Peneraju Teknologi Machine Learning untuk Siswazah
Professional Certification in
Python Data Science
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Start your data science journey with Python
and power up your career today!

Program Tajaan Penuh Kerajaan Khas Untuk Graduan
Bumiputera Berumur Diantara 18-30 Tahun


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Python Data Science
Professional Certification
Training Program
 
Start your data science journey
with Python and power up
your career today!
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Mod Pengajian

Sepenuh Masa / Kelas secara bersemuka
Fully Funded Residential Bootcamp
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Mod Pengajian

Sepenuh Masa / Kelas secara bersemuka
Fully Funded Residential Bootcamp
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numpy
pandas
matplotlib
seaborn2
python
scipy

Learn the fundamentals of Python Data Science and kickstart your career in one of the hottest professions of the decade.

This course includes the fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library.
The course also introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as group by, merge, and pivot tables effectively.
By the end of this course, participants will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.

Duration

6 days / 48 hours

Level

Beginner to Intermediate

Delivery

100% Online - Instructor Led
Request For Information

    Key Features

    • 48 hours of instructor led training
    • Fully Online
    • Class recording available
    • Interactive Learning
    • Additional Coaching Session
    • 100% HRDF SBL-KHAS & MDEC MyWiT Claimable!

    Pre-Requisites

    • Basic Programming Knowledge
    • Analytical Mindset
    • Willingness to self learn online
    • No prior experience is required
    • We will start from the very basics
    • Committed to complete all tasks

    Who Should Join

    • Professional switching careers
    • Business Analysts
    • IT Engineers
    • Students
    • New Programmers
    • Anyone interested in Python
    Key Learning Outcomes
    Upon completion, participants should be able to demonstrate each of the following outcome:-
    • Translate fundamental programming concepts such as loops, conditionals, etc into Python code.
    • Understand the key data structures in Python.
    • Understand how to write functions in Python and assess if they are correct via unit testing.
    • Know when and how to abstract code (e.g., into functions, or classes) to make it more modular and robust.
    • Produce human-readable code that incorporates best practices of programming, documentation, and coding style.
    • Use NumPy perform common data wrangling and computational tasks in Python.
    • Use Pandas to create and manipulate data structures like Series and DataFrames.
    • Wrangle different types of data in Pandas including numeric data, strings, and datetimes.
    Core Skills Areas Covered

    Data Wrangling

    Data Exploration

    Data Visualization

    Web Scraping

    NumPy and SciPy Packages

    Course Modules Covered in the Python Data Science program
    Module 1 - An Overview of Python

    An Overview of Python

    1. What is Python?
    2. Interpreted languages
    3. Advantages and disadvantages
    4. Downloading and installing
    5. Which version of Python
    6. Where to find documentation
    7. Python Comments
    8. Output to the screen
    9. Running Python Scripts
    10. Structure of a Python script
    11. Using the interpreter interactively
    Module 2 - Getting Started

    Getting Started

    1. Using variables
    2. Assigning value to multiple variables
    3. Expression
    4. Math operators
    5. String types: normal, raw and Unicode
    6. String operators
    7. Command line parameters
    8. Reading from the keyboard
    Module 3 - Decision & Flow Control,

    Decision & Flow Control

    1. About flow control
    2. Indenting is significant
    3. The if statements
    4. The nested if statements
    5. The elif statements
    6. The for loops
    7. The while loops
    8. Loop Controls - break and continue
    9. The range() function
    10. Arrays
    Module 4 - Defining Functions

    Defining Functions

    1. Syntax of function definition
    2. Formal parameters
    3. Global versus local variables
    4. Passing parameters and returning values
    5. Passing list of parameters
    6. Variable length arguments
    7. Lambda functions
    8. Passing function to another function
    9. Returning function
    10. Inner functions
      Module 5 - Working with Files

      Working with Files

      1. Text file I/O overview
      2. Opening a text file
      3. Reading text files
      4. Raw (binary) data
      5. Writing to a text file
      6. Opening Excel File
      7. Reading from Excel File
      8. Writing data into Excel File
      Module 6 - Sequence

      Sequence

      1. List overview
      2. List methods
      3. Tuple overview
      4. Tuple methods
      5. Dictionary overview
      6. Dictionary methods
      7. Set overview
      8. Set methods
      9. Fetching values
      10. Fetching keys
      11. Testing for existence of elements
      12. Deleting elements
      13. Set Operators
      Module 7 - Python Classes

      Python Classes

      1. About o-o programming
      2. Defining classes
      3. Class methods and data
      4. Constructors
      5. Objects
      6. Instance methods
      7. Instance data
      8. Destructors
      9. Interfaces
      10. Inheritances
      Module 8 - Errors and Exception Handling

      Errors and Exception Handling

      1. Dealing with syntax errors
      2. Exceptions
      3. Handling exceptions with try/except
      4. Cleaning up with finally
      Module 9 - Using Modules

