DATA SCIENCE WITH PYTHON

DATA SCIENCE WITH PYTHON

Summary

Location

Location

Malaysia

Duration

Duration

3 Days
Format

Format

Public Class

Public Class

Python for Data Science: Unlocking Insights from Data

Are you ready to embark on a data-driven journey? Dive into the world of ‘Python for Data Science’ with our intensive 3-day course. Whether you’re an aspiring data enthusiast or a seasoned professional, this course is your gateway to unlocking the power of Python for data analysis.

What to Expect

In this hands-on course, we’ll start with the fundamentals of Python, covering everything from syntax to data manipulation. You’ll learn to harness the capabilities of Python for data visualization, statistical analysis, and machine learning. By the end, you’ll be equipped to gain profound insights from data, giving your career a significant boost.

Your Data-Driven Future

Imagine the endless possibilities that open up when you master ‘Python for Data Science.’ From making data-driven decisions that drive business success to standing out in a competitive job market, this course empowers you to turn raw data into valuable insights. Don’t miss this opportunity to supercharge your career. Join us and become a data-savvy professional ready to conquer the challenges of the modern corporate world.

Course Details

Course Duration: 3 days; Instructor-led

Audience

This course “Data Science with Python” is intended for learners who have basic python knowledge and wants to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data.

Prerequisites

There are no prerequisites for this course but python knowledge with a little programming background is preferred

Methodology

This program will be conducted with interactive lectures, PowerPoint presentation, discussion and practical exercise.

Course Objectives

After completing this course, you should be able to:

  • Explore Python fundamentals, including basic syntax, variables, and types.
  • Create and manipulate regular Python lists.
  • Use functions and import packages.
  • Build Numpy arrays and perform interesting calculations.
  • Create and customize plots on real data.
  • Supercharge with control flow, and get to know the Pandas DataFrame
  • Use Python to read and write files.
  • Illustrate Supervised Learning Algorithms
  • Identify and recognize machine learning algorithms around us

Outlines

  • Introduction to the Course
  • Environment Set-Up
  • Virtual Environments
  • Data types and Operators
  • Integers, Floats, Strings, Bytes, Tuples and Lists
  • Dictionaries and Ordered Dictionaries
  • Sets and frozen sets.
  • Flow control – if, elif statements
  • Flow control – while loops
  • Creating and using functions
  • Creating modules and packages
  • Distributing code to repositories
  • Creating Classes
  • Creating Objects and Instances
  • Data Encapsulation
  • Class Inheritance
  • Multiple Inheritance
  • Decorators
  • Handling Exception
  • Raising exceptions
  • Writing tests cases
  • Executing tests
  • Checking code coverage by tests
  • Accessing different types of files
  • File handling principles
  • Creating and reading Files
  • Updating Files
  • Deleting files
  • Text Files
  • CSV Files
  • Microsoft Word
  • Microsoft Excel
  • Regular Expressions
  • Extracting data from text files using Regular Expressions
  • Creating and deleting directories
  • Listing and searching for files
  • Introduction to RDBMS Databases and Concepts
  • Types of SQL Statements (DDL vs DML)
  • How to Access Databases Using Python
  • CREATE TABLE
  • ALTER, DROP and TRUNCATE
  • SELECT statement
  • COUNT, DISTINCT, LIMIT
  • INSERT statement
  • UPDATE AND DELETE statements
  • Using String Patterns and Ranges
  • Sorting Result Sets
  • Grouping Result Sets
  • Built-in Database Functions
  • Date and Time Built-in Functions
  • Sub-Queries and Nested Selects
  • Working with Multiple Tables
  • Object Relational Mapper (SQLAlchemy)
  • Introduction and Architecture
  • Introduction to SQLAlchemy ORM
  • Database Models
  • Relationships
  • Queries
  • Inserting Data
  • Updating Data
  • Deleting Data
  • Introduction
  • Differences between MongoDB and MySQL
  • Setting up MongoDB
  • Overview of MongoDB Features and Architecture
  • Mapping between a relational database and MongoDB
  • Connecting to MongoDB
  • Starting a Python + MongoDB application
  • Understanding the MongoDB Data Processing Pipeline
  • Reading and Writing to the database
  • Creating a New Database
  • Understanding Availability in MongoDB
  • Summary and Conclusion
  • Introduction
  • Setting up the Development Environment
  • Python Primer: Data Structures, Conditionals, File Handling, etc.
  • Python Packages for Web Scraping: Scrapy and BeautifulSoup
  • How a Website Works
  • How HTML is Structured
  • Making a Web Request
  • Scraping an HTML Page
  • Working with XPath and CSS
  • Filtering Data Using Regular Expressions
  • Creating a Web Crawler
  • Crawling AJAX and JavaScript Pages with Selenium.
  • Web Scraping Best Practices
  • Troubleshooting
  • Summary and Conclusion
  • Introduction
  • Ndarray Object
  • Data Types
  • Array Attributes
  • Array Creation Routines
  • Array from existing data
  • Numerical ranges
  • Array Indexing and Slicing
  • Advanced Indexing
  • Iterating over Array
  • Array Manipulation
  • Arithmetic Operators
  • Binary Operators
  • String Functions
  • Mathematical Functions
  • Statistical Functions
  • Introduction
  • Basic functions
  • Special functions
  • Integration
  • Optimization
  • Interpolation
  • Fourier transforms
  • Signal Processing
  • Linear Algebra
  • Sparse Eigenvalue Problems with ARPACK
  • Compressed Sparse Graph Routines
  • Spatial data structures and algorithms
  • Statistics
  • Multidimensional image processing
  • File IO
  • Introduction to Pandas
  • Series
  • DataFrames
  • Missing Data
  • Groupby
  • Merging Joining and Concatenating
  • Operations
  • Data Input and Output
  • Matplotlib
  • Seaborn
  • Distribution Plots
  • Categorical Plots
  • Matrix Plots
  • Grids
  • Regression Plots
  • Pandas Built-in Data Visualization
  • Plotly and Cufflinks
  • Geographical Plotting
  • Choropleth Maps

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Reviews

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DATA SCIENCE WITH PYTHON

Starting From
RM3900
Intake Date
5-7 FEB 2024
,
6-8 MAY 2024
,
5-7 AUG 2024
,
25-27 NOV 2024
Class Type
Private, Public

Why Us

Variety of Courses

Variety of Courses

Customizable Class

Customizable Class

Consultants Facilitate

Consultants Facilitate

HRDF Claimable

HRDF Claimable

Professional Certifications

Professional Certifications

Free Chat to Get Quote

Free Chat to Get Quote

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