
Outline of the course structure covers KPI visualizations, engagement to converge on conversion, product analytics, a product recommender system, and customer lifetime value, with coding checks and projects.
Explore how data models manage marketing data across structured, semi-structured, and unstructured formats, and how Python and pandas transform inputs into reliable, fully structured analytics.
Import pandas and numpy, create and merge dataframes, and handle missing values by replacing them with the mean to produce a complete dataset.
Explore descriptive analyses for marketing progress and performance. Apply kpi, sales revenue metrics, campaign attribution, cpa, and digital marketing channel metrics, plus regression and machine learning for explanatory insights.
Learn how to encode marital status and housing and loan variables by creating new columns, mapping yes/no to 1/0, and applying a function to derive married, single, and divorce categories.
Implement product analytics by loading a dataset, cleaning canceled orders, and analyzing monthly orders and revenue with pandas-powered visualizations of quantity distribution.
Analyze repeat customers by tracking monthly repeat purchases, attributing revenue to repeats, and cleaning and grouping invoice data to count customers with multiple orders per month.
Analyze monthly marketing data to quantify repeat customers, showing that 20–30 percent are repeat customers and that repeat revenue accounts for 40–50 percent of total monthly revenue.
Clean and prepare a retail dataset in pandas by removing negative quantities and invalid orders, handling missing data, computing per-order total sales, and grouping by customer id and invoice number.
Prepare data for a predictive model to forecast the last three months of customer value. Aggregate data by customer over three-month periods, encode dates, and train a linear regression model.
Explains data preparation for marketing analytics, training models on tabular data, building a pivot_table with customer_id as the index, and forecasting the next three months' sales for customers.
Learn to build and evaluate a linear regression model with train/test split to predict next three months' customer value from prior features, and inform targeted marketing strategies.
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:
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 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.
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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 Marketing problems by taking this course!