Introduction
This comprehensive
course will be your guide to learning how to use the power of Python to analyze
big data, create beautiful visualizations, and use powerful machine learning
algorithms. This course is designed for both beginners with basic programming
experience or experienced developers looking to make the jump to Data Science
and big data Analysis. Python has been
one of the most adaptable, and robust open-source languages that are easy to
learn and uses powerful libraries for data manipulation and analysis. For many
years now, Python has been used in scientific computing and mathematical
domains such as physics, finance, oil and gas, and signal processing.This Big Data
Analytics with Python course provides a complete overview of data analysis
techniques using Python. A Data Scientist is one of the strongest professions
today and Python is a crucial skill for such roles.The Big Data Analytics with
Python course teaches you to master the concepts of Python programming. Through
this training, you will gain knowledge of the essential tools of Data Analytics
with Python.
DURATION
10 Days
Course Objective
WHO SHOULD ATTEND?
Course content
Module1: Basic statistical terms and concepts
· Introduction
to statistical concepts
· Descriptive
Statistics
· Inferential
statistics
Module 2:Research Design
· The
role and purpose of research design
· Types
of research designs
· The
research process
· Which
method to choose?
· Exercise:
Identify a project of choice and developing a research design
Module 3: Survey Planning, Implementation and Completion
· Types
of surveys
· The
survey process
· Survey
design
· Methods
of survey sampling
· Determining
the Sample size
· Planning
a survey
· Conducting
the survey
· After
the survey
· Exercise:
Planning for a survey based on the research design selected
MODULE
4: DATA SCIENCE OVERVIEW
· Introduction
to Data Science
· Different
Sectors Using Data Science
· Purpose
and Components of Python
MODULE
5: 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 for Plotting
· Data
Types and Plotting
MODULE
6: STATISTICAL ANALYSIS AND BUSINESS APPLICATIONS
· Introduction
to Statistics
· Statistical
and Non-statistical Analysis
· Major
Categories of Statistics
· Statistical
Analysis Considerations
· Population
and Sample
· Statistical
Analysis Process
· Data
Distribution
· Dispersion
· Histogram
· Correlation
and Inferential Statistics
MODULE
7 PYTHON ENVIRONMENT SETUP AND ESSENTIALS
· Anaconda
· Installation
of Anaconda Python Distribution
· Data
Types with Python
· Basic
Operators and Functions
MODULE
8: MATHEMATICAL COMPUTING WITH PYTHON (NUMPY)
· Introduction
to NumPy
· Activity-Sequence
it Right
· Creating
and Printing an nd array
· Class
and Attributes of nd array
· Basic
Operations
· Copy
and Views
· Mathematical
Functions of NumPy
· Evaluate
the datasets containing GDPs of different countries
· Evaluate
the datasets of Summer Olympics 2012
MODULE
9: SCIENTIFIC COMPUTING WITH PYTHON (SCIPY)
· Introduction
to SciPy
· SciPy
Sub Package – Integration and Optimisation
· SciPy
Sub package
· Demo
– Calculate Eigenvalues and Eigenvector
· Use
SciPy to solve a linear algebra problem
· Use
SciPy to define 20 random variables for random values
MODULE
10: DATA MANIPULATION WITH PANDAS
· Introduction
to Pandas
· Understanding
DataFrame
· View
and Select Data Demo
· Missing
Values
· Data
Operations
· File
Read and Write Support
· Pandas
SQL Operation
· Analyse
the Federal Aviation Authority (FAA) dataset using Pandas
· Analyse
the dataset in CSV format given for fire department
MODULE
11: MACHINE LEARNING WITH SCIKIT–LEARN
· Machine
Learning Approach
· Understand
data sets and extract its features
· Identifying
problem type and learning model
· How
