A Beginner-Friendly Course
Welcome to Python Basics
A perfect foundation stone to start a career in Python programming by giving you the hands-on training and practical experience you need to succeed. You'll learn the ins and outs of Python programming and become proficient in using libraries like Pandas, NumPy, and Matplotlib. With our comprehensive modules, you'll have the tools and confidence you need to excel in Python. Whether you're a complete beginner or have some programming experience, our course will give you the skills and knowledge you need to succeed. Ideal for: Financial Analysts, Quant Risk Analysts, Post-Grad Students, and Professionals preparing for roles in: Banks, Investment Firms, Asset Management Companies, Consulting Firms, Mutual Funds and Hedge Funds, and Other Financial Institutions
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HOURS ON-DEMAND
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PB PYTHON SCRIPTS
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REAL-TIME PROJECTS
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5.0
BLOGS PUBLISHED
RATING ACHIEVED
What You'll Learn
Python Data Types and Variables: Learn the foundational building blocks of Python, including the four core data types – Strings, Integers, Floats, and Booleans. Understand how to declare, assign, and manipulate variables. Practice using built-in functions like print() and type() and master casting between data types. Develop an understanding of Python's indentation rules and comment usage for clean, readable code.
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Python Operations and Data Structures: Master Python's operators – Arithmetic, Assignment, Comparison, and Logical – to manipulate data effectively. Dive deep into essential data structures including Lists, Tuples, Sets, and Dictionaries. Learn how to create, index, slice, and update these structures. Explore core methods like append(), remove(), sort(), add(), union(), and dictionary methods like keys() and values().
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Python Statements and Control Flow: Develop logic-building skills with conditional and looping statements. Learn how to structure programs using if, if-else, and if-elif-else statements. Gain proficiency in iterative control flows using for and while loops, nested conditionals, loop control statements like break, and comprehension techniques for lists and sets. Handle unexpected events with Python’s try-except error-handling mechanisms.​
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Basic Financial Applications using Python: Introduce finance-specific tools and workflows in Python. Extract and analyze historical stock data, calculate returns and volatility, and visualize market movements. Understand the structure of the Treasury Yield Curve and use Python to visualize interest rate environments. Gain a conceptual understanding of call and put options with Python-based examples.
Functions and Object-Oriented Programming (OOP): Understand how to structure code modularly with functions. Learn to define custom functions, pass arguments, and use return statements. Transition into Object-Oriented Programming by mastering classes, objects, constructors, and methods. Explore key OOP principles: Inheritance, Encapsulation, Polymorphism, and Abstraction for scalable and reusable code design.​
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Project – Bank Account Management System: Apply object-oriented programming concepts in a real-world mini-project. Build a fully functional banking system that supports account creation, deposits, withdrawals, fund transfers, and balance tracking. This project consolidates class-based programming and user-defined methods to simulate a practical financial application.
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Python for Data Analysis – Pandas and NumPy: Leverage Python’s most powerful libraries for data analysis. Use Pandas to handle structured data, manipulate DataFrames, clean missing values, and perform group-by and pivot operations. Utilize NumPy for efficient numerical computations, array manipulations, and mathematical operations critical for performance optimization in large datasets.
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Data Visualization with Matplotlib: Bring data to life with visual representations. Learn to use Matplotlib to plot line charts, histograms, scatter plots, pie charts, and multi-panel subplots. Customize titles, axis labels, legends, and data annotations to enhance clarity and interpretability of insights.
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Project – Stock Screening and Ranking Tool: Build a real-world Python application to evaluate stock performance. Create a stock screener that computes key metrics such as Mean Return, Beta, Sharpe Ratio, and Standard Deviation. Learn to filter and rank stocks using quantitative measures and object-oriented design for modularity and reusability.
Premium Subscription
Welcome to Python Basics
reach out to us at contact@thefinanalytics.com
INR 3,000
USD 35
Subscription Info.:
Prerequisites: No Prerequisites
25+ hrs Duration (6 Months Access)
Live Sessions (Instructor-led Interactive) + Recordings
Recorded Sessions (Self-Paced)
3+ Hands-On Projects (Team Collaboration)
Supported Devices: Desktop, Laptop, iPad (Not Supported on
Mobile Devices)
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Program Coverage Curated By Experienced Mentors
We're focused on delivering practical skills to data science, data analytics, and finance professionals with an in-depth understanding & implementation using python. We never stop adding more content to it.
