Overview

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This Specialization is intended for business professionals and analysts seeking to develop practical Python skills for real-world data work. Through three courses, you'll cover core Python data science tools (built-in functions, NumPy, SciPy, pandas, matplotlib, and seaborn), advanced pandas techniques for cleaning, combining, and reshaping messy datasets, and openpyxl for building and automating polished Excel reports. By the end, you'll be able to inspect, clean, analyze, and visualize real business data, and turn manual reporting work into fast, repeatable Python workflows.

Syllabus

Course 1: Python Functions for Data Science
Course 2: Using Python With Excel
Course 3: Advanced Pandas

Courses

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Most Python users pick up just enough pandas to load a CSV and glance at a few rows, then stop there, leaving most of the library's real power untouched. Data scientist Brett Vanderblock built this course to close that gap and take you well past the basics of DataFrame handling. It moves from foundational setup into the cleaning, combining, and scaling techniques that real, messy datasets actually demand. You'll set up and navigate DataFrames, clean and convert messy real-world values, combine and reshape data from multiple sources, and visualize and summarize what you find. You'll also work with tools that extend pandas beyond its core capabilities, including automated data profiling, geospatial analysis, and frameworks built for datasets too large for pandas alone. By the end of this course, you'll be able to take a messy, multi-source dataset and turn it into a clean, well-organized foundation ready for analysis, visualization, or a decision-maker's next move.
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Data science in Python comes down to having the right functions ready at the right moment, not memorizing the entire language from the ground up. This course covers the tools that data analysts and data scientists rely on daily: Python's own built-in functions, NumPy, SciPy, pandas, matplotlib, and seaborn. Rather than treating each library as an abstract topic, you'll build a single, practical toolkit one skill at a time. You'll inspect, aggregate, sort, and filter data using nothing but Python's built-in functions, then move into NumPy to create and transform numeric arrays for fast, large-scale calculations. From there, you'll use SciPy to solve matrix-based problems and test whether a difference in your data is statistically meaningful. You'll bring that same data into pandas, where you'll build, clean, and reshape real tabular datasets, combining and grouping information exactly the way a real analysis project demands. Finally, you'll turn your findings into clear, presentation-ready charts with matplotlib and seaborn. By the end of this course, you'll be able to inspect, transform, analyze, and visualize real datasets using Python's core data science toolkit, ready to apply immediately to your own data.
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Excel can be a blessing and a curse. Working with small amounts of data and simple operations? Excel is your go-to tool. However, Excel's limitations show themselves whenever you need to work with large amounts of data and perform the types of complex operations necessary to compile key analytics, and pushing a spreadsheet past its comfort zone usually means slower performance. Enter Python, rapidly becoming one of the world's most popular programming languages. As a powerful tool that can speed up your data operations, Python, in conjunction with Excel, lets you inspect, clean, and filter data, along with the ability to create memorable and dynamic reports that deliver crucial business insights. Through the pandas and openpyxl libraries, you'll load and reshape data, build and format workbooks directly through code, and automate reporting work that used to take hours by hand. Whether it's converting data, creating visually striking graphs and charts, or shaping large amounts of data in any number of useful ways, you'll find a new-found ease and efficiency with Python that would be otherwise unattainable in Excel alone. By the end of this course, you'll combine data from multiple sources and build fully automated, visually polished Excel reports.