Business Analytics In Python: Mastering Data-Driven Insights
Published 4/2024
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Becoming a Business Analytics Practitioner

What you'll learn

Master Business Analytics Basics: Understand fundamental concepts and data-driven decision-making techniques.

Python Proficiency: Gain skills in Python for data analysis with key libraries like Pandas and NumPy.

Statistical Decision Making: Learn inferential statistics to support business insights.

Econometrics & Regression: Master econometric models and regression analysis for predicting outcomes.

Time-Series Analysis: Acquire forecasting skills using Python for economic and business trends.

Customer Segmentation: Analyze customer behavior and market segments for targeted strategies.

Cultivate a Data-Driven Mindset: Develop critical thinking for data interpretation and decision-making.

Real-World Data Practice: Apply business analytics techniques to industry-specific datasets.

High Academic Quality: Experience content and methods at the level of graduate classes in U.S. universities.

Career Preparation: Equip yourself for roles in business analytics with in-demand skills and knowledge.

Requirements

An understanding of Python programming at the most basic level. You should be comfortable with variables, basic data types, loops, and functions.

Willingness to Learn: Approach the course with enthusiasm for learning new analytical techniques and applying them to real-world business scenarios.

Description

Course Description:Welcome to "Business Analytics in Python: Mastering Data-Driven Insights," where you embark on a transformative journey to unravel the complexities of business analytics using Python. This course is meticulously designed to equip you with the knowledge, skills, and practical experience needed to excel in the fast-evolving world of business analytics.What You Will Learn:Fundamental principles of business analytics and their application in real-world scenarios.Hands-on proficiency in Python for data collection, manipulation, analysis, and visualization.Advanced statistical methods for insightful data analysis and decision-making.Techniques in forecasting, regression, and econometrics to predict market trends and business performance.Practical application of the Meta Prophet model, understanding its components, parameter estimation, and forecasting capabilities.Essentials of Markov Models, exploring their significance in predictive analytics.Course Features:Comprehensive video lectures that blend theoretical knowledge with practical applications.Interactive Python notebooks and real-world datasets for hands-on learning in Google Colab.Case studies and examples from various industries to illustrate the impact of business analytics.Quizzes and exercises to reinforce learning and apply concepts.Who Should Enroll:Aspiring data analysts and business professionals looking to leverage data for strategic decision-making.IT professionals and software developers aiming to pivot or advance in the field of business analytics.Entrepreneurs and business owners seeking to understand and apply data analytics for business growth.Anybody desiring a practical, hands-on approach to learning business analytics.Prerequisites:Basic understanding of Python programming.Curiosity and willingness to dive into the data-driven world of business analytics.Embark on this journey with "Business Analytics in Python: Mastering Data-Driven Insights" and transform your ability to analyze, predict, and make informed business decisions using the power of data analytics.

Overview

Section 1: Your Business Analytics Journey

Lecture 1 Course Details - Overview of your learning journey.

Lecture 2 AIM 315 - Business Analytics in Python: Mastering Data-Driven Insights

Lecture 3 Preparing your Lab Environment: Introduction to Google Lab

Lecture 4 How to Download and Use the Resources Provided in this Class

Section 2: Introduction to Business Analytics

Lecture 5 Understanding the Power of Business Analytics

Lecture 6 The Art and Science of Business Analytics

Lecture 7 Business Analytics and Big Data

Lecture 8 Executing Business Analytics Projects - The CRISP-DM Methodology, Part I

Lecture 9 Executing Business Analytics Projects - The CRISP-DM Methodology, Part II

Lecture 10 Executing Business Analytics Projects - The Microsoft TDSP Methodology

Lecture 11 Business Analytics & Data Science Tools

Section 3: Statistics in Business Analytics

Lecture 12 Introduction to Statistics

Lecture 13 What is Statistics

Lecture 14 Datasets

Lecture 15 Data Types

Lecture 16 Statistics Vs Probabilities

Lecture 17 Should you invest in Bitcoins?

Section 4: Descriptive Statistics

Lecture 18 Random Variables

Lecture 19 1st Measure of Central Tendency: Mean

Lecture 20 How good is the Mean?

