Mastering AI-Driven Sales Forecasting, Market Analysis, customer Segmentation, Predictive Analytics ML models in Python.
Welcome to Comprehensive Marketing Data Analysis and YouTube Analytics using Python, an in-depth, hands-on course designed to equip you with the practical skills needed to leverage Python for marketing data analysis and analytics across various platforms. This course covers a wide range of topics, from YouTube Analytics and Marketing Data Analysis to more advanced case studies using machine learning in marketing, customer segmentation, churn detection, and AB testing. Whether you’re a marketing professional, data analyst, or a Python enthusiast, this course will take you from beginner to advanced levels, empowering you to make data-driven decisions in marketing.
What you’ll learn
- Analyze Market Trends with Python Identify and analyze key market trends and consumer behaviors using Python tools..
- Develop Sales Forecasting Models Build predictive models to forecast sales and understand their impact on marketing strategies..
- Leverage Data for Market Insights Extract, manipulate, and visualize data to generate valuable market insights and recommendations..
- Apply Statistical Methods to Market Analysis Utilize statistical techniques to assess market potential and optimize marketing efforts..
Course Content
- Marketing Data Analysis Overview. –> 14 lectures • 1hr 16min.
- Python Refresher. –> 23 lectures • 1hr 13min.
- Accessing, Manipulating & Filtering DataFrames Lectures Refresher. –> 6 lectures • 41min.
- Data Visualization Refresher. –> 4 lectures • 25min.
- Time Series Analysis of Financial Data using Python. –> 9 lectures • 38min.
- ML Refresher. –> 33 lectures • 5hr 7min.
- ML Marketing Project: Building Recommender System using NMF. –> 1 lecture • 10min.
Requirements
Welcome to Comprehensive Marketing Data Analysis and YouTube Analytics using Python, an in-depth, hands-on course designed to equip you with the practical skills needed to leverage Python for marketing data analysis and analytics across various platforms. This course covers a wide range of topics, from YouTube Analytics and Marketing Data Analysis to more advanced case studies using machine learning in marketing, customer segmentation, churn detection, and AB testing. Whether you’re a marketing professional, data analyst, or a Python enthusiast, this course will take you from beginner to advanced levels, empowering you to make data-driven decisions in marketing.
Part 1: Marketing Data Analysis with Pandas and Python
Building on the foundations of YouTube Analytics, this part focuses on general marketing data analysis using Python, emphasizing practical techniques for data-driven marketing strategies.
- Data Exploration and Preprocessing:
Get hands-on with Pandas to clean, preprocess, and explore marketing datasets. Understand how to handle inconsistencies and anomalies to ensure reliable insights. - Customer Segmentation:
Learn to segment customers effectively, identifying unique profiles and tailoring marketing efforts accordingly. Utilize clustering algorithms to classify customers based on behaviors and preferences. - Sales Forecasting and Time Series Analysis:
Gain expertise in time series analysis for sales forecasting. Apply techniques like moving averages and exponential smoothing to project future trends. - Automating Marketing Analysis:
Develop automation skills to streamline data analysis workflows, save time, and scale your analytical capabilities with Python scripts that generate consistent and reliable insights. - Marketing Metrics and Campaign Visualization:
Dive into key marketing metrics such as CTR, conversion rates, and customer lifetime value. Visualize campaigns using Python libraries to create data-driven strategies.
Part 2: YouTube Analytics using Python
YouTube is a treasure trove of marketing insights. This section shows you how to collect, process, and analyze YouTube data with Python.
- Introduction to YouTube Analytics:
Get an overview of YouTube Analytics and how to extract and interpret data using Python. Understand the significance of various metrics for marketing strategies. - Data Collection and Processing:
Learn how to use the YouTube API for data extraction, covering setup, authentication, and data merging techniques. - Text Processing and Sentiment Analysis:
Analyze video comments to gauge audience sentiment using NLP techniques in Python. - Network Analysis:
Explore content creator-audience relationships through network analysis, measuring network metrics, and visualizing connections. - Geospatial Data and Mapping:
Incorporate geographical data using JSON and create maps to plot user distributions and demographics.
Part 3: Banking Data Analysis and Case Study
In this section, you’ll work on a case study using the Kaggle banking dataset, focusing on customer demographics, transaction data, and marketing campaign responses to develop targeted marketing strategies for the banking sector.
Part 4: Machine Learning in Marketing
Delve into machine learning applications in marketing, including supervised and unsupervised learning, predictive modeling, and budget optimization.
Part 5: Customer Segmentation
Learn advanced customer segmentation techniques to refine marketing strategies, focusing on clustering and customer behavior analysis.
Part 6: Churn Detection
Apply machine learning to detect customer churn, understanding how to predict and mitigate potential losses in customer retention.
Part 7: Customer Analytics and AB Testing
Master customer analytics with the Google Analytics Customer Revenue Prediction dataset, and gain proficiency in AB testing to measure marketing impact, calculate lift, and perform significance testing with Python.
Learning Outcomes:
- Collect, process, and analyze marketing data and YouTube insights using Python.
- Build and evaluate predictive models to forecast sales, detect churn, and segment customers.
- Visualize complex metrics and create maps for geographical insights.
- Perform sentiment analysis, network analysis, and AB testing for data-driven marketing decisions.
Requirements:
- Basic Python programming knowledge is helpful but not required.
- No prior marketing or data analysis experience needed; all concepts are introduced from scratch.
Intended Audience: This course is ideal for aspiring data analysts, marketers, and professionals who want to enhance their Python skills in a marketing context. It offers insights into YouTube Analytics, customer segmentation, and machine learning for marketing, empowering you to excel in the data-driven marketing field.