Data Science in Marketing: An Introduction Course 2022
Data Science in Marketing: An Introduction Course 2022, available at $19.99, has an average rating of 4.8, with 58 lectures, based on 24 reviews, and has 132 subscribers.
You will learn about Pandas JSON Handling missing data Decision Tree Collaborative filtering Data Cleanup Linear Regression Model Evaluating model Performance K-means Analyzing Customer lifetime value Product analytics Product Recommendation system Interpreting customer segments Analyzing and visualizing KPI This course is ideal for individuals who are Beginners to Data Science or Business Analysts who wish to do more with their data or College graduates who lack real world experience or Business oriented persons (Management or MBAs) who'd like to use data to enhance their business or Software Developers or Engineers who'd like to start learning Data Science or Anyone looking to become more employable as a Data Scientist or Anyone with an interest in using Data to Solve Real World Problems It is particularly useful for Beginners to Data Science or Business Analysts who wish to do more with their data or College graduates who lack real world experience or Business oriented persons (Management or MBAs) who'd like to use data to enhance their business or Software Developers or Engineers who'd like to start learning Data Science or Anyone looking to become more employable as a Data Scientist or Anyone with an interest in using Data to Solve Real World Problems.
Enroll now: Data Science in Marketing: An Introduction Course 2022
Summary
Title: Data Science in Marketing: An Introduction Course 2022
Price: $19.99
Average Rating: 4.8
Number of Lectures: 58
Number of Published Lectures: 58
Number of Curriculum Items: 58
Number of Published Curriculum Objects: 58
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will Learn
- Pandas
- JSON
- Handling missing data
- Decision Tree
- Collaborative filtering
- Data Cleanup
- Linear Regression Model
- Evaluating model Performance
- K-means
- Analyzing Customer lifetime value
- Product analytics
- Product Recommendation system
- Interpreting customer segments
- Analyzing and visualizing KPI
Who Should Attend
- Beginners to Data Science
- Business Analysts who wish to do more with their data
- College graduates who lack real world experience
- Business oriented persons (Management or MBAs) who'd like to use data to enhance their business
- Software Developers or Engineers who'd like to start learning Data Science
- Anyone looking to become more employable as a Data Scientist
- Anyone with an interest in using Data to Solve Real World Problems
Target Audiences
- Beginners to Data Science
- Business Analysts who wish to do more with their data
- College graduates who lack real world experience
- Business oriented persons (Management or MBAs) who'd like to use data to enhance their business
- Software Developers or Engineers who'd like to start learning Data Science
- Anyone looking to become more employable as a Data Scientist
- Anyone with an interest in using Data to Solve Real World Problems
Welcome to the Data Science in Marketing: An Introduction Course 2021
This course teaches you how Data Science can be used to solve real-world business problems and how you can apply these techniques to solve real-world case studies.
Traditional Businesses are hiring Data Scientists in droves, and knowledge of how to apply these techniques in solving their problems will prove to be one of the most valuable skills in the next decade!
“Data Scientist has become the top job in the US for the last 4 years running!” according to Harvard Business Review & Glassdoor.
However, Data Science has a difficult learning curve – How does one even get started in this industry awash with mystique, confusion, impossible-looking mathematics, and code? Even if you get your feet wet, applying your newfound Data Science knowledge to a real-world problem is even more confusing.
This course seeks to fill all those gaps in knowledge that scare off beginners and simultaneously apply your knowledge of Data Science to real-world business problems.
This course has a comprehensive syllabus that tackles all the major components of Data Science knowledge.
Our Learning path includes:
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How Data Science and Solve Many Common Marketing Problems
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The Modern Tools of a Data Scientist– Python, Pandas, Scikit-learn, and Matplotlib.
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Machine Learning Theory– Linear Regressions, Decision Trees, and Model Assessment.
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Data Science in Marketing – Modelling Engagement Rates.
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Data Science in Retail– Customer Segmentation, Lifetime Value, and Customer/Product Analytics
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Unsupervised Learning – K-Means Clustering.
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Recommendation Systems – Collaborative Filtering.
Four (3) Data Science in Marketing Case Studies:
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Analysing Conversion Rates of Marketing Campaigns.