it Works
· Train,
test and optimising the model
· Supervised
Learning Model Considerations
· Scikit-Learn
· Supervised
Learning Models – Linear Regression
· Supervised
Learning Models – Logistic Regression
· Unsupervised
Learning Models
· Pipeline
· Model
Persistence and Evaluation
· Analyse
a dataset to find the features and response label of it
MODULE
12: NATURAL LANGUAGE PROCESSING WITH SCIKIT LEARN
· NLP
Overview
· NLP
Applications
· NLP
Libraries-Scikit
· Extraction
Considerations
· Scikit
Learn-Model Training and Grid Search
· Analyse
a given spam collection dataset
· Analyse
the sentiment dataset using NLP
MODULE
13: DATA VISUALISATION IN PYTHON USING MATPLOT-LIB
· Introduction
to Data Visualisation
· Line
Properties
· (x,
y) Plot and Subplots
· Types
of Plots
· Analyse
the “auto mpg data” and draw a pair plot
· Draw
a pie chart to visualise a dataset
MODULE
14: WEB SCRAPING WITH BEAUTIFUL SOUP
· Web
Scraping and Parsing
· Knowledge
Check
· Understanding
and Searching the Tree
· Navigating
options
· Demo3
Navigating a Tree
· Knowledge
Check
· Modifying
the Tree
· Parsing
and Printing the Document
· Scrape
the Simplilearn website page to perform some tasks
· Scrape
the Simplilearn website page to perform some tasks
MODULE
15: INTEGRATION WITH HADOOP MAP-REDUCE AND SPARK
· Why
Big Data Solutions are Provided for Python0
· 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
· Determine
the word count for Amazon dataset
General Notes
·
All our courses can be
Tailor-made to participants needs
·
The participant must be
conversant with English
·
Presentations are
well guided, practical exercises, web-based tutorials, and group work. Our
facilitators are experts with more than 10years of experience
·
Upon completion of
training, the participant will be issued with a Global King Project Management
certificate
·
Training will be done at
the Global King Project Management Centers (Nairobi Kenya, Mombasa Kenya,
Kigali Rwanda, Dubai ,Lagos Nigeria and More others).
·
A discount of 20% will
be given to more than 4 participants from same organization.
·
Course duration is
flexible and the contents can be modified to fit any number of
days.
·
Payment should be done
before commencement of the training, to the Global King Project Management
account, so as to enable us to prepare better for you.
·
For any inquiry
to: [email protected] or +254 114 830 889
·
Website: www.globalkingprojectmanagement.org
·
Tablet and Laptops are
provided to participants on request as an add-on cost to the training fee
·
The course fee for
onsite training includes facilitation training materials, 2 coffee breaks, a
buffet lunch, and a Certificate of successful completion of Training.
Participants will be responsible for their own travel expenses and
arrangements, airport transfers, visa application dinners, health/accident
insurance, and other personal expenses.
Start Date | End Date | Action | Duration | Location | Fee(Kes) | Fee(Us$) |
---|---|---|---|---|---|---|
06/03/2023 | 17/03/2023 | 10Days | Nairobi | 200000 | 2400 | |
03/04/2023 | 14/04/2023 | 10Days | Nairobi | 200000 | 2400 | |
01/05/2023 | 12/05/2023 | 10Days | Nairobi | 200000 | 2400 | |
26/06/2023 | 07/07/2023 | 10Days | Nairobi | 200000 | 2400 | |
24/07/2023 | 04/08/2023 | 10Days | Nairobi | 200000 | 2400 | |
21/08/2023 | 01/09/2023 | 10Days | Nairobi | 200000 | 2400 | |
18/09/2023 | 29/09/2023 | 10Days | Nairobi | 200000 | 2400 | |
16/10/2023 | 27/10/2023 | 10Days | Nairobi | 200000 | 2400 | |
09/01/2023 | 20/01/2023 | 10Days | Mombasa | 240000 | 3000 | |
06/02/2023 | 17/02/2023 | 10Days | Mombasa | 240000 | 3000 | |
29/05/2023 | 09/06/2023 | 10Days | Mombasa | 240000 | 3000 | |
13/11/2023 | 27/10/2023 | 10Days | Mombasa | 240000 | 3000 |