Business Data Analytics & Intelligence with Python 2023 - HowToFree

Name: Business Data Analytics & Intelligence with Python 2023 - HowToFree | Free Tutorials
Uploaded: 2024-01-05T03:48:47+00:00
Description: Welcome to our immersive and transformative free video course for 2023, delving into the captivating realm of Business Data Analytics & Intelligence with

Welcome to our immersive and transformative free video course for 2023, delving into the captivating realm of Business Data Analytics & Intelligence with Python. This comprehensive journey spans over 30 hours of enriching video content, providing a robust foundation for individuals at all proficiency levels.

Throughout this course, we will walk you through the ever-changing field of business analytics as we unravel the complexities of data-driven decision-making. Unleash the full potential of Python, a formidable programming language, as we investigate its uses for garnering insightful information from data.

Whether you’re a beginner or an experienced professional, our meticulously crafted course ensures an inclusive learning experience. Gain practical skills and insights that are directly applicable to real-world scenarios, empowering you to make informed decisions and drive business success.

Table of Contents of Data Analytics & Intelligence with Python

Embark on this educational adventure and equip yourself with the tools needed to thrive in the evolving field of Business Data Analytics & Intelligence. Unleash the power of Python and transform your analytical capabilities with our engaging and enlightening video course. Join us as we pave the way for your success in the world of data analytics!

What You’ll Gain from This Business Data Analytics Course:

Professional Business Analyst Skills: Acquire the expertise needed to become a professional Business Analyst and land lucrative job opportunities.
Guidance from Industry Experts: Receive step-by-step guidance from seasoned industry professionals, ensuring a comprehensive and structured learning experience.
Python Proficiency: Harness the power of Python for diverse applications, including statistics, causal inference, econometrics, segmentation, matching, and predictive analytics.
Cutting-Edge Tools and Techniques: Master the latest data and business analysis tools, including Google Causal Impact, Facebook Prophet, Random Forest, and more, staying ahead of the curve in the dynamic analytics landscape.
Practical Challenges and Exercises: Engage in hands-on challenges and exercises that reinforce your knowledge, preparing you for real-world scenarios.
Understanding Business Analyst Roles: Learn the responsibilities of a Business Analyst, how they provide value, and why they are in high demand in today’s job market.
Real Dataset Analysis: Analyze real datasets covering diverse topics such as Moneyball, wine quality, Wikipedia searches, employee remote work satisfaction, and more, gaining practical experience in data interpretation.
Data-Driven Decision-Making: Develop the skills to make informed, data-driven decisions, a crucial competency for success in business analytics.
Advanced Python Proficiency: Enhance your proficiency in Python, one of the most popular programming languages, strengthening your coding capabilities.
Impactful Case Studies: Explore case studies demonstrating how analytics have shaped the world, contributing to individual and corporate success.
Advanced Data Analytics Skills: Develop advanced skills in data analytics and statistics, positioning yourself as a sought-after business data analyst.
Business Analysis Methodologies: Gain a deep understanding of business analysis methodologies and learn how to apply them effectively in real-world business scenarios.
Data Visualization Techniques: Master data analysis and visualization techniques to effectively communicate insights to stakeholders and drive impactful business decisions.
Key Concepts in Business Analytics: Familiarize yourself with key concepts and methods of business analytics, including statistical modeling, forecasting, and optimization.

Course Content of Data Analytics & Intelligence with Python

1. Introduction

Python for Business Analytics & Intelligence
Introduction
Join Our Online Classroom!
Exercise: Meet Your Classmates + Instructor
Setting up the Course Material
The Modern Day Business Analyst
ZTM Resources
Monthly Coding Challenges, Free Resources and Guides

2. PART A: STATISTICS

What are Statistics and why are they important?

3. Basic Statistics

Basic Statistics – Game Plan
Arithmetic Mean
CASE STUDY: Moneyball (Briefing)
Python – Directory, Libraries and Data
Python – Mean
EXERCISE: Python
Median and Mode
Python – Median
EXERCISE: Python
Python – Mode
EXERCISE: Python
Correlation
Mean
Median
Mode
Python – Correlation
EXERCISE: Python – Correlation
Standard Deviation
Python – Standard Deviation
EXERCISE: Python – Standard Deviation
CASE STUDY: Moneyball

4. Intermediary Statistics

Intermediary Statistics – Game Plan
Normal Distribution
CASE STUDY: Wine Quality (Briefing)
Python – Preparing Script and Loading Data
Python – Normal Distribution Visualization
EXERCISE: Python – Normal Distribution
p_value
Shapiro-Wilks Test
Python – Shapiro-Wilks Test
EXERCISE: Python – Shapiro-Wilks
Standard Error of the Mean
Python – Standard Error
EXERCISE: Python – Standard Error
Z-Score
Confidence interval
Python – Confidence Interval
EXERCISE: Python – Confidence Interval
T-test
CASE STUDY: Remote Work Predictions (Briefing)
Python – T-test
EXERCISE: Python – T-test
Chi-square test
Python – Chi-square test
EXERCISE: Python – Chi-square
Powerposing and p-hacking

