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Online
Python for Data Analysis
Course summary
Duration: 2 consecutive days the 1st displays as the course date.
You would learn to manipulate large and varied datasets by getting hands-on practical experience working on real-life data problems on anonymized data sets. You would gain working knowledge of the most commonly used Python modules for data scientists.
We concentrate onhandling files Numpy (‘Numerical Python') SciPy (used for scientific and technical computing ) Pandas (data analysis library) and Matplotlib.
The course is useful for professionals who anyone who use data as part of their work and who need to draw analysis from the data. It is best to already have an understanding of programming. We would issue pre-course work for beginners.
Virtual Classroom: You would need internet connection with audio download anaconda.com.
Laptops: Bring your own or arrange to use ours. ( for now virtual attendance only )
Course Outlines:
Day 1
Session 1: Brief Revision of Python Basics.
Session 2: Data Structures Data and Files
Lists Tuples Sets.
Dictionaries and Nested dictionaries Dict Comprehensions.
CSV files. Reading and writing Csv Files. The CSV module.
Txt Files. Bytes and Unicode with files.
Json Files.
Exception Handling.
Linking with SQL Database Insert Tables Insert Update and delete records. Select queries traverse and display query results.
Interacting with Api's
Session 3: Numpy: The Python NumPy Module: Working with arrays array manipulation string math arithmetic and statistical functions.
Session 4: Pandas:
Pandas Series Date/ Time Functionality. Time series.
Pandas Dataframes Indexing Sorting Filter Slicing Iteration Functions Aggregation.
Day 2
Session 5: Data Cleaning and preparation
Random Sampling. Finding and filtering Missing data Remove Duplicates String objects Regex. Replacing values. Transforming data using function and mapping Renaming Axis Indexes Discretization and Binning.
Session 6: Data Wrangling
Hierarchical Indexing Reorder Sorting Stastitics Dataframe Joins Merging Concatenation Overlap. Reshaping and pivoting.
Session 7: Scipy
Introduction and overview of SciPy functions.
Session 6: Plotting and Visualization
Introducing to plotting data with MatPlotLib
Tickets for Data Analysis with Python, Pandas and Numpy. Webinar, Virtual Classroom. can be booked here.
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