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Đề cương khóa học
Module 1
Introduction to Data Science & Applications in Marketing
- Analytics Overview: Type of analytics- Predictive, Prescriptive, Inferential
- Analytics Practice in Marketing
- Use of Big Data and Different Technologies - Introduction
Module 2
Marketing in a Digital World
- Introduction to Digital Marketing
- Online Advertising - Introduction
- Search Engine Optimization (SEO) – Google Case Study
- Social Media Marketing: Tips and Secret – Example of Facebook, Twitter
Module 3
Exploratory Data Analysis & Statistical Modeling
- Data Presentation and Visualization – Understanding the Business data using Histogram, Pie-chart, Bar Chart, Scatter Diagram – Fast inference – Using Python
- Basic Statistical Modeling – Trend, Seasonality, Clustering, Classifications (Only basics, different Algorithm and usage, not any detail) – Ready code in Python
- Market Basket Analysis (MBA) – Case Study using Association rules, Support, Confidence, Lift
Module 4
Marketing Analytics I
- Introduction to Marketing Process – Case Study
- Utilizing Data to Improve Marketing Strategy
- Measuring Brand Assets, Snapple and Brand Value – Brand Positioning
- Text Mining for Marketing – Basics of Text mining – Case Study for Social Media Marketing
Module 5
Marketing Analytics II
- Customer Lifetime Value (CLV) with Calculation – Case Study of CLV for business decisions
- Measuring Case and Effect through Experiments – Case Study
- Calculating Projected Lift
- Data Science in Online Advertising – Click-rate Conversion, Website Analytics
Module 6
Regression Basics
- What Regression Reveals and basic Statistics (not much details of Mathematics)
- Interpreting Regression Results – With Case Study using Python
- Understanding Log-Log Models – With Case study using Python
- Marketing Mix Models – Case study using Python
Module 7
Classification and Clustering
- Basics of Classification and Clustering – Usage; Mention of Algorithms
- Interpreting the Results – Python Programs with Outputs
- Customer Targeting using Classification and Clustering – Case Study
- Business Strategy Improvement – Example of Email Marketing, Promotions
- Need of Big Data Technologies in Classification and Clustering
Module 8
Time Series Analysis
- Trend and Seasonality – Using Python driven Case Study - Visualizations
- Different Time Series Techniques – AR and MA
- Time Series Models – ARMA, ARIMA, ARIMAX (Usage and Examples with Python) – Case Study
- Time Series Prediction for Marketing Campaign
Module 9
Recommendation Engine
- Personalization and Business Strategy
- Different Types of Personalized Recommendations – Collaborative, Content based
- Different Algorithms for Recommendation Engine – User driven, Item Driven, Hybrid, Matrix Factorization (Only mention and usage of the algorithms without Mathematical details)
- Recommendation Metrics for Incremental Revenue – Detailed Case Study
Module 10
Maximizing Sales using Data Science
- Basics of Optimization Technique and its Uses
- Inventory Optimization – Case Study
- Increasing ROI using Data Science
- Lean Analytics – Startup Accelerator
Module 11
Data Science in Pricing & Promotion I
- Pricing – The Science of Profitable Growth
- Demand Forecasting Techniques - Model and estimate the structure of price-response demand curves
- Pricing Decision – How to Optimize Pricing Decision – Case Study Using Python
- Promotion Analytics – Baseline Calculation and Trade Promotion Model
- Using Promotion for Better Strategy - Sales Model Specification – Multiplicative Model
Module 12
Data Science in Pricing and Promotion II
- Revenue Management - How to manage perishable resources with multiple market segments
- Product Bundling – Fast and Slow Moving Products – Case Study with Python
- Pricing of Perishable Goods and Services - Airline & Hotel Pricing – Mention of Stochastic Models
- Promotion Metrics – Traditional and Social
Requirements
There are no specific requirements needed to attend this course.
21 Hours
Testimonials (5)
Hiểu rõ hơn về dữ liệu lớn
Shaune Dennis - Vodacom
Course - Big Data Business Intelligence for Telecom and Communication Service Providers
Machine Translated
Giảng viên rất linh hoạt và thực sự khích lệ tôi tham gia khóa học.
Grace Goh - DBS Bank Ltd
Course - Python in Data Science
Machine Translated
Học máy, python, thao tác dữ liệu
Siphelo Mapolisa - University Of South Africa
Course - Data Science: Analysis and Presentation
Machine Translated
Bài trình bày kiến thức về chủ đề thời gian
Aly Saleh - FAB banak Egypt
Course - Introduction to Data Science and AI (using Python)
Machine Translated
Rất tuyệt khi khóa học được定制以我在课程前问卷中突出的关键领域。这真的有助于解决我对主题的疑问，并与我的学习目标保持一致。 I apologize for the mistake in the previous response. Here is the correct translation: Rất tuyệt khi khóa học được tùy chỉnh cho các khu vực quan trọng mà tôi đã đánh dấu trong bảng câu hỏi trước khóa học. Điều này thực sự giúp giải quyết những câu hỏi của tôi về nội dung môn học và đồng hành cùng mục tiêu học tập của tôi.
Winnie Chan - Statistics Canada
Course - Jupyter for Data Science Teams
Machine Translated
