Real-World Business Analysis And Statistics In Python

Master the Power of Pandas, NumPy, Matplotlib, & Statmodels from Data Analytics to Strategic Decision-Making in Python.

What you'll learn

Gain a solid understanding of Python for data analytics. Develop the skills to clean and preprocess real-world datasets, ensuring data quality and reliability.

Learn to use key libraries such as Pandas, NumPy, and Matplotlib for data manipulation, analysis, and visualization.

Master advanced data processing techniques, including feature engineering, data transformation, and handling missing values.

Explore how to implement statmodels API to build statistical models and determine the most influential factors to make effective decisions and recommendations.

Requirements

Basic Python Knowledge

Desire to Learn

Description

Embark on a transformative journey into the realm of Real-World Business Analysis and Statistics in Python with our comprehensive course. In this dynamic learning experience, you will acquire a robust foundation in Python tailored for data analytics, gaining essential skills to navigate, clean, and preprocess real-world datasets effectively. Through hands-on exercises and real-life scenarios, you will become adept at ensuring data quality and reliability, laying the groundwork for informed decision-making in a data-driven world.Dive deep into the heart of data manipulation, analysis, and visualization with key libraries such as Pandas, NumPy, and Matplotlib. Unlock the potential of these powerful tools to derive meaningful insights from complex datasets, equipping you with the capabilities to transform raw information into actionable intelligence. The course will guide you through advanced data processing techniques, including feature engineering, data transformation, and handling missing values, ensuring you possess the skill set needed to tackle the intricacies of real-world data.Beyond foundational skills, the course elevates your proficiency by exploring the application of the statmodels API. Learn how to construct statistical models that unveil the most influential factors in a dataset, enabling you to make effective decisions and recommendations. Through practical applications and case studies, you will gain a holistic understanding of leveraging statistical insights for strategic decision-making in various business scenarios. By the end of the course, you will emerge not only with technical expertise but also with a strategic mindset to tackle business challenges head-on in today's competitive landscape.Join us on this transformative learning journey, where theory meets practical application, and empower yourself with the tools and knowledge to navigate the complexities of real-world data analytics and drive meaningful impact within your organization.

Overview

Section 1: Setting Up Your Data Analysis Platform

Lecture 1 Install Python and Jupyter Notebook

Lecture 2 Setting Up ChatGPT for SMART Analysis

Lecture 3 Download Resources Used in this Course

Section 2: Phase 1: Steps in Data Cleaning for Real - World Projects

Lecture 4 Importing Sales Dataset in Jupyter Notebook

Lecture 5 Imputing missing values with scikit-learn method

Lecture 6 Finding out and dealing with inconsistent values

Lecture 7 Fixing wrong data types and assign the correct type

Lecture 8 Dropping duplicates making dataset error free

Section 3: Phase 2: Steps in Data Manipulation for Real - World Projects

Lecture 9 Organinzing and Sorting dataset and finding insight

Lecture 10 Conditional filtering, data splitting, data partitioning

Lecture 11 Merge extra necessary variables to the dataset

Lecture 12 Concatenating extra necessary data within existing data

Section 4: Phase 3: Steps in Exploratory Data Analysis for Real - World Challenges

Lecture 13 Understand exploratory data analysis

Lecture 14 Challenge 1: What is the country of residence for the majority of customers?

Lecture 15 Challenge 2: Find the descriptives of order value, cost and refund.

Lecture 16 Challenge 3: Find top 3 product categories based on both order value and cost.

Lecture 17 Challenge 4: Who are the most loyal customers of your superstore?

Lecture 18 Challenge 5: Which sales manager sold product that has the highest sales volume?

Lecture 19 Challenge 6: Find the relationship between order value, cost and refund amount.

Section 5: Phase 4: Understanding Statistical Analysis and Hypothesis Testing

Lecture 20 Various aspects of hypothesis testing in statistics

Lecture 21 Understand confidence level, significance level and p-value

Lecture 22 Understand complete steps in hypothesis testing

Section 6: Phase 5. Transform Data into Normal Distribution Format

Lecture 23 Testing normal distribution of numeric variables

Lecture 24 Square root transformation for normal distribution conversion

Lecture 25 Logarithmic transformation for normal distribution conversion

Lecture 26 Box-cox transformation for normal distribution conversion

Lecture 27 Yeo-Johnson transformation for normal distribution conversion

Section 7: Phase 6: Perform Statistical Data Analysis and Hypothesis Testing

Lecture 28 One - way ANOVA: Testing the difference

Lecture 29 Pearson correlation test: Testing the relationship

Lecture 30 Regression analysis: Testing the influence

Section 8: Practice Time!!!

Lecture 31 Download the practice dataset

Data analytics beginners who desire to master Python programming for data manipulation, analysis, and visualization.,Businesspeople who want to make data-driven judgments. The course shows how to use analytics to address business problems.,People seeking to improve decision-making and company value with data-driven insights. The course shows how to use data analytics to business problems.