This course introduces the core concepts of data analysis using Python, equipping learners with essential skills to perform data manipulation and analysis. Data analysis is a crucial skill in today's tech-driven world, and this course empowers professionals with the tools they need to extract insights from data.
Python Data Analysis
Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
3 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Install and use open source Python modules across different platforms
Master the fundamentals of NumPy for efficient data manipulation
Analyze and process data using pandas and other key Python libraries
Details to know
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Recently updated!
March 2026
Assessments
12 assignments
Taught in English
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There are 12 modules in this course
In this section, we install Python and core libraries on multiple OSs, implement NumPy arrays for numerical tasks, and use IPython for interactive sessions.
What's included
2 videos4 readings1 assignment
In this section, we explore NumPy arrays, their data types, and operations like indexing and shaping. Key concepts include array creation, manipulation, and efficient numerical computing techniques.
What's included
1 video6 readings1 assignment
In this section, we explore descriptive statistics, linear algebra operations, and random number generation using NumPy and SciPy for data analysis and modeling.
What's included
1 video6 readings1 assignment
In this section, we explore pandas installation, DataFrame data structures, and perform data querying, aggregation, and analysis.
What's included
1 video8 readings1 assignment
In this section, we explore writing CSV files with NumPy and pandas, analyzing binary formats like .npy and pickle, and storing data using PyTables and HDF5 for efficient data management.
What's included
1 video4 readings1 assignment
In this section, we explore data visualization techniques using matplotlib and pandas, focusing on basic plots and subpackage functionality.
What's included
1 video4 readings1 assignment
In this section, we explore time series analysis techniques including moving averages, autocorrelation, and Fourier analysis. These methods enable modeling and forecasting of sequential data using statistical and signal processing tools.
What's included
1 video5 readings1 assignment
In this section, we explore relational and NoSQL databases, focusing on SQLite3, SQLAlchemy, and PyMongo/MongoDB.
What's included
1 video5 readings1 assignment
In this section, we explore text analysis using NLTK, covering word frequency, sentiment analysis, classification, and visualization techniques for unstructured data.
What's included
1 video5 readings1 assignment
In this section, we explore scikit-learn for predictive analytics, focusing on logistic regression, preprocessing, and classification techniques to enable data-driven decision-making.
What's included
1 video6 readings1 assignment
In this section, we explore data exchange with MATLAB/Octave, Python integration with R and Java, and cloud deployment strategies.
What's included
1 video4 readings1 assignment
In this section, we explore profiling, multiprocessing, and optimization techniques to improve performance.
What's included
1 video5 readings1 assignment
Instructor
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Frequently asked questions
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.
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Financial aid available,