What you'll 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
Requirements
Solid Python knowledge is important
Description
Welcome to the Data Science in Marketing: An Introduction Course 2021
This course teaches you how Data Science & Deep Learning 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:
How Data Science and Solve Many Common Marketing Problems
The Modern Tools of a Data Scientist - Python, Pandas, Scikit-learn, and MatDescriptionlib.
Machine Learning Theory - Linear Regressions, Decision Trees, and Model Assessment.
Data Science in Marketing - Modelling Engagement Rates.
Data Science in Retail - Customer Segmentation, Lifetime Value, and Customer/Product Analytics
Unsupervised Learning - K-Means Clustering.
Recommendation Systems - Collaborative Filtering.
Four (3) Data Science in Marketing Case Studies:
Analysing Conversion Rates of Marketing Campaigns.
Predicting Engagement - What drives ad performance?
Who are Your Best Customers? & Customer Lifetime Values (CLV).
Four (2) Retail Data Science Case Studies:
Product Analytics (Exploratory Data Analysis Techniques
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 Businesses by taking this course!
Who this course is for
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
Homepage

https://www.udemy.com/course/data-science-in-marketing-an-introduction-course-2021/

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