Python for Data Analysis 2-Day Course, London
Python for Data Analysis
Duration: 2 consecutive days, the 1st displays as the course date.
In this course, we cover Python packages that are commonly used for data analysis. These packages include handling files, Numpy (‘Numerical Python'), SciPy (used for scientific and technical computing ) and Pandas (data analysis library). Learn to use these powerful extensions available in Python. Y
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.
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, although we would issue pre-course work for beginners.
Looping, OO Principles.
Reading and writing CSV files into a Python program: The CSV module,
Txt Files. Json Files. Identifying and fixing errors in datasets. Linking with API's.
Linking with SQL Database, Create a database, drop a database, Insert Tables, Alter Table, Drop Table,
Insert, Update and delete records. Select queries, traverse and display query results.
Data Structures, Lists, Sets, Tuples, Dictionaries.
Identifying and fixing errors in datasets.
The Python Pandas: Dataframes, Series, Indexing, Sorting, Filter, Slicing, Iteration, Functions, Aggregation, Merge/join, Concatenation, Date/ Time Functionality.
The Python NumPy Module: Working with arrays, array manipulation, string, math, arithmetic and statistical functions.
Introduction and overview of SciPy functions.
Plotting data with MatPlotLib