriculum Objects: 95

Original Price: $129.99

Quality Status: approved

Status: Live

What You Will Learn

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.

Who Should Attend

Anyone who wants to build a career in data science but lacks the foundational skills in statistics

Data analysts who are familiar with analyzing data but want to learn concepts in statistics to be able to do rigorous analysis

Masters and research students who would like to learn statistics

Data analysts who are familiar with data analysis in excel but want to learn statistics using python

Data visualization experts who would like to explore statistics using python

Target Audiences

Anyone who wants to build a career in data science but lacks the foundational skills in statistics

Data analysts who are familiar with analyzing data but want to learn concepts in statistics to be able to do rigorous analysis

Masters and research students who would like to learn statistics

Data analysts who are familiar with data analysis in excel but want to learn statistics using python

Data visualization experts who would like to explore statistics using 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!

Course Curriculum

Chapter 1: Getting started

Lecture 1: Lets get started: Download code and Datasets

Lecture 2: Quick note!

Chapter 2: Python basics

Lecture 1: Installing anaconda distribution and Jupyter

Lecture 2: Tour of Jupyter notebook

Lecture 3: Calculations in Python

Lecture 4: Variables in python

Lecture 5: Collection data types in python – List

Lecture 6: Collection data types in python continued – Tuples, Sets and Dictionaries

Chapter 3: Core programming in Python

Lecture 1: Conditional and logical statements

Lecture 2: For and While loops

Lecture 3: Functions

Chapter 4: Arrays, Matrices and data frames

Lecture 1: Numpy arrays

Lecture 2: ndarrays in numpy

Lecture 3: Access values from a matrix

Lecture 4: Pandas Series

Lecture 5: Pandas Data Frames

Lecture 6: Data frame manipulation

Chapter 5: Introduction to Statistical Data Analysis

Lecture 1: Introduction to Statistical Data Analysis

Lecture 2: Variables in Statistical Data Analysis

Lecture 3: Population Vs Samples

Chapter 6: Data visualization in python

Lecture 1: One way tables

Lecture 2: Line Charts and Bar charts

Lecture 3: Pie Charts

Lecture 4: Two way cross tables

Lecture 5: Heat maps

Chapter 7: Univariate data analysis

Lecture 1: Central tendency measures: mean, median, mode

Lecture 2: Dispersion measures: range and interquartile range

Lecture 3: Histogram

Lecture 4: Box plot

Lecture 5: Outliers

Lecture 6: Variance and Standard deviation

Lecture 7: Univariate hands-on exercise

Chapter 8: Bivariate data analysis

Lecture 1: Introduction to Bivariate analysis

Lecture 2: Covariance and Correlation

Lecture 3: Bivariate hands-on exercise

Chapter 9: Probability

Lecture 1: Probability theory

Lecture 2: Estimating simple probabilities – single independent event

Lecture 3: Estimating probability in case of two or more events

Lecture 4: Conditional Probability

Lecture 5: Review the Multiplication law of probability

Lecture 6: Bayes theorem

Chapter 10: Random Distributions

Lecture 1: Random Variables and Probability Distribution

Lecture 2: Using Probability Distribution to Estimate Probabilities

Lecture 3: Normal distribution

Lecture 4: Normal Distribution Hands-on

Lecture 5: T-distribution

Lecture 6: Finding actual values from the probability

Lecture 7: Sampling Distribution

Lecture 8: Central limit theorem hands-on

Chapter 11: Hypothesis Testing – Test of Means

Lecture 1: Introduction to Inferential statistics and hypothesis testing

Lecture 2: Introduction to test of means

Lecture 3: Steps for conducting test of means

Lecture 4: One sample, right tail test

Lecture 5: One sample, left tail test

Lecture 6: One sample, two tail T test

Lecture 7: Two sample, unpaired T test

Lecture 8: Two sample, paired T test

Lecture 9: Errors in hypothesis testing

Chapter 12: Analysis of Variance (ANOVA)

Lecture 1: ANOVA Introduction

Lecture 2: ANOVA Intuition

Lecture 3: One Way ANOVA manual computation

Lecture 4: One Way ANOVA using python

Lecture 5: Two Way ANOVA – case 1 (Diet Plan)

Lecture 6: Two Way ANOVA – case 2 (Movies analysis)

Chapter 13: Test of proportions

Lecture 1: Introduction to test of proportions and independence using Chi-square test

Lecture 2: Chi square test hands-on

Chapter 14: Simple Linear Regression

Lecture 1: Introduction to Linear Regression

Lecture 2: Goodness of fit

Lecture 3: Condition for linear regression

Instructors

Farzan Sajahan
Business Management and Data Science Consultant

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Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!