      Using Modules

      1. What is a module?
      2. The import statement
      3. Function aliases
      4. Packages
      5. Installing Packages from PYPI
      6. Standard Modules – sys
      7. Standard Modules – math
      8. Standard Modules – time
      Module 10 - Regular Expressions

      Regular Expressions

      1. RE Objects and Pattern matching
      2. Parsing data
      3. Subexpressions
      4. Complex substitutions
      5. RE tips and tricks
      Module 11 - Standard Library

      Highlights of the Standard Library

      1. Working with the operating system
      2. Grabbing web pages
      3. Sending email
      4. Using glob for filename wildcards
      5. math and random
      6. Accessing dates and times with datetime
      7. Working with compressed files
      Module 12 - Databases

      Accessing Databases

      1. Selecting Data
      2. Inserting and Updating Data
      3. Deleting data
      4. Generic database API based on MySQL
      5. Using the Object Relational Mapper (SQLAlchemy)
      6. Working with NoSQL databases
      Module 13 - Data Distribution

      Data distribution

      1. Center
      2. Spread
      3. Shape – Symmetry, Number of peaks, Skewness, Uniform
      4. Unusual Features – Gaps, Outliers
      5. Measures of central tendency - Mean, Median, Mode, Midrange
      6. Measures of spread - Range, Variation, Standard deviation, Interquartile range
      7. Measures of shape - Empirical rule, Chebyshev's rule, Skewness, Kurtosis
      8. Measures of relative position – Quartiles, Percentiles, Midquartile
      Module 14 - Extract data from Website

      Extract data from Website - Beautiful soup

      1. Installing Beautiful Soup
      2. Installing a parser
      3. Making the soup
      4. Kinds of objects
      5. Navigating the tree
      6. Managing the tree
      7. Searching the tree
      8. Append the tree
      9. Insert inside the tree
      10. Extract, decompose, replace with,
      11. wrap and unwrap
      12. Pretty-printing
      13. Non-pretty printing
      14. Output formatters
      15. Get Text
      16. Output Encoding
      17. Unicode
      Module 15 - Selenium IDE

      Selenium IDE

      1. Selenium Overview
      2. Selenium IDE Introduction
      3. Downloading and Installing Selenium IDE
      4. Recording and Running a Simple Test
      5. Selenium IDE – Features
      6. Installing Useful Tools for Writing Tests
      7. Selenium Concepts
      Module 16 - Selenium Webdriver

      Selenium Webdriver

      1. Introduction to selenium webdriver
      2. Advantages of webdriver
      3. Downloading and configuring Webdriver
      4. Converting Selenium IDE test to programming language (Python)
      5. Detailed discussion about webdriver commands
      6. Handling different browsers
      7. Create our own methods in Webdriver
      8. Using RC commands from webdriver project
      Module 17 - Python for Data Analysis – NumPy

      Python for Data Analysis – NumPy

      1. Introduction
      2. Ndarray Object
      3. Data Types
      4. Array Attributes
      5. Array Creation Routines
      6. Array from existing data
      7. Numerical ranges
      8. Array Indexing and Slicing
      9. Advanced Indexing
      10. Iterating over Array
      11. Array Manipulation
      12. Arithmetic Operators
      13. Binary Operators
      14. String Functions
      15. Mathematical Functions
      16. Statistical Functions
      Module 18 - Python for Data Analysis – Pandas

      Python for Data Analysis – Pandas

      1. Introduction to Pandas
      2. Series
      3. DataFrames
      4. Missing Data
      5. Group By
      6. Merging Joining and Concatenating
      7. Operations
      8. Data Input and Output
      Module 19 - Python for Data Visualization

      Python for Data Visualization

      1. Matplotlib
      2. Seaborn
      3. Distribution Plots
      4. Categorical Plots
      5. Matrix Plots
      6. Grids
      7. Regression Plots
      8. Pandas Built-in Data Visualization
      9. Plotly
      10. Cufflinks
      11. Geographical Plotting
      12. Choropleth Maps
      Module 20 - Python for Data Analysis – SciPy

      Python for Data Analysis – SciPy

      1. Introduction
      2. Basic functions
      3. Special functions
      4. Integration
      5. Optimization
      6. Interpolation
      7. Fourier transforms
      8. Signal Processing
      9. Linear Algebra
      10. Sparse Eigenvalue Problems with ARPACK
      11. Compressed Sparse Graph Routines
      12. Spatial data structures and algorithms
      13. Statistics
      14. Multidimensional image processing
      Program Key Highlights
      online-learning-2
      48 hours of Remote Online Learning
      learning-hours
      Additional Coaching Hours
      hands-on
      Live Hands-on Projects
      certification
      Certified by International Body
      mentor
      Mentorship with Industry Experts
      industry
      Designed for Beginners & Professionals
      Request For Information

        python-cert
        Get Professionally Certified
        Upon successfully completing this program, participants will be awarded the Professional Certification in Python Data Science by International Council for Technology Certifications (ICTC).
        This award is a validation to the efforts taken to master the domain expertise that will set you apart from your competition.
        Be a part of the global network of data science professionals and join the community across sectors.
         
         



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