This course takes a deep dive into the statistical foundation upon which Marketing Analytics is built. The first part of this course is all about getting a thorough understanding of a dataset and gaining insight into what the data actually means. The second part of this course goes into sampling and how to ask specific questions about your data. Finally, the third part is about answering those questions with analyses. Many of the mistakes made by Marketing Analysts today are caused by not understanding the concepts behind the analytics they run, which causes them to run the wrong test or misinterpret the results. This course is specifically designed to give you the background you need to understand what you are doing and why you are doing it on a practical level. By the end of this course you will be able to: • Understand the concept of dependent and independent variables • Identify variables to test • Understand the Null Hypothesis, P-Values, and their role in testing hypotheses • Formulate a hypothesis and align hypotheses with business goals • Identify actions based on hypothesis validation/invalidation • Explain Descriptive Statistics (mean, median, standard deviation, distribution) and their use cases • Understand basic concepts from Inferential Statistics • Explain the different levels of analytics (descriptive, predictive, prescriptive) in the context of marketing • Create basic statistical models for regression using data • Create time-series forecasts using historical data and basic statistical models • Understand the basic assumptions, use cases, and limitations of Linear Regression • Fit a linear regression model to a dataset and interpret the output using Tableau and statsmodels • Explain the difference between linear and multivariate regression • Run a segmentation (cluster) analysis • Describe the difference between observational methods and experiments This course is designed for people who want to learn the basics of descriptive and inferential statistics and analytics in marketing. Learners don't need marketing or data analysis experience, but should have basic internet navigation skills and be eager to participate. Ideally learners have already completed course 1 (Marketing Analytics Foundation) and course 2 (Introduction to Data Analytics) in this program.

This course is designed for business professionals that wish to identify basic concepts that make up machine learning, test model hypothesis using a design of experiments and train, tune and evaluate models using algorithms that solve classification, regression and forecasting, and clustering problems. To be successful in this course a learner should have a background in computing technology, including some aptitude in computer programming.

In this 1-hour long project-based course, you will learn how to pivot data into wide and long format, split and combine cells and columns, handling missing values, select groups of observations and variables, and join data from different tables. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. Understanding the importance and challenges of learning agents that make decisions is of vital importance today, with more and more companies interested in interactive agents and intelligent decision-making. This course introduces you to the fundamentals of Reinforcement Learning. When you finish this course, you will: - Formalize problems as Markov Decision Processes - Understand basic exploration methods and the exploration/exploitation tradeoff - Understand value functions, as a general-purpose tool for optimal decision-making - Know how to implement dynamic programming as an efficient solution approach to an industrial control problem This course teaches you the key concepts of Reinforcement Learning, underlying classic and modern algorithms in RL. After completing this course, you will be able to start using RL for real problems, where you have or can specify the MDP. This is the first course of the Reinforcement Learning Specialization.

We consume forecasted data regularly in our personal and business lives, covering everything from the weather to projected investment returns. At work we use forecasted data for a multitude of purposes including developing strategies, budgets, and to provide the right amount of resources to meet demand. In this course, you will get your feet wet with statistical forecasting by designing, creating, and interpreting a growth forecast. You will do this as we work side-by-side in the free-to-use software Google Sheets. By the end of this course, you will understand use cases for conducting forecasts in your workplace and be able to confidently conduct a growth forecast in any spreadsheet software. You will also understand when it is necessary to refine a model to improve the accuracy of forecasted projections. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

In this hands-on project, we will train a deep learning model to predict the type of food and then fine tune the model to improve its performance. This project could be practically applied in food industry to detect the type and quality of food. In this 2-hours long project-based course, you will be able to: - Understand the theory and intuition behind Convolutional Neural Networks (CNNs). - Understand the theory and intuition behind transfer learning. - Import Key libraries, dataset and visualize images. - Perform data augmentation. - Build a Deep Learning Model using Pre-Trained InceptionResnetV2. - Compile and fit Deep Learning model to training data. - Assess the performance of trained CNN and ensure its generalization using various KPIs.

Hello everyone and welcome to this new hands-on project on data visualization and storytelling in python. In this project, we will leverage 3 powerful libraries known as Seaborn, Matplotlib and Plotly to visualize data in an interactive way. This project is practical and directly applicable to many industries. You can add this project to your portfolio of projects which is essential for your next job interview.

In this 2-hour long project-based course, you will build and train a convolutional neural network (CNN) in Keras from scratch to recognize facial expressions. The data consists of 48x48 pixel grayscale images of faces. The objective is to classify each face based on the emotion shown in the facial expression into one of seven categories (0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral). You will use OpenCV to automatically detect faces in images and draw bounding boxes around them. Once you have trained, saved, and exported the CNN, you will directly serve the trained model to a web interface and perform real-time facial expression recognition on video and image data. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and Keras pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

This is a self-paced lab that takes place in the Google Cloud console. In this lab you will build an end to end machine learning solution using Tensorflow + AI Platform and leverage the cloud for distributed training and online prediction.

Please note: You will need an AWS account to complete this course. Your AWS account will be charged as per your usage. Please make sure that you are able to access Sagemaker within your AWS account. If your AWS account is new, you may need to ask AWS support for access to certain resources. You should be familiar with python programming, and AWS before starting this hands on project. We use a Sagemaker P type instance in this project for training the model, and if you don't have access to this instance type, please contact AWS support and request access. In this 2-hour long project-based course, you will how to train and deploy a Recommendation System using AWS Sagemaker. We will go through the detailed step by step process of training a recommendation system on the Amazon's Electronics dataset. We will be using a Notebook Instance to build our training model. You will learn how to use Apache's MXNET Deep Learning Model on the AWS Sagemaker platform. Since this is a practical, project-based course, we will not dive in the theory behind recommendation systems, but will focus purely on training and deploying a model with AWS Sagemaker. You will also need to have some experience with Amazon Web Services (AWS) and knowledge of how deep learning frameworks work. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.