Training Course on Data Science and Big Data Analytics with Python

Course Description

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 Training Course on Data Science and Big Data Analytics with Python provides a complete overview of data analysis techniques using Python. Data Science is one of the strongest professions today and Python is a crucial skill for such roles.

The Training Course on Data Science and Big Data Analytics with Python 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. Participants will learn concepts and techniques like web scraping, hypothesis building, data wrangling, data exploration, data visualization, mathematical computing, Python programming concepts, NumPy and SciPy, and Scikit-Learn for Natural Language Processing.

This Training Course on Data Science and Big Data Analytics with Python offered by Phoenix Center for Policy, Research and Training will empower you with the practical experience required to perform predictive modelling that would require Machine Learning using Python.

Course Objectives

Upon the successful completion of this Training Course on Data Science and Big Data Analytics with Python, participants will be able to:

• Programmatically download and analyze data
• Practice techniques to manage various types of data ordinal, categorical, encoding
• Learn and master data visualization
• 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

Training Methodology

The course is designed to be highly interactive, challenging and stimulating. It will be an instructor led training and will be delivered using a blended learning approach comprising of presentations, discussions, guided sessions of practical exercise, case study review, web-based tutorials, group work, exploration of relevant issues collaborative strength training, performance measurement, and workshops of participants displays, all of which adhere to the highest standards of training. The training technique is built on learning by doing, with lecturers using a learner-centered approach to engage participants and provide tasks that allow them to apply what theyve learned. Experiential knowledge is also given equal importance within the format of training. Our facilitators are seasoned industry professionals with years of expertise in their chosen fields. All facilitation and course materials will be offered in English.

Who Should Attend?

This Training Course on Data Science and Big Data Analytics with Python would be suitable for, but not limited to:

• 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

Module 1: Data Science Overview

• Introduction to Data Science
• Different Sectors Using Data Science
• Purpose and Components of Python

Module 2: 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 3: 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 4: Python Environment Setup and Essentials

• Anaconda
• Installation of Anaconda Python Distribution
• Data Types with Python
• Basic Operators and Functions
  

Module 5: 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 6: Scientific Computing with Python (Scipy)

• Introduction to SciPy
• SciPy Sub Package Integration and Optimization
• 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 7: 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
• Analyze the Federal Aviation Authority (FAA) dataset using Pandas
• Analyze the dataset in CSV format given for fire department

Module 8: Machine Learning with ScikitLearn

• Machine Learning Approach
• Understand data sets and extract its features
• Identifying problem type and learning model
• How it Works
• Train, test and optimizing 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
• Analyze a dataset to find the features and response label of it

Module 9: Natural Language Processing with Scikit Learn

• NLP Overview
• NLP Applications
• NLP Libraries-Scikit
• Extraction Considerations
• Scikit Learn-Model Training and Grid Search
• Analyze a given spam collection dataset
• Analyze the sentiment dataset using NLP

Module 10: Data Visualization in Python Using Matplot-Lib

• Introduction to Data Visualization
• Line Properties
• (x, y) Plot and Subplots
• Types of Plots
• Analyze the auto mpg data and draw a pair plot
• Draw a pie chart to visualize a dataset

Module 11: 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 12: 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

Requirements

• Participants should be reasonably proficient in English.
• Applicants must live up to Phoenix Center for Policy, Research and Training admission criteria.

NOTE

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free
2. What is catered for by the Course Fees: Fees cater for all requirements for the training Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.
3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.
4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.
5. Approval of Course: Our Programs are NITA Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

How to Book

Simply send an email to the Training Officer on training@phoenixtrainingcenter.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254737566961

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above Cheque Payments to: Phoenix Center for Policy, Research and Training Limited should be paid in advance, 5 days to the training.
2. Invoice: We can send a bill directly to you or your company.
3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.
2. Participants may cancel attendance 14 days or more prior to the training commencement date.
3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location.

For further inquiries, please contact us on Tel: +254720272325 / +254737566961 or Email training@phoenixtrainingcenter.com  

Accommodation

Accommodation is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@phoenixtrainingcenter.com or on Tel: +254720272325 / +254737566961