5. Linear Regression

Linear Regression – Game Plan
CASE STUDY: Diamonds (Briefing)
Linear Regression
Python – Preparing Script and Loading Data
Python – Isolate X and Y
Python – Adding Constant
Linear Regression Output
Python – Linear Regression model and summary
Python – Plotting Regression
Dummy Variable Trap
Python – Dummy Variable
EXERCISE: Python – Linear Regression

7. Multilinear Regression Data Analytics & Intelligence with Python

Multilinear Regression – Game Plan
The Concept of Multilinear Regression
CASE STUDY: Professors’ salary (Briefing)
Python – Preparing Script and Loading Data
Python – Summary Statistics
Outliers
Python – Plotting Continuous Variables
Python – Correlation Matrix
Python – Categorical Variables
Python – For Loop
Python – Creating Dummy Variables
Python
Isolate X and Y
Python – Adding Constant
Under and Over Fitting
Training and Test Set
Python – Train and Test Split
Python – Multilinear Regression
Accuracy KPIs (Key Performance Indicators)
Python – Model Predictions
Python – Accuracy Assessment
CHALLENGE: Introduction
CHALLENGE: Solutions

8. Logistic Regression

Logistic Regression – Game Plan
CASE STUDY: spam Emails (Briefing)
Logistic Regression
Python – Preparing Script and Loading Data
Python – Summary Statistics
Python – Histogram and Outlier Removal
Python – Correlation Matrix
Python – Transforming Dependent Variable
Python – Prepare X and Y
Python – Training and Test Set
How to Read Logistic Regression Coefficients
Python – Logistic Regression
Python – Function to Read Coefficients
Python – Predictions
Confusion Matrix
Python – Confusion Matrix
Python – Confusion Matrix
Python – Manual Accuracy Assessment
Python – Classification Repon
CHALLENGE: Introduction
CHALLENGE: Solutions

9. PART B: ECONOMETRICS & CAUSAL INFERENCE

What are Econometrics & Causal Inference, and why are they important?

10. Google Causal Impact (Econometrics and Causal Inferenc

Why Econometrics and Causal Inference
Google Causal Impact – Game Plan
Time Series Data
CASE STUDY: Bitcoin and PayPal (Briefing)
Difference-in-Differences Framework
Causal Impact Step-by-Step Guide
Python – Libraries and Dates
Python – Dates
Python – Load Bitcoin Price Data
Assumptions
Python – Loading More Stock Data
Python – Data Preparation
Python – Training Dataframe
Correlation Recap and Stationarity
Python – Stationarity
Python – Stationarity
Python – Correlation Matrix and Heatmap
Python – Google Causal Impact Setup
Python – Google Causal Impact
Interpreting the Causal Impact Plots
Python – Causal Impact Results
CHALLENGE: Introduction
CHALLENGE: Solutions
EXERCISE: Imposter Syndrome

11. Matching

Matching – Game Plan
Matching
CASE STUDY: Catholic Schools & Standardized Tests (Briefing)
Python – Libraries and Directory
Python – Loading Data
Unconfoundedness
Python – Comparing Means per Group
T-Test
Python
Python – T-Test Loop
Python – Chi-square Test
Python – Chi-square Loop
The Curse of Dimensionality
Python – Transforming Race Variable
Python – Transforming Education Variable
Python – Cleaning and Preparing Dataframe
Common Support Region
Python – Logistic Regression for Common Support Region
Python – Plotting Common Support Region
Python – Matching Model
Matching Robustness Check
Python – Matching Robustness Repeated Samples
Python – Removing 1 Confounder
CHALLENGE: Introduction
CHALLENGE: Solutions
My Experience with Matching

12. PART C: SEGMENTATION

What is Segmentation and why is it important?

13. RFM (Recency, Frequency, Monetary) Analysis

RFM – Game Plan
Value Based Segmentation
RFM Model
CASE STUDY: Online Shopping (Briefing)
Python – Directory and Libraries
Python – Loading Data
Python – Creating Sales Variable
Python – Date Variable
Python – Customer Level Aggregation
Python – Monetary Variable
Python – Tidying up Dataframe
Python – Quartiles
Python
_ RFM Score
Python
RFM Function
Python – Applying RFM Function
Python – Results Summary
CHALLENGE: Introduction
CHALLENGE: Solutions

14. Gaussian Mixture

Gaussian Mixture – Game Plan
Clustering
Gaussian Mixture Model
CASE STUDY: Credit Cards (Briefing)
Python – Directory and Data
Python – Load Data
Python – Transform Character variables
AIC and BIC
Python – Optimal Number of Clusters
Python – Gaussian Mixture Model
Python – Cluster Prediction and Assignment
Python – Interpretation
CHALLENGE: Introduction
CHALLENGE: Solutions
My Experience with Segmentation

15. PART D: PREDICTIVE ANALYTICS

What are Predictive Analytics and why are they important?

