Name: Marketing Analyst: Learn Sales Forecasting & Market Analysis
Rating: 4.4 (24 reviews)

Udemy Business

Teach on Udemy

Turn what you know into an opportunity and reach millions around the world.

Learn More

Your cart is empty.

Keep shopping

Created byTemotec Academy

Last updated 5/2026

English

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

9 sections • 91 lectures • 9h 32m total length

Introduction to Pandas for Marketing Data Analysis6:29
Assignment Solution0:58
Exploratory Data Analysis10:57
Assignment Solution1:02
Marketing Metrics6:03
Customer Segmentation Overviewing7:45
Visualization of the Marketing Campaigns using Python7:44
Automating Marketing Analysis in Python5:14
Identifying Marketing Data Inconsistencies using Python5:17
Resolving Inconsistencies in Marketing Data using Python5:38
Designing an AB Test for Marketing Data in Python.5:56
Designing an AB Test for Marketing Data with Segmentation in Python.7:01
Calculating Lift & Significance Testing for Marketing Data in Python.5:45
ALL Codes & Data used in This Module Attached to this lecture Resource Section.0:02

String Functions in Python Part 12:53
String Functions in Python Part 21:38
String Functions in Python Part 32:06
String Functions in Python Part 41:18
String Functions in Python Part 51:32
Lists.4:21
Tuples.2:29
Sets.5:32
Dictionaries.4:48
Control Flow IF.4:13
For Loop Part 1.2:44
For Loop Part 2.5:01
While Loop Part 1.5:14
While Loop Part 2.3:34
While Loop Best Practices.2:48
Introduction to Functions in Python.2:18
Functions in Python and Arguments.2:16
Function Tips & Tricks Recursion.2:15
Function Tips & Tricks Functions Decorators and Higher Order Functions.2:24
Functions Tips & Tricks Lambda Functions.2:00
Function Tips & Tricks Functions Caching & Memoization.3:44
Error Handling in Python.1:06
Files and Modules in Python.6:52

Datasets used in this Section.0:03
Introduction to Time Series Analysis.5:33
Creating, Converting Datetimes from Strings & Manipulating Datetime Data.4:27
Accessing Datetime Attributes, Comparing Datetimes, and Making Relative Datetime5:07
Understanding Time Series Growth Rates & Comparing Stock Prices with a Benchmark4:42
Changing Time Series Frequency By Up-Sampling & Interpolation.4:06
Changing Time Series Frequency & Down-Sampling.3:46
Window Functions in Time Series Analysis.6:39
Stock Price Series Analysis with Lags.3:32

Introduction to ML.6:59
The Supervised Learning Workflow.7:08
Measuring Model Performance.7:44
Introduction to Regression.5:19
The Basics of Linear regression & Regression Performance.5:59
Cross-validation for R-squared & Analyzing Cross-Validation Metrics.6:47
Regularized Regression.7:34
The k-Nearest Neighbors Classification Supervised ML Learning Algorithms.6:41
How Good is your Model? and How to use Random Forest Classifier?6:16
Logistic Regression and the ROC Curve.13:36
Hyperparameter Tuning with GridSearchCV & RandomizedSearchCV.9:06
Preprocessing Data and Creating Dummy Variable for Categorical Data Variables.11:00
Handling Missing Data And Creating The ML Pipeline.8:45
Centering and Scaling Techniques in ML Supervised Learning Algorithms.11:08
Evaluating Multiple Models with Examples.10:41
Comparing Models.12:17
Logistic Regression and Support Vector Machines for Text Classification.10:12
Linear Classifier & Decision Boundaries.9:35
Linear Classifiers & The Coefficients.7:37
What is The Loss Function?8:19
Loss Function Diagrams.15:01
Logistic Regression Regularization & Identifying Negative Reviews.15:01
Logistic Regression and Probabilities.10:58
Multi-class Logistic Regression.16:29
Unsupervised Learning The Fundamentals of Clustering.9:52
Clustering Evaluation & Optimization.7:53
Transforming Features for Better Clustering & Clustering Stocks Using KMeans.12:01
Hierarchical Clustering & Visualizing Hierarchies.7:55
t-SNE for 2-dimensional maps.8:31
Visualizing The PCA Transformation & Calculating Cumulative Explained Variance.8:58
Dimension Reduction with PCA.6:59
Dimension Reduction with PCA and Non-negative Matrix Factorization (NMF).14:40
Review All Course Python ML Code From Here.0:03

Requirements

Basic understanding of marketing concepts.
Familiarity with Python programming (e.g., variables, loops).
Access to a computer with Python installed.
No advanced data analysis experience required, as foundational concepts will be covered.

Description

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.

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.

Who this course is for:

This course is ideal for marketing professionals, business analysts.
Aspiring data-driven marketers who want to expand their analytical skills.
Whether you’re new to Python or looking to enhance your marketing insights with data.
This course will provide the knowledge and practical experience needed to make informed marketing decisions.

What you'll learn

Explore related topics

Course content

Marketing Data Analysis Overview.14 lectures • 1hr 16min

Python Refresher.23 lectures • 1hr 13min

Accessing, Manipulating & Filtering DataFrames Lectures Refresher.7 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

Marketing Analysis Role Play.1 lecture0

Bonus1 lecture • 3min

Requirements

Description

Who this course is for: