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Customer Analytics in Python
5 hours
6 exercises
3 assignments
0 quizzes
55 downloadablesCustomer Analytics in Python is where marketing and data science meet. Data science and Marketing are two of the key driving forces that help companies create value and stay on top in today’s fast-paced economy. This course is packed with knowledge, and includes sections on customer and purchase analytics, as well as a deep-learning model, all implemented in Python.
Explore CourseProgram Contents
1. Data Science Fundamentals
In the first module of the training you will create a strong foundation for your data science career. You will understand the core principles of probability, statistics, and mathematics necessary for building machine and deep learning models further in the program. You will start working with data and learn how to visually present the results of your analyses.
Intro to Data and Data Science
Introducing you to the field of data science and building your understanding of the key data science terms and processes.
2 hours
10 exercises
Introduction to Microsoft Excel
Giving you the skills to crunch numbers in Excel; even in data science, smaller datasets are faster preprocessed in Excel than Python, R or SQL.
4 hours 27 exercises
Advanced Microsoft Excel
Teaching you how to setup pivot tables and analyze data with filters and calculated fields, as well as advanced Excel tools like function keys, multiple sheets, and circular references.
2 hours 18 exercises
Probability
Teaching you the probability theory necessary to think like a data scientist. You will learn about expected values, combinatorics, Bayesian notation, and probability distributions.
4 hours 36 exercises
Statistics
Covering descriptive & inferential statistics and hypothesis testing techniques and exercises; statistics puts the “scientist” in data scientist.
3 hours 96 exercises
Mathematics
Teaching you the linear algebra you need for the true data science expertise of developing powerful machine learning models.
1 hours 12 exercises
Tableau
Teaching you how to tell compelling stories with data by building accessible visualizations and dashboards. Present complicated results to non-technical audiences.
2 hours 10 exercises
Introduction to Power BI
Giving you the skills to create stunning dashboards using all Power BI features. Complete with practical tasks and real-life projects.
8 hours 0 exercises
2. Programming for Data Science
In the second module of the training, you will focus on developing a versatile programming skillset. You will start working with relational databases and using essential SQL queries to preprocess data, as well as coding in Python and leveraging popular Python libraries, like numpy, and matplotlib. You will also learn the best ways to manipulate and visualize data in R.
SQL
Equipping you with the essential data science skills to effectively manage relational databases by extracting, transforming, and loading your data.
8 hours 63 exercises
SQL + Tableau
Combining two expertises for a powerful integration: extract information from multiple databases using Tableau while coding in SQL.
1 hours 10 exercises
Introduction to Python
Laying the foundations of programming in Python to prepare you for deploying machine and deep learning algorithms later in the training.
2 hours 40 exercises
Introduction to R Programming
Providing you with the skills to manipulate, analyze, and visualize data with the best programming language for statistical analysis for data science.
6 hours 36 exercises
3. Machine and Deep Learning
Expanding on your knowledge from Modules 1 and 2, in Module 3 you will start applying advanced statistical methods to do predictive analytics. You will learn how to use sklearn to build complete linear and logistic regression models from scratch, and how to cluster unlabeled datasets with k-means. You will progress to building deep learning models with the Keras library in TensorFlow 2.0, optimizing your neural network with backpropagation, and fine-tuning your model.
Machine Learning in Python
Sharpening your predictive modelling skills to differentiate you as a data scientist instead of a data analyst; covering regressions, classifications, and clustering.
4 hours 25 exercises
Deep Learning with TensorFlow
Building on everything covered in the program to teach you how to develop, implement, and deploy complicated machine and deep learning algorithms (with TensorFlow).
5 hours 28 exercises
Deep Learning with TensorFlow 2.0
Helping you learn and practice advanced deep learning techniques with TensorFlow 2.0 code and syntax. Complete with new TF2 exercises and projects.
5 hours 28 exercises
SQL + Tableau + Python
Building on you SQL + Tableau expertise, this course provides you with the skills to also do complex pre-processing and agile real-time machine learning.
4 hours 10 exercises4. Advanced Specialization
Please be advised that these are advanced matter courses and a solid understanding of Python programming and statistical modeling is expected. Modules 1, 2, and 3 address these skills.
Credit Risk Modeling in Python
Teaching you the programming behind how banks decide who should get a loan. You will learn risk modeling theory and advance your Python modeling skills.
6 hours 52 exercises
Time Series Analysis in Python
Introducing you to the world of time series and exploring how to utilize Python in analyzing and modeling such data. We will also discuss volatility and making forecasts about the future.
5 hours 17 exercises
Customer Analytics in Python
Introducing you to Customer Analytics with Python. You will learn the fundamentals of Marketing, as well as acquire practical skills in how to analyze customer data and learn to predict the purchase behaviour of clients.
5 hours 6 exercisesReady to start?
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