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Business and Data Analytics in Python

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Rating4.9
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Business and Data Analytics in Python, available at $44.99, has an average rating of 4.91, with 187 lectures, 10 quizzes, based on 21 reviews, and has 237 subscribers.

You will learn about 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. This course is ideal for individuals who are 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. or Professionals in Business and Finance: Individuals in business, finance, marketing, or related fields seeking to enhance their decision-making with data-driven insights. or Entrepreneurs and Small Business Owners: Those looking to leverage data to make informed decisions, understand market trends, and drive business growth. or Business Managers in Analytical Departments: Managers and supervisors looking to deepen their analytical capabilities to drive decision-making and strategy. or Students in Business, Economics, or IT: Undergraduates or postgraduates desiring to enhance their academic knowledge with practical analytics and Python skills. or IT Professionals and Software Developers: Those looking to expand their skills into business analytics to support data-driven projects or transition into analytics roles. or Curious Learners: Anyone interested in applying Python to tackle real-world business challenges and to inform decisions with data. It is particularly useful for 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. or Professionals in Business and Finance: Individuals in business, finance, marketing, or related fields seeking to enhance their decision-making with data-driven insights. or Entrepreneurs and Small Business Owners: Those looking to leverage data to make informed decisions, understand market trends, and drive business growth. or Business Managers in Analytical Departments: Managers and supervisors looking to deepen their analytical capabilities to drive decision-making and strategy. or Students in Business, Economics, or IT: Undergraduates or postgraduates desiring to enhance their academic knowledge with practical analytics and Python skills. or IT Professionals and Software Developers: Those looking to expand their skills into business analytics to support data-driven projects or transition into analytics roles. or Curious Learners: Anyone interested in applying Python to tackle real-world business challenges and to inform decisions with data.

Enroll now: Business and Data Analytics in Python

Summary

Title: Business and Data Analytics in Python

Price: $44.99

Average Rating: 4.91

Number of Lectures: 187

Number of Quizzes: 10

Number of Published Lectures: 187

Number of Published Quizzes: 10

Number of Curriculum Items: 197

Number of Published Curriculum Objects: 197

Original Price: $99.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

  • 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.

Target Audiences

  • 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.

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 and data 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.

  • Understand how to use time series analysis to predict future performance, including challenging time series like stock prices.

  • Practical application of the Meta Prophet model, understanding its components, parameter estimation, and forecasting capabilities.

  • Powerful causal inference tools like the Difference in Difference framework and Google Causal Impact model

  • 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.

Go ahead and watch the many preview videos available to peek into most learning modules and see what you will learn.

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.

Course Curriculum

Chapter 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

Chapter 2: Introduction to Business Analytics

Lecture 1: Understanding the Power of Business Analytics

Lecture 2: The Art and Science of Business Analytics

Lecture 3: Business Analytics and Big Data

Lecture 4: Executing Business Analytics Projects – The CRISP-DM Methodology, Part I

Lecture 5: Executing Business Analytics Projects – The CRISP-DM Methodology, Part II

Lecture 6: Executing Business Analytics Projects – The Microsoft TDSP Methodology

Lecture 7: Business Analytics & Data Science Tools

Chapter 3: Statistics in Business Analytics

Lecture 1: Introduction to Statistics

Lecture 2: What is Statistics

Lecture 3: Datasets

Lecture 4: Data Types

Lecture 5: Statistics Vs Probabilities

Lecture 6: Should you invest in Bitcoins?

Chapter 4: Descriptive Statistics

Lecture 1: Random Variables

Lecture 2: 1st Measure of Central Tendency: Mean

Lecture 3: How good is the Mean?

