Python is excellent for working with large datasets.
This course covers popular libraries designed for Python Data Analytics, like numPy, Pandas, sciPy and Matplotlib. <
These libraries and library functions clean, reshape, manipulate and summarise data.
Duration: 2 Consecutive Days, the first of which shows as the booking date.
Our Style: This Python Data Course course is hands-on, practical.
Group size: Max 4 people, so we have time for personalised attention.
Brief Revision of Python Basics.
Lists, Tuples, Sets, Dictionaries, List and Dict Comprehensions
Reading and writing CSV files into a Python program: 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.
Inteacting with Api's
The Python NumPy Module: Working with arrays, array manipulation, string, math, arithmetic and statistical functions.
Dataframes, Series, Indexing, Sorting, Filter, Slicing, Iteration, Functions, Aggregation, Merge/join, Concatenation,
Date/ Time Functionality. Time series.
Finding and filtering Missing data, Remove Duplicates, Transforing data using fundction and mapping, Replacing values, Renaming Axis Indexes, Discretization and Binning, Random Sampling. String objects, Regex.
Hierarchical Indexing, Reorder, Sorting, Stastitics, Dataframe Joins, Merging, Concatenation, Overlap. Reshaping and pivoting.
Introduction and overview of SciPy.
Introducing to plotting data with MatPlotLib
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