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Business Analytics In Python Mastering Data–Driven Insights

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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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