Lecture 21 1st Measure of Spread: Standard Deviation

Lecture 22 HANDS ON - Descriptive Statistics - Part I

Lecture 23 HANDS ON - Descriptive Statistics - Part II

Lecture 24 Sample Vs Population

Lecture 25 Degrees of Freedom

Lecture 26 2nd Measure of Central Tendency: Medium

Lecture 27 HANDS ON - Median Household Income

Lecture 28 Mode, Percentiles, and Box Plot

Lecture 29 HANDS ON - Analysis of Median Household Income - Part I

Lecture 30 HANDS ON - Analysis of Median Household Income - Part II

Lecture 31 Distributions of Random Variables

Lecture 32 HANDS ON - Service Calls in Washington DC - Part I

Lecture 33 HANDS ON - Service Calls in Washington DC - Part II

Lecture 34 HANDS ON - Service Calls in Washington DC - Part III

Lecture 35 HANDS ON - Service Calls in Washington DC - Part IV

Lecture 36 Correlation and Contingency Tables

Lecture 37 HANDS ON - Analyzing Blood Pressure & Cholesterol and Comparing Salaries

Section 5: Inferential Statistics

Lecture 38 Sample & Data

Lecture 39 Population & Sampling Techniques

Lecture 40 HANDS ON - Random Sampling - Part I

Lecture 41 HANDS ON - Stratified Sampling - Part II

Lecture 42 HANDS ON - Clustering Sampling - Part III

Lecture 43 Parameter Estimation Procedure

Lecture 44 Mean of the Sample as Parameter

Lecture 45 Bootstrapping & Sample Distribution of the Means

Lecture 46 HANDS ON - Sample Distribution of the Means - Part I

Lecture 47 HANDS ON - Sample Distribution of the Means - Part II

Lecture 48 Central Limit Theorem

Lecture 49 HANDS ON - Central Limit Theorem - Part I

Lecture 50 HANDS ON - Central Limit Theorem - Part II

Lecture 51 HANDS ON - Central Limit Theorem - Part III

Lecture 52 Point Estimates

Lecture 53 Confidence Intervals

Lecture 54 HANDS ON - Confidence Intervals

Section 6: Data Preprocessing

Lecture 55 Introduction to Data Preprocessing

Lecture 56 HANDS ON - Using Existing Sample Datasets from Python Libraries

Lecture 57 HANDS ON - Using Existing Sample Datasets from Python Libraries - Part II

Lecture 58 HANDS ON - Using Existing Sample Datasets from Python Libraries - Part III

Lecture 59 Understanding Data Formats

Lecture 60 HANDS ON - Introduction to Delimited Formats

Lecture 61 HANDS ON - Comma Delimited Files - Part I

Lecture 62 HANDS ON - Comma Delimited Files - Part II

Lecture 63 HANDS ON - Other Delimited Formats

Lecture 64 HANDS ON - Headless Files

Lecture 65 HANDS ON - Notes on Pandas Index

Lecture 66 HANDS ON - The ARFF Format

Lecture 67 HANDS ON - The JSON Format

Lecture 68 HANDS ON - SQL-based Data

Lecture 69 Documenting Data

Lecture 70 Data Preprocessing Tasks

Lecture 71 Different Data Issues

Lecture 72 Automation and Data Shuffling

Lecture 73 Feature Engineering

Lecture 74 Dealing with Categorical Variables

Lecture 75 Filling the Blanks - Handling Missing Values

Lecture 76 HANDS ON - Missing Values - Identifying Missing Values

Lecture 77 HANDS ON - Missing Values - Replacing Missing Values with the Mean

Lecture 78 HANDS ON - Missing Values - Replacing Missing Values with a Random Draw