16. Random Forest

Random Forest – Game Plan
Ensemble Learning and Random Forest
How Decision Trees Work
CASE STUDY: Credit Cards #2 (Briefing)
Python – Directory and Libraries
Python – Loading Data
Python – Transform Object into Numerical Variables
Python – Summary Statistics
Random Forest Quirks
Python – Isolate X and Y
Python – Training and Test Set
Python – Random Forest Model
Python – Predictions
Python – Classification Report and Fl score
Parameter Tuning
Python – Parameter Grid
Python – Parameter Tuning
CHALLENGE
CHALLENGE
CHALLENGE
: Introduction
: Solutions (Part 1)
: Solutions (Part 2)

17. Facebook Prophet Data Analytics & Intelligence with Python

Facebook Prophet – Game Plan
Structural Time Series
Facebook Prophet
CASE STUDY: Wikipedia (Briefing)
Python – Directory and Libraries
Python – Loading and Inspecting the Data
Python – Transforming Date Variable
Python – Renaming Variables
Dynamic Holidays
Python – Easter Holiday
Python – Black Friday Holiday
Python – Finishing Holiday Preparation
Training and Test Set in Time Series
Python – Training and Test Set
Facebook Prophet Model
Additive vs. Multiplicative Seasonality
Python – Facebook Prophet
Python – Regressor Coefficients
Python – Forecasting
Python – Event Assessment
Python – Accuracy Assessment
Python – Visualization
Cross-Validation
Python – Cross-Validation
Python – Cross-Validation Results and Visualization
Parameters to Tune
Python – Parameter Grid
Python – Parameter Tuning
Python – Parameter Tuning Results
CHALLENGE•
. Introduction – Demand in NYC
CHALLENGE.
Solutions (Part 1)
CHALLENGE.
Solutions (Part 2)
CHALLENGE: Solutions (Part 3)
Forecasting at Uber

18. Where To Go From Here Data Analytics & Intelligence with Python?

Thank You!
Become An Alumni
Endorsements On LinkedIn
Learning Guideline
Coding Challenges

19. BONUS Section

Special Bonus Lecture

Requirements:

No Prior Python Knowledge Needed: While basic Python knowledge is helpful, it is not required to start this course.
Willingness to Learn: Bring your enthusiasm and readiness to dive into the world of Business Data Analytics & Intelligence with Python. Get ready to learn and take meaningful actions throughout the course.

Who Should Enroll in This Business Data Analytics Course?

This comprehensive course is tailor-made for individuals with diverse backgrounds and ambitions. Whether you’re a seasoned developer, a dedicated student, or a programming enthusiast, this program is designed to meet your specific needs and aspirations. Here’s a breakdown of who can benefit the most:
Developers Seeking Mastery: Ideal for developers aspiring to seamlessly learn and master Business Data Analytics, progressing from the fundamentals to securing positions in top-tier companies. A step-by-step guide ensures a structured learning experience.
Ambitious Students: Go beyond the basics! This course is perfect for students who are ready to move past introductory Python and Data Analytics tutorials, seeking a deeper understanding of these crucial skills.
Career Changers: Developers eager to leverage their existing skills in a new and exciting discipline will find this course invaluable. Gain expertise in Business Data Analytics and open up new career pathways.
Programmers Pursuing In-Demand Skills: Stay ahead in the dynamic tech landscape by acquiring one of the most sought-after skills. This course caters to programmers keen on mastering Business Data Analytics, a skill in high demand across industries.
Top 10% Aspirants: Aim to be among the elite Business Data Analysts? This course is for students who are driven to excel and reach the top echelons of their field.
Data Enthusiasts: Dive into hands-on experience with large, intriguing datasets. This course is perfect for students eager to hone their skills while working on real-world projects Data Analytics & Intelligence with Python.
Bootcamp or Tutorial Graduates: Graduates from bootcamps or online tutorials looking to elevate their understanding beyond the basics will find this course to be the perfect next step in their learning journey.
Real-World Learning Seekers: Tired of generic online instructors? Enroll in this course to learn from an industry professional with authentic real-world experience. Benefit from insights that go beyond mere documentation-based teaching.
Beginner Python Developers: For those starting their Python journey, delve into the world of data analytics and statistics. This course provides a solid foundation for beginner Python developers eager to explore these domains.
Embark on a transformative learning experience that goes beyond the ordinary, led by industry experts who bring a wealth of practical knowledge to the table. Elevate your skills, advance your career, and become a proficient Business Data Analyst.

How long is the course Data Analytics & Intelligence with Python?

The course spans over 30 hours of immersive video content, providing a comprehensive learning experience in Business Data Analytics & Intelligence with Python.

Is this course suitable for beginners?

Absolutely! Our course is designed to cater to learners of all levels, ensuring a smooth and structured educational journey.

Which subjects are taught in the course?

A wide range of subjects are covered in the course, including preprocessing, advanced analytics approaches, and the actual use of Python libraries like Matplotlib, NumPy, and Pandas Data Analytics & Intelligence with Python.

Free now and unleash the power of data with our free Python Analytics course – 30+ hours of transformative insights await you! HowToFree.ORG“

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