Business Analytics In Python Mastering DataDriven Insights free download

Academy Udemy

Category:
Management

Views: 12
Likes: 0

Catalogue

Uploader:

oblivion

Upload Date:

4/11/2024 10:39:13 PM

To download click on link in the Links Table below

Description: Click to see full description

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 -

Links Table (Note: Download all parts and then extract file if below is multiple parts)

Links

Download Link

3254799

Part

https://nitroflare.com/view/09B4AEBABFCC78A/xqntg.Business.Analytics.In.Python.Mastering.DataDriven.Insights.part15.rar

3254800

Part

https://nitroflare.com/view/42648F6743C5C8D/xqntg.Business.Analytics.In.Python.Mastering.DataDriven.Insights.part04.rar

3254801

Part

https://nitroflare.com/view/2A5B6AE073CD642/xqntg.Business.Analytics.In.Python.Mastering.DataDriven.Insights.part16.rar