* RE: Invitation to Quantitative Data management, analysis and Visualization with Python workshop May 2023
@ 2023-04-18 2:13 Foscore Development Center
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Invitation
to Quantitative Data management, analysis and Visualization with Python workshop
May 2023
Course
Date
Duration
Location
Registration
15/05/2023 To 26/05/2023
10
Days
Nairobi
Kenya
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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
·
Research
Design
·
Python
for Data Science and Machine
·
Spark
for Big Data Analysis
·
Programmatically
download and analyse data
·
Practise
techniques to manage various types of data – ordinal, categorical,
encoding
·
Learn
data visualisation
·
Master
the art of performing step-by-step data analysis
·
Gain
information on the roles of a Machine Learning Engineer
·
Describe
Machine Learning
·
Work
with real-time data
·
Learn
tools and techniques for predictive modelling
·
Discuss
Machine Learning algorithms and their implementation
·
Validate
Machine Learning algorithms
·
Explain
Time Series and its related concepts
·
Perform
Text Mining and Sentimental analysis
WHO
SHOULD ATTEND?
·
Analytics
Team Managers
·
Business
Analysts who want to comprehend Machine Learning concepts
·
Information
Architects who want to gain proficiency in Predictive
Analytics
·
Programmers,
Developers, Technical Leads, Architects
·
Individuals
aspiring to be Machine Learning Engineers
·
Professionals
who want to develop automatic predictive models using data
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 (
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· EDA-Quantitative
Technique
· EDA
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· 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
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our courses can be Tailor-made to participants' needs
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participant must be conversant in English
· Presentations
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completion of training the participant will be issued with a Foscore development
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days.
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2-5
10%
6-10
15%
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2023-04-18 2:13 Invitation to Quantitative Data management, analysis and Visualization with Python workshop May 2023 Foscore Development Center
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