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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