Lecture 79 Unusual Values - The Art of Visually Detecting Outliers

Lecture 80 HANDS ON - Outliers, Visual Methods - Part I

Lecture 81 HANDS ON - Outliers, Visual Methods - Part II

Lecture 82 Moving the Scale with Data Normalization

Lecture 83 HANDS ON - Normalization, MinMax Method

Lecture 84 HANDS ON - Normalization, Z-Score Method

Lecture 85 HANDS ON - Normalization, Decimal Method

Lecture 86 Unusual Values - Numerical methods for Outliers Detection

Lecture 87 HANDS ON - Detecting Outliers, Z-Score Method

Lecture 88 HANDS ON - Detecting Outliers, IQR Method

Lecture 89 Changing the Shape of a Distribution

Lecture 90 HANDS ON - Transforming Variables, Log Transformation

Lecture 91 HANDS ON - Transforming Variables, Testing for Normality

Lecture 92 Turning Categories to Numbers

Lecture 93 HANDS ON - Categorical to Numerical, One Hot Encoding Method

Lecture 94 HANDS ON - Categorical to Numerical, Mapping Method

Lecture 95 HANDS ON - Numerical to Categorical, Equal Width Binning

Lecture 96 HANDS ON - Numerical to Categorical, Equal Frequency Binning

Lecture 97 HANDS ON - Numerical to Categorical, Single Binary Variable

Lecture 98 Dealing with Data Imbalances

Lecture 99 HANDS ON - Data Imbalance, Undersampling Method

Lecture 100 HANDS ON - Data Imbalance, Oversampling Method

Section 7: Estimation & Regression Models

Lecture 101 Introduction to Estimation & Regression

Lecture 102 Introduction to Simple Linear Regression

Lecture 103 HANDS ON - Simple Linear Regression - Getting Ready

Lecture 104 HANDS ON - Simple Linear Regression - Generating the Model

Lecture 105 Understanding the Linear Model - Degrees of Freedom

Lecture 106 Understanding the Linear Model - Errors and Coefficient of Determination

Lecture 107 How Good is the Simple Linear Regression Model?

Lecture 108 HANDS ON - Calculating the Errors and the R-Squared coefficient

Lecture 109 HANDS ON - Estimating the Simple Linear Regression Coefficients

Lecture 110 A Closer Look at the Residuals

Lecture 111 Introduction to the Multiple Regression Model

Lecture 112 HANDS ON - Generating a Multiple Regression Model

Lecture 113 HANDS ON - The Adjusted R-Squared

Lecture 114 HANDS ON - The F-Statistics for Coefficient Meaningfulness

Lecture 115 HANDS ON - The Log-Likelihood Measure

Lecture 116 Multiple Regression Model - The AIC and BIC Metrics

Lecture 117 HANDS ON - Calculating AIC & BIC

Lecture 118 Multiple Regression - Which Model is Better?

Lecture 119 HANDS ON - Testing Model Assumptions - Part I

Lecture 120 HANDS ON - Testing Model Assumptions - Part II

Section 8: Time Series and Econometrics

Lecture 121 Introduction to Econometrics, Time Series, and Causal Inference

Lecture 122 Introduction to Time Series Analysis

Lecture 123 Time Series Analysis for a Milk Production Company

Lecture 124 HANDS ON - Predicting Milk Production into the Future

Lecture 125 Quality Metrics for a Time Series Analysis

Lecture 126 HANDS ON - Quality Metrics for a Time Series Analysis

Lecture 127 Introduction to Time Series Analysis for Stock Prices

Lecture 128 Autocorrelation in Time Series Analysis

Lecture 129 Stationarity and Stationary Time Series

Lecture 130 HANDS ON - Stationarity and Autocorrelation

Lecture 131 Introduction to Moving Averages - The Simple Moving Average (SMA)

Lecture 132 HANDS ON - Simple Moving Average - Part I

Lecture 133 HANDS ON - Simple Moving Average - Part II

Lecture 134 The Weighted Moving Average (WMA) and the Double Simple Moving Average (DSMA)

Lecture 135 The Exponential Moving Average and the Double Exponential Moving Average (DEMA)

Lecture 136 The Hull Moving Average

Lecture 137 The Directional Moving Index (DMI) and the Moving Average convergence Divergence

Lecture 138 HANDS ON - Moving Beyond SMA

Section 9: Causal Inference

Lecture 139 Introduction to Causal Inference Analysis

Lecture 140 Two Causal Inference Frameworks: DiD and Google Causal Impact Analysis

Lecture 141 The Causal Inference Analytical Process

Lecture 142 The Difference-in-Difference (DiD) Method

Lecture 143 HANDS ON - DiD Analysis - Effect of an announcement on stock price.

