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Business Analytics In Python Mastering DataDriven Insights
Published 4/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 17.17 GB | Duration: 27h 37m 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 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 -