Data Analysis with Python & Pandas Online Course

Data Analysis with Python and Pandas Online Courses in Zimbabwe

 

2KO offers an online computer course called Data Analysis with Python and Pandas. Python programmers are some of the most sought-after employees in the tech world, and Python itself is fast becoming one of the most popular programming languages. 2KO Africa offers IT consulting, technical IT services and top-of-the-range IT training on multiple platforms. Our best-of-breed computer courses are presented as instructor led classes or as online internet-based elearning. One of the best applications of Python however is data analysis; which also happens to be something that employers can't get enough of. Gaining skills in one or the other is a guaranteed way to boost your employability - but put the two together and you'll be unstoppable!

 

Become and expert data analyser

Learn efficient python data analysis

Manipulate data sets quickly and easily

Master python data mining

Gain a skillset in Python that can be used for various other applications

 

Python Data Analytics made Simple
This course contains 51 lectures and 6 hours of content, specially created for those with an interest in data analysis, programming, or the Python programming language. Once you have Python installed and are familiar with the language, you'll be all set to go. The course begins with covering the fundamentals of Pandas (the library of data structures you'll be using) before delving into the most important functions you'll need for data analysis; creating and navigating data frames, indexing, visualising, and so on. Next, you'll get into the more intricate operations run in conjunction with Pandas including data manipulation, logical categorising, statistical functions and applications, and more. Missing data, combining data, working with databases, and advanced operations like resampling, correlation, mapping and buffering will also be covered. By the end of this course, you'll have not only have grasped the fundamental concepts of data analysis, but through using Python to analyse and manipulate your data, you'll have gained a highly specific and much in demand skill set that you can put to a variety of practical used for just about any business in the world.

 

Tools Used

Python: Python is a general purpose programming language with a focus on readability and concise code, making it a great language for new coders to learn. Learning Python gives a solid foundation for learning more advanced coding languages, and allows for a wide variety of applications.

Pandas: Pandas is a free, open source library that provides high-performance, easy to use data structures and data analysis tools for Python; specifically, numerical tables and time series. If your project involves lots of numerical data, Pandas is for you.

NumPy: Like Pandas, NumPy is another library of high level mathematical functions. The difference with NumPy however is that was specifically created as an extension to the Python programming language, intended to support large multi-dimensional arrays and matrices.

 

Curriculum

 

Introduction to the Course

Course Introduction

Getting pandas and fundamentals

Introduction to Pandas

Section introduction

Creating and Navigating a Dataframe

Slices, head and tail

Indexing

Visualizing The Data

Converting To Python List Or Pandas Series

Section Conclusion

 

IO Tools

Section introduction

Read Csv And To Csv

io operations

Read_hdf and to_hdf

Read Json And To Json

Read Pickle And To Pickle

Section Conclusion

 

Pandas Operations

Section introduction

Column Manipulation (Operatings on columns, creating new ones)

Column and Dataframe logical categorization

Statistical Functions Against Data

Moving and rolling statistics

Rolling apply

Section Outro

 

Handling for Missing Data / Outliers

Section Intro

drop na

Filling Forward And Backward Na

detecting outliers

Section Conclusion

 

Combining Dataframes

Section Introduction

Concatenation

Appending data frames

Merging dataframes

Joining dataframes

Section Conclusion

 

Advanced Operations

Section Introduction

Basic Sorting

Sorting by multiple rules

Resampling basics time and how (mean, sum etc)

Resampling to ohlc

Correlation and Covariance Part 1

Correlation and Covariance Part 2

Mapping custom functions

Graphing percent change of income groups

Buffering basics

Buffering Into And Out Of Hdf5

Section Conclusion

 

Working with Databases

Section Introduction

Writing to reading from database into a data frame

Resampling data and preparing graph

Finishing Manipulation And Graph

Section and course Conclusion

Certificate Exam Access