Lecture 4: 1st Measure of Spread: Standard Deviation

Lecture 5: HANDS ON – Descriptive Statistics – Part I

Lecture 6: HANDS ON – Descriptive Statistics – Part II

Lecture 7: Sample Vs Population

Lecture 8: Degrees of Freedom

Lecture 9: 2nd Measure of Central Tendency: Medium

Lecture 10: HANDS ON – Median Household Income

Lecture 11: Mode, Percentiles, and Box Plot

Lecture 12: HANDS ON – Analysis of Median Household Income – Part I

Lecture 13: HANDS ON – Analysis of Median Household Income – Part II

Lecture 14: Distributions of Random Variables

Lecture 15: HANDS ON – Service Calls in Washington DC – Part I

Lecture 16: HANDS ON – Service Calls in Washington DC – Part II

Lecture 17: HANDS ON – Service Calls in Washington DC – Part III

Lecture 18: HANDS ON – Service Calls in Washington DC – Part IV

Lecture 19: Correlation and Contingency Tables

Lecture 20: HANDS ON – Analyzing Blood Pressure & Cholesterol and Comparing Salaries

Chapter 5: Inferential Statistics

Lecture 1: Sample & Data

Lecture 2: Population & Sampling Techniques

Lecture 3: HANDS ON – Random Sampling – Part I

Lecture 4: HANDS ON – Stratified Sampling – Part II

Lecture 5: HANDS ON – Clustering Sampling – Part III

Lecture 6: Parameter Estimation Procedure

Lecture 7: Mean of the Sample as Parameter

Lecture 8: Bootstrapping & Sample Distribution of the Means

Lecture 9: HANDS ON – Sample Distribution of the Means – Part I

Lecture 10: HANDS ON – Sample Distribution of the Means – Part II

Lecture 11: Central Limit Theorem

Lecture 12: HANDS ON – Central Limit Theorem – Part I

Lecture 13: HANDS ON – Central Limit Theorem – Part II

Lecture 14: HANDS ON – Central Limit Theorem – Part III

Lecture 15: Point Estimates

Lecture 16: Confidence Intervals

Lecture 17: HANDS ON – Confidence Intervals

Chapter 6: Data Preprocessing

Lecture 1: Introduction to Data Preprocessing

Lecture 2: HANDS ON – Using Existing Sample Datasets from Python Libraries

Lecture 3: HANDS ON – Using Existing Sample Datasets from Python Libraries – Part II

Lecture 4: HANDS ON – Using Existing Sample Datasets from Python Libraries – Part III

Lecture 5: Understanding Data Formats

Lecture 6: HANDS ON – Introduction to Delimited Formats

Lecture 7: HANDS ON – Comma Delimited Files – Part I

Lecture 8: HANDS ON – Comma Delimited Files – Part II

Lecture 9: HANDS ON – Other Delimited Formats

Lecture 10: HANDS ON – Headless Files

Lecture 11: HANDS ON – Notes on Pandas Index

Lecture 12: HANDS ON – The ARFF Format

Lecture 13: HANDS ON – The JSON Format

Lecture 14: HANDS ON – SQL-based Data

Lecture 15: Documenting Data

Lecture 16: Data Preprocessing Tasks

Lecture 17: Different Data Issues

Lecture 18: Automation and Data Shuffling

Lecture 19: Feature Engineering

Lecture 20: Dealing with Categorical Variables

Lecture 21: Filling the Blanks – Handling Missing Values

Lecture 22: HANDS ON – Missing Values – Identifying Missing Values

Lecture 23: HANDS ON – Missing Values – Replacing Missing Values with the Mean

Lecture 24: HANDS ON – Missing Values – Replacing Missing Values with a Random Draw

Lecture 25: Unusual Values – The Art of Visually Detecting Outliers

Lecture 26: HANDS ON – Outliers, Visual Methods – Part I

Lecture 27: HANDS ON – Outliers, Visual Methods – Part II

Lecture 28: Moving the Scale with Data Normalization

Lecture 29: HANDS ON – Normalization, MinMax Method

Lecture 30: HANDS ON – Normalization, Z-Score Method

Lecture 31: HANDS ON – Normalization, Decimal Method

Lecture 32: Unusual Values – Numerical methods for Outliers Detection

Lecture 33: HANDS ON – Detecting Outliers, Z-Score Method

Lecture 34: HANDS ON – Detecting Outliers, IQR Method

Lecture 35: Changing the Shape of a Distribution

Lecture 36: HANDS ON – Transforming Variables, Log Transformation

Instructors

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  • 5 stars: 18 votes

Frequently Asked Questions


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