Data Analysis with Python, Pandas and Numpy. Webinar, Virtual Classroom., Online, Thursday, 19. July 2018

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 

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

Thursday, 19. July 2018, Online, Data Analysis with Python, Pandas and Numpy. Webinar, Virtual Classroom.

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