Lecture 144 The Google Causal Impact Analysis Method

Lecture 145 HANDS ON - Announcements & Stock Prices Causal Analysis

Section 10: Customer Segmentation

Lecture 146 Customer Segmentation - Section Presentation

Lecture 147 Introduction to Segmentation Analysis

Lecture 148 The Pareto Principle

Lecture 149 Recency Frequency Monetary (RFM) Analysis Process

Lecture 150 RFM Formulations

Lecture 151 HANDS ON - Understanding Your Buyers

Lecture 152 Segmentation Via Clustering - The KMeans Algorithm

Lecture 153 HANDS ON - Understanding KMeans Clustering

Lecture 154 The KMedoids Clustering Variation

Lecture 155 HANDS ON - Understanding the KMedoids Clustering

Lecture 156 Picking the Correct Distance Function in Clustering Your Data

Lecture 157 Selecting the Number of Clusters K

Lecture 158 HANDS ON - The Elbow and Silhouette Methods

Lecture 159 HANDS ON - Segmenting Buyers via Clustering Analysis

Lecture 160 The Importance of the Shape in Your Data

Lecture 161 HANDS ON - KMeans on Circular Data

Lecture 162 HANDS ON - KMeans on Sparse Data with Unequal Variance

Lecture 163 HANDS ON - KMeans on Anisotropic Data

Lecture 164 Introduction to the Gaussian Mixture Models (GMMs)

Lecture 165 The GMM Model

Lecture 166 HANDS ON - Customer Segmentation with GMM - Part I

Lecture 167 HANDS ON - Customer Segmentation with GMM - Part II

Section 11: Forecasting

Lecture 168 Introduction to Forecasting

Lecture 169 HANDS ON - Forecasting using Additive or Multiplicative Strategy

Lecture 170 Introduction to the Meta Prophet Model

Lecture 171 The Trend Components in Meta Prophet

Lecture 172 Estimating Parameters in the Meta Prophet Algorithm: a Bayesian Approach

Lecture 173 The Sampling Method in Parameter Estimation: MCMC and NUTS methods

Lecture 174 Training Machine Learning Models

Lecture 175 HANDS ON - Forecasting Stock Price with Meta Prophet

Lecture 176 Introduction to Markov Models

Lecture 177 Markov Chains

Lecture 178 The Steady Vector in a Markov Chain

Lecture 179 Simulating a Business Model Success using a Markov Chain

Lecture 180 HANDS ON - Simulating StreamX Business Success - Part I

Lecture 181 HANDS ON - Simulating StreamX Business Success - Part II

Lecture 182 HANDS ON - Simulating StreamX Business Success - Part III

Lecture 183 HANDS ON - Simulating StreamX Business Success - Part IV

Lecture 184 Congratulations and Thank You for Completing this Course

Section 12: Bonus - End of the Course

Lecture 185 Special Bonus Lecture

Aspiring Data Analysts and Business Analysts: Beginners or those transitioning from other fields who want to build a career in data analytics or business analytics.,Professionals in Business and Finance: Individuals in business, finance, marketing, or related fields seeking to enhance their decision-making with data-driven insights.,Entrepreneurs and Small Business Owners: Those looking to leverage data to make informed decisions, understand market trends, and drive business growth.,Business Managers in Analytical Departments: Managers and supervisors looking to deepen their analytical capabilities to drive decision-making and strategy.,Students in Business, Economics, or IT: Undergraduates or postgraduates desiring to enhance their academic knowledge with practical analytics and Python skills.,IT Professionals and Software Developers: Those looking to expand their skills into business analytics to support data-driven projects or transition into analytics roles.,Curious Learners: Anyone interested in applying Python to tackle real-world business challenges and to inform decisions with data.


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