Data Analytics Using Python
(14 hrs)
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Home / Data, Tech & AI / Data Analytics Using Python (14 hrs)
*Classroom-based Learning*
This 2-day workshop provides a strong foundation in Python programming, covering fundamental concepts like variables, data types, operators, control flow, functions, and data structures. You will also explore essential tools like Jupyter Notebook and gain exposure to various applications of Python in the data science world through real-world examples and case studies.
Key Benefits
- Grasp the fundamentals of Python programming
- Work effectively with data structures
- Utilise functions and loops to automate tasks
- Explore essential data science tools
- Gain insights into real world applications with Python code
Course Contents
An Introduction to Programming with Python
- Why python - handling Big Data (Macro) vs small data
- Downloads
- Anaconda, Jupyter interface and dashboards
- Global examples and case studies of applications in the data science world
Python Variables and Data Types
- Variables
- Numbers and Boolean Values
- Strings
Basic Python Syntax
- Arithmetic Operators
- Double equality sign
- Reassigning values
- Adding comments
- Line continuation
- Indexing elements
- Structure of code with identation
- Comparison Operators
- Logical and identity operators
Conditional Statements
- Introduction to the IF statement
- Adding ELSE
- Else if, ELIF
- A note on boolean values
Python Functions
- Defining a function in Python
- Creating a Function with a Parameter
- Another way to define a function
- Function within a Function
- Combining Conditional Statements and Functions
- Creating Functions Containing a Few arguments
Python Sequences
- Lists, Tuples, Dictionaries
Using Iterations in Python
- For Loops
- While loops and incrementing
- Create Lists with range() Function
- Using Conditional Statements and Loops together
- All in - Conditional Statements, Functions, and Loops
- Iterating over Dictionaries
Advanced Python Tools
- OOP
- Modules & Packages
- The Standard Library
- Importing Modules
- Must have packages for Finance and Data Science
- Numpy, Pandas, Matplotib
- Statsmodels etc
- Generating Random Numbers
- Sources of Financial Data
- Accessing Notebook Files
- Importing and organizing data
- Time Series Data
Global - Real World Scenario Applications with Python Code
- Case Studies to be discussed for Data Science and Business
- Time Series Regression
- Loan Defaults
- Predicting crime with crime rate data
- Deep Learning & Medicine
- AI and Deep Learning & Markets
Who Should Attend
This workshop is suitable for individuals keen to learn more about Python for Data Science.
*This programme is customisable to suit organisational needs. Please contact us for exclusive and attractive corporate rates.
Trainer
Suhnylla Kler is a highly experienced investment and offshore banker with over 30 years of experience in the financial markets. She is a registered member of Persatuan Kewangan Malaysia (PKM) and a licensed fund manager under the Securities Commission of Malaysia. With her extensive experience in the financial services industry, Suhnylla has worked with numerous leading financial institutions and has participated in the set up of fixed income and debt origination activities for both HSBC and ABN AMRO. She is also a proficient coder in AI/ML and Deep Learning.
