Use Python to solve problems in Retail, Marketing, Product Recommendation, Customer Clustering

Pandas

JSON

Handling missing data

Decision Tree

Collaborative filtering

Data Cleanup

Linear Regression Model

Evaluating model Performance

K-means

Analyzing Customer life value

Product analytics

Product Recommendation system

Interpreting customer snts

Analyzing and visualizing KPI

Solid Python knowledge is important

Welcome to the Data Science in Marketing: An Introduction Course 2021

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 bners 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 Matplotlib.

Machine Learning Theory - Linear Regressions, Decision Trees, and Model Assessment.

Data Science in Marketing - Modelling Engagement Rates.

Data Science in Retail - Customer Sntation, Life 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 Life 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, , 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 stratally will win."

With Data Scientist salaries creeping up higher and higher, this course seeks to take you from a bner and turn you into a Data Scientist capable of solving challeg real-world problems.

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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!

Bners 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 Eeers 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




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