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Module 1
Data Science Fundamentals
This module is optimized to help you develop a strong foundation for a data science career. It dives deep into 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.
01Advanced Microsoft Excel32 lessons
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02Data Analysis with Excel Pivot Tables19 lessons
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03Data Literacy58 lessons
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04Data Strategy81 lessons
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05How to Think Like a Data Scientist to Become One21 lessons
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06Introduction to Data and Data Science22 lessons
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07Introduction to Excel83 lessons
08Introduction to Tableau40 lessons
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09Mathematics12 lessons
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10Power BI76 lessons
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11Probability47 lessons
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12SQL for Data Science Interviews23 lessons
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13Starting a Career in Data Science: Project Portfolio, Resume, and Interview Process52 lessons
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14Statistics44 lessons
Module 2
Programming for Data Science
Here you will focus on developing a versatile programming skillset. You will acquire a thorough functional understanding of relational databases, using essential SQL queries to preprocess data, as well as coding and leveraging popular libraries in Python, like NumPy and matplotlib. You will also learn the best ways to manipulate and visualize data in R.
01Data Cleaning and Preprocessing with pandas27 lessons
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02Data Preprocessing with NumPy68 lessons
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03Dates and Times in Python23 lessons
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04Git and GitHub12 lessons
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05Introduction to Jupyter9 lessons
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06Introduction to Python41 lessons
07Introduction to R Programming87 lessons
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08Python Programmer Bootcamp128 lessons
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09SQL121 lessons
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10SQL + Tableau20 lessons
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11The Complete Data Visualization Course with Python, R, Tableau, and Excel100 lessons
Module 3
Machine & Deep Learning
Building on the foundations developed in the first 2 modules, Module 3 will teach you how to apply advanced statistical methods to execute 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 then 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.
01Convolutional Neural Networks with TensorFlow in Python48 lessons
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02Deep Learning with TensorFlow 283 lessons
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03Linear Algebra and Feature Selection32 lessons
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04Machine Learning in Excel70 lessons
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05Machine Learning in Python72 lessons
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06Machine Learning with Decision Trees and Random Forests20 lessons
07Machine Learning with K-Nearest Neighbors17 lessons
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08Machine Learning with Naïve Bayes14 lessons
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09Machine Learning with Ridge and Lasso Regression19 lessons
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10Machine Learning with Support Vector Machines15 lessons
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11SQL + Tableau + Python61 lessons
Module 4
Advanced Specialization
By now, you will have developed a solid understanding of Python programming and statistical modeling. The concluding module gives you the opportunity to mold your data science expertise according to a field of your choosing. From developing next-gen fintech products to helping retail giants boost profitability through customer analytics, you’ll be able to make a valuable contribution to a diverse industry spectrum.
01A/B Testing in Python27 lessons
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02AI Applications for Business Success27 lessons
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03Credit Risk Modeling in Python58 lessons
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04Customer Analytics in Python60 lessons
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05Data-Driven Business Growth38 lessons
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06Fashion Analytics with Tableau41 lessons
07Introduction to Business Analytics54 lessons
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08Product Management for AI & Data Science67 lessons
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09Python for Finance65 lessons
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10Time Series Analysis with Python89 lessons
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11Web Scraping and API Fundamentals in Python48 lessons
