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
5 days / 40 hours
Level
Beginner to Intermediate
Delivery
100% Online - Instructor Led
Key Features
- 40 hours of instructor led training
- Fully Online
- Class recording available
- Interactive Learning
- 8 hour Coaching Session
- 100% HRDF SBL-KHAS 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
Day 1 - An Overview of Python, Getting Started, Decision & Flow Control, Defining Functions
An Overview of Python
- What is Python?
- Interpreted languages
- Advantages and disadvantages
- Downloading and installing
- Which version of Python
- Where to find documentation
- Python Comments
- Output to the screen
- Running Python Scripts
- Structure of a Python script
- Using the interpreter interactively
Getting Started
- Using variables
- Assigning value to multiple variables
- Expression
- Math operators
- String types: normal, raw and Unicode
- String operators
- Command line parameters
- Reading from the keyboard
Decision & Flow Control
- About flow control
- Indenting is significant
- The if statements
- The nested if statements
- The elif statements
- The for loops
- The while loops
- Loop Controls - break and continue
- The range() function
- Arrays
Defining Functions
- Syntax of function definition
- Formal parameters
- Global versus local variables
- Passing parameters and returning values
- Passing list of parameters
- Variable length arguments
- Lambda functions
- Passing function to another function
- Returning function
- Inner functions
Day 2 - Working with Files, Sequence, Python Classes
Working with Files
- Text file I/O overview
- Opening a text file
- Reading text files
- Raw (binary) data
- Writing to a text file
- Opening Excel File
- Reading from Excel File
- Writing data into Excel File
Sequence
- List overview
- List methods
- Tuple overview
- Tuple methods
- Dictionary overview
- Dictionary methods
- Set overview
- Set methods
- Fetching values
- Fetching keys
- Testing for existence of elements
- Deleting elements
- Set Operators
Python Classes
- About o-o programming
- Defining classes
- Class methods and data
- Constructors
- Objects
- Instance methods
- Instance data
- Destructors
- Interfaces
- Inheritances
Day 3 - Errors and Exception Handling, Using Modules, Regular Expressions, Standard Library, Databases, Data Distribution
Errors and Exception Handling
- Dealing with syntax errors
- Exceptions
- Handling exceptions with try/except
- Cleaning up with finally
Using Modules
- What is a module?
- The import statement
- Function aliases
- Packages
- Installing Packages from PYPI
- Standard Modules – sys
- Standard Modules – math
- Standard Modules – time
Regular Expressions
- RE Objects and Pattern matching
- Parsing data
- Subexpressions
- Complex substitutions
- RE tips and tricks
Highlights of the Standard Library
- Working with the operating system
- Grabbing web pages
- Sending email
- Using glob for filename wildcards
- math and random
- Accessing dates and times with datetime
- Working with compressed files
Accessing Databases
- Selecting Data
- Inserting and Updating Data
- Deleting data
- Generic database API based on MySQL
- Using the Object Relational Mapper (SQLAlchemy)
- Working with NoSQL databases
Data distribution
- Center
- Spread
- Shape – Symmetry, Number of peaks, Skewness, Uniform
- Unusual Features – Gaps, Outliers
- Measures of central tendency - Mean, Median, Mode, Midrange
- Measures of spread - Range, Variation, Standard deviation, Interquartile range
- Measures of shape - Empirical rule, Chebyshev's rule, Skewness, Kurtosis
- Measures of relative position – Quartiles, Percentiles, Midquartile
Day 4 - Extract data from Website, Selenium IDE, Selenium Webdriver, Python for Data Analysis – NumPy
Extract data from Website - Beautiful soup
- Installing Beautiful Soup
- Installing a parser
- Making the soup
- Kinds of objects
- Navigating the tree
- Managing the tree
- Searching the tree
- Append the tree
- Insert inside the tree
- Extract, decompose, replace with,
- wrap and unwrap
- Pretty-printing
- Non-pretty printing
- Output formatters
- Get Text
- Output Encoding
- Unicode
Selenium IDE
- Selenium Overview
- Selenium IDE Introduction
- Downloading and Installing Selenium IDE
- Recording and Running a Simple Test
- Selenium IDE – Features
- Installing Useful Tools for Writing Tests
- Selenium Concepts
Selenium Webdriver
- Introduction to selenium webdriver
- Advantages of webdriver
- Downloading and configuring Webdriver
- Converting Selenium IDE test to programming language (Python)
- Detailed discussion about webdriver commands
- Handling different browsers
- Create our own methods in Webdriver
- Using RC commands from webdriver project
Python for Data Analysis – NumPy
- 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
Day 5 - Python for Data Analysis – Pandas, Python for Data Visualization, Python for Data Analysis – SciPy
Python for Data Analysis – Pandas
- Introduction to Pandas
- Series
- DataFrames
- Missing Data
- Group By
- Merging Joining and Concatenating
- Operations
- Data Input and Output
Python for Data Visualization
- Matplotlib
- Seaborn
- Distribution Plots
- Categorical Plots
- Matrix Plots
- Grids
- Regression Plots
- Pandas Built-in Data Visualization
- Plotly
- Cufflinks
- Geographical Plotting
- Choropleth Maps
Python for Data Analysis – SciPy
- 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
Our Training Methodology
Practical Assignments
We provide hands-on assignments that requires practical implementation.
Virtual Coaching Sessions
Online coaching sessions that happen over the phone, via video, or on a web platform.
1 Year Access to LMS
Get access to learning resources upto 1 year of class completion.
Live Project Experience
Hands-on learning and training gives participants the opportunity to experience real world situations.
Online Assessments
Participants can assess reflect on their own learning and their level/skills.
Free Industry Webinars
Stay current on market research trends, learn best practices through our webinar sessions.
Your Instructor
Thayanithy Jegan
CTO & Co-Founder of Thulija Technologies,
Certified Trainer and Consultant
Certified Trainer and Consultant
A seasoned technology professional with over 17 years of industry experience as a software developer, solutions architect and technology consultant for major organizations.
Thayanithy Jegan has trained executives and developers in companies such as Maxis, IFCA, SWIFT, PSDC, DHL, Standard Chartered, Infineon Technologies, Siemens and Bank Negara to name a few to break into various technology stacks and as well as data science, big data, and artificial intelligence.
He has led major projects with clients such as Suruhanjaya Syarikat Malaysia (SSM), MYCOID, Kementerian Kerja Raya, Ministry of Education (MOE), Universiti Malaya, Perfisio Solutions, Kementerian Perdagangan Antarabangsa dan Industri, amongst others. He has also served as a Consultant for MIMOS Berhad, a Research and Development organisation that functions as an advisor to the Malaysian Government on technologies, policies and strategies relating to IT.
Program Key Highlights
40 hours of Remote Online Learning
80 Additional Self Learning Hours
12 Live Hands-on Projects
Certified by International Body
Mentorship with Industry Experts
Designed for Beginners & Professionals
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.
Get in Touch With Us Today!
This training program is suitable for anyone who intends to enter into the field of Data Science.
This program is being conducted in Malaysia and can be joined by anyone, anywhere in the world remotely.
This program is being conducted in Malaysia and can be joined by anyone, anywhere in the world remotely.
Program Fee
Scholarship price MYR 3700 per pax .
Limited Scholarships available for early birds. Apply now and get 40% OFF program fee.
One-time fee. One year access to course materials.
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READY TO KICKSTART YOUR CAREER?
Please fill in the form and a Program Advisor will reach out to you. You can also reach out to us at info@thulija.com or +60123661502