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Course Name: Quantitative Data Analysis and Visualization using Python Training Course

Special offer for group registration:
Number of participants Fees
2-5 $ 700
6 -10 $ 650
11-50 $ 500
> 50 $ 450
To register as a group, contact us through: E: trainings@vitalextralearning.com or give us a call on T: +254707053111
Course Date Duration Location Fee: Registrations
Date: 20/05/2024 - 24/05/2024 5 Days Pretoria, S/Africa $ 950 Click to register
Date: 17/06/2024 - 21/06/2024 5 Days Mombasa, Kenya $ 950 Click to register
Date: 01/07/2024 - 05/07/2024 5 Days Nairobi, Kenya $ 950 Click to register
Date: 29/07/2024 - 02/08/2024 5 Days Abuja, Nigeria $ 950 Click to register
Date: 05/08/2024 - 09/08/2024 5 Days Kigali,Rwanda $ 950 Click to register
Date: 26/08/2024 - 30/08/2024 5 Days Pretoria, South Africa $ 950 Click to register
Date: 02/09/2024 - 06/09/2024 5 Days Nairobi, Kenya $ 950 Click to register
Date: 23/09/2024 - 27/09/2024 5 Days Abuja, Nigeria $ 950 Click to register
Date: 30/09/2024 - 04/10/2024 5 Days Kigali, Rwanda $ 950 Click to register
Date: 04/11/2024 - 08/11/2024 5 Days Nairobi, Kenya $ 950 Click to register
Date: 25/11/2024 - 29/11/2024 5 Days Abuja, Nigeria $ 950 Click to register
Date: 09/12/2024 - 13/12/2024 5 Days Mombasa, Kenya $ 950 Click to register
Date: 09/12/2024 - 13/12/2024 5 Days Mombasa, Kenya $ 950 Click to register

Duration

5 Days

Our Contacts:

Email: info@ vitalextralearning.com

Telephone:
+234 (0) 8038 066705,
+234 (0) 9039055940
+254 707 053 111

Website:
www.vitalextralearning.com

Important quick links:
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Course Description

Python has been one of the premier, flexible, and powerful open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. This training is a step-by-step guide to Python and Statistical Data Analysis with extensive hands on. The course is delivered with several activity problems, assignments and scenarios that help participants gain practical experience in data handling, analysis, interpretation as well as reporting. This course starts by exploring basic statistics such as mean, median and mode and commence to advanced exploratory features such as groups comparisons, regression, test of relationships, classification, clustering, just to mention a few.

 

Learning outcomes

By the end of this course the participants will be able to:

     Easily read and write files of various types in to a Python program.

     Identify and fix errors in datasets.

     Work with Python 'modules' and use them for data analysis tasks

     Use libraries like pandas, numpy, matplotlib, scikit, and master the concepts like Python machine learning, scripts, and sequence.

     Gain high level skills on statistical results interpretation and report writing.

 

Who should enroll?

The course is useful for professionals who use data as part of their work and who need to make decisions from data analysis. Those with prior understanding of programming and statistics finds it easier to take this course.

  

Why train with us

Vital Extra Learning guarantees our clients:

     State-of-the-art facilities and training infrastructure

     Extended tradition of hand-holding during post engagement

     Service delivery through highly seasoned industry experts.

     Value for money

 

TOPICS TO BE COVERED:

Module1: Introduction

Introduction to Statistical Data Analysis

     Introduction to statistical concepts

     Descriptive and inferential statistics

     Research designing

     The research/survey process

 

Overview of Data Science

     Introduction to data science

     Different sectors using data science

     Purpose and components of python

 

Data Analytics Overview

     Data analytics process

     Knowledge check

     Exploratory Data Analysis (EDA)

     EDA-Quantitative technique

     EDA – Graphical technique

     Data analytics conclusion or predictions

     Data analytics communication

     Data types and plotting considerations

 

Module 2: Statistical Analysis and Business Applications

Introduction to statistical data analysis

     Statistical analysis considerations

     Population and sample

     Statistical analysis process

     Descriptive statistics – Measures of centres, distribution, dispersion

     Inferential Statistics (correlation, regression, t-tests, chi-square, etc)

 

Python Environment Setup and Essentials

     Anaconda

     Installation of Anaconda Python distribution

     Data types with Python

     Basic operators and functions

 

Mathematical Computing with Python (NUMPY)

     What is NumPy?

     NumPy vs list

     Installation

     NumPy arrays

     Built-in methods of NumPy (arrange; zeros and ones; linspace; eye; random)

     Array attributes and methods (reshape; max, min, argmax, argmin; shape; dtype)

     NumPy indexing and selection

     Broadcasting

     Indexing a 2D array (matrices)

     Selection

     NumPy operations (arithematic; universal array functions)

     Vectorization

 

Module 3: Scientific Computing with Python (SCIPY)

     Introduction to SciPy

     SciPy sub package – integration and optimisation

     Calculating eigenvalues and eigenvector

     Using SciPy to solve a linear algebra problem

     Use SciPy to define random variables for random values

 

Data Manipulation with PANDAS

     Introduction to Pandas

     DataFrame in Pandas

     Viewing and opening data

     Dealing with missing values

     Data operations

     Reading and writing files

     Pandas SQL operation

 

Machine Learning with SCIKIT–LEARN

     Introduction to machine learning

     Understanding data sets and extraction features

     Problem types and learning models

     How to train, test and optimise models

     Considerations for supervised learning models

     Scikit-Learn

     Supervised learning models – Linear regression, logistic regression

     Unsupervised learning models

     Pipeline

     Model persistence and evaluation

 

Module 4: Natural Language Processing with SCIKIT LEARN

     Overview of Natural Language Processing

     Applications of Natural Language Processing

     Libraries-Scikit

     Extraction considerations

     Scikit Learn-model training and grid search

 

Data Visualisation in Python Using MATPLOT-LIB

     Introduction to data visualisation

     Line properties

     (x, y) plot and subplots

     Types of plots

 

Module 5: Web Scraping with Beautiful Soup

     Web scraping and parsing

     Knowledge check

     Understanding and searching the tree

     Navigating options and modification options of a tree

     Parsing and printing documents

 

Integration with Hadoop MapReduce and Spark

     Big data solutions in Python

     Big Data and Hadoop

     Hadoop core components

     Python integration with HDFS using Hadoop streaming

     Using Hadoop streaming for calculating word count

     Python Integration with Spark using PySpark

     Using PySpark to determine word count

 

TRAINING CUSTOMIZATION

This training can be customized for your institution or delivered at your preferred location upon request. For training customization, contact us through
Email: info@ vitalextralearning.com
Telephone:
+234 (0) 8038 066705,
+234 (0) 9039055940
+254 707 053 111

LANGUAGE
English

_ Kenya _

Register with us for any short course in Nairobi and get an opportunity to visit and see endangered rhinos (white and black), hippos, crocodiles, ostrich, over 400 species of birds, leopards, lions, cheetah, buffalo and more.

OUR APPROACH – TADs
We THINK
We ANALYSE
We DEVELOP AND DELIVER SOLUTIONS that satisfy our clients