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Predicting Engagement – What drives ad performance?
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Who are Your Best Customers? & Customer Lifetime Values (CLV).
Four (2) Retail Data Science Case Studies:
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Product Analytics (Exploratory Data Analysis Techniques
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Product Recommendation Systems.
Businesses NEED Data Scientists more than ever. Those who ignore this trend will be left behind by their competition. In fact, the majority of new Data Science jobs won’t be created by traditional tech companies (Google, Facebook, Microsoft, Amazon, etc.) they’re being created by your traditional non-tech businesses. The big retailers, banks, marketing companies, government institutions, insurances, real estate and more.
“Consumer data will be the biggest differentiator in the next two to three years. Whoever unlocks the reams of data and uses it strategically will win.”
With Data Scientist salaries creeping up higher and higher, this course seeks to take you from a beginner and turn you into a Data Scientist capable of solving challenging real-world problems.
—
Data Scientist is the buzz of the 21st century for good reason! The tech revolution is just starting and Data Science is at the forefront. Get a head start applying these techniques to all types of Marketing problems by taking this course!
Course Curriculum
Chapter 1: Introduction
Lecture 1: Course structure
Lecture 2: How To Make The Most Out Of This Course
Lecture 3: Important note on tool
Lecture 4: Introduction to Data Models and Structured Data
Lecture 5: Introduction to Pandas
Lecture 6: Importing JSON Files into pandas
Lecture 7: Identifying Semi-Structured and Unstructured Data
Lecture 8: Creating and Modifying Test Dataframes
Lecture 9: Combining DataFrame and Handling Missing Values
Lecture 10: Applying Data Transformation
Chapter 2: Key Performance Indicators and Visualizations
Lecture 1: Data Science and Marketing
Lecture 2: Introduction to Key Performance Indicators and Visualizations
Lecture 3: Computing and visualizing KPIs
Lecture 4: Aggregate conversion rate
Lecture 5: Conversion rates by age
Lecture 6: Conversion vs non-conversion
Lecture 7: Conversions by age and marital status
Lecture 8: Summary of the section
Chapter 3: From engagement to conversion
Lecture 1: Introduction
Lecture 2: Decision trees and interpretations
Lecture 3: Conversion rates by job
Lecture 4: Default Rates by Conversions
Lecture 5: Bank balances by conversions and Conversion rates by number of contacts
Lecture 6: Encoding Months
Lecture 7: Encoding Jobs
Lecture 8: Encoding marital and the housing and loan variables
Lecture 9: Building decision trees
Lecture 10: Interpreting Decision Tree
Lecture 11: Summary of the project
Chapter 4: Product analytics
Lecture 1: Introduction
Lecture 2: Product analytics Implementation Part 1
Lecture 3: Product analytics Implementation Part 2
Lecture 4: Repeat customers Implementation
Lecture 5: Repeat customers Implementation Part 2
Lecture 6: Trending Items over time
Lecture 7: Trending Items over time Implementation
Lecture 8: Analysing Results
Chapter 5: Product Recommender System
Lecture 1: Introduction
Lecture 2: Data Preperation
Lecture 3: Building a customer-item matrix
Lecture 4: Collaborative Filtering
Lecture 5: Item-based product recommendation Part 1
Lecture 6: Item-based product recommendation Part 2
Chapter 6: Customer Lifetime Value
Lecture 1: Introduction
Lecture 2: Data Clean-Up
Lecture 3: Data Analysis Part 1
Lecture 4: Data Analysis Part 2
Lecture 5: Data preparation Part 1
Lecture 6: Data preparation Part 2
Lecture 7: Data preparation Part 3
Lecture 8: Building Linear Regression Model
Lecture 9: Evaluating Model Performance
Chapter 7: Data-Driven Customer Segmentation
Lecture 1: Introduction
Lecture 2: Data Cleanup
Lecture 3: K-means Clustering
Lecture 4: Selecting the best number of clusters
Lecture 5: Interpreting customer segments
Chapter 8: Thank you
Lecture 1: Thank you
Instructors
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Hoang Quy La
Electrical Engineer
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 1 votes
- 3 stars: 2 votes
- 4 stars: 3 votes
- 5 stars: 17 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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