Introduction to Python Platforms and Programming
→ Everything You Need To Get Started On Your Machine | Installation Process | Integrated Development Environment vs. Code Editor vs. Compiler Learnings | Python Libraries & Packages | Recommendations
→ Open-Source Web-Based Interactive Computing Platform Launching Application | Default Directories | Creating a New Jupyter Notebook | Menu Options & Toolbar | Keyboard Shortcuts | Code Cell
→ Python DataTypes - String | Integer | Float | Boolean | Functions - Print | Type | Python Comments
→ Python Concepts - Variables | Functions - Type | Casting Functions - Str | Int | Float | Rule of Indentation
→ Creating Variables | Concatenation of String DataType | Tagging Print Content | Modifying Variables using Python Built-In Methods - Upper, Lower, Replace
Introduction to Python Programming [7 Sessions]
Python Data Structures
→ Operators - Arithmetic | Assignment | Logical | Comparison
→ Creating Python List | List of Multiple Lists | Indexing Python List → Methods - Append | Insert | Extend | Remove | Reverse | Sort → Creating Python Tuple | Tuple of Multiple Tuples | List of Multiple Tuples | Indexing Python Tuple | Functions - Type | Length | Casting Functions - List | Tuple | Methods - Count | Index → Python Set - Creating | List of Multiple Sets | Tuple of Multiple Sets | Functions - List | Set | Methods - Add | Union | Intersection | Difference
→ Key-Value Pair Concept | Python Dictionary - Static DataTypes I Sequential DataTypes | Nested Dictionary | Methods - Items | Keys | Values | Clear Dictionary
Python Statements and Dynamics
→ Performing Operations - Comparison Operators | Logical Operators | Python Statements - IF Statement - Static & Sequential DataTypes | IF-ELSE Statement | IF-ELIF Statement | IF-ELIF-ELSE Statement | Nested Statements - Nested IF Statement | Nested IF-ELSE Statement | Nested IF-ELIF-ELSE Statement
→ FOR LOOP Statement | WHILE LOOP Statement | Conditional Loop Statements | Range Function | Break Statement | Comprehension - List | Set | All Possible Combinations
→ Input Function | TRY-EXCEPT Statements | TRY-EXCEPT-FINALLY Statements | Nested TRY-EXCEPT Statements | Python Exceptions/Errors - ZeroDivisionError | ValueError
Python Functions and Methods | Object-Oriented Programming
→ Python Class | Functions | Methods | Parameters/Arguments | Attributes/Variables | Return Statement → Global vs. Local Variable → Class - Functions & Methods | Constructors & Objects | OOP Concepts - Inheritance | Encapsulation | Polymorphism | Abstraction
→ Bank Account System: An Object-Oriented Finance Project | New Account Creation | Deposit Funds | Withdraw Funds | Transfer Funds Between Accounts | Update & Print Balance on Screen
Data Analysis with Python Libraries
→ Introduction to Pandas Library | Installation | Import | Pandas Data Structures - Data Series | DataFrames Methods - Describe | Append | Drop Duplicates | Difference | Fill NaN | Head | Tail Data | Filter - Single Condition → Pandas Data Structures - DataFrames | Dynamics of Creating DataFrames - Single Column | Multiple Columns | Indexing - loc | iloc | Slicing DataFrame | View DataFrame → Import & Export Data | Data Summary | Data Cleaning and Operations – Selection, Sorting, Filtering | Data Aggregation and Analysis – GroupBy, Resample, PivotTable | Data Restructuring | Data Visualization
Building Blocks – Python for Finance
→ Extract Historical & Intraday Time-Series Data - Stocks - Single | Multiple → Absolute Returns/Shocks | Proportional/Relative Shocks - Discrete | Continuous | ShockType Use | Comparison → Treasury Yield Curve - Normal | Inverted | Humped/Flat | Historical Time-Series of Interest Rates | 2007-08 & 2022-23 Interest Rate Profiles | Market Sentiments → Option Derivatives - Definition | Bullish & Bearish Belief - Buyer & Seller | Long & Short Positions | Option Premium | Payoff & Profit Profile | Use Case → Extract Historical Time-Series Data - Multiple Options | Prepare Option Chain for Calls & Puts for Multiple Strike for Single & Multiple Expiries → Extract Historical Time-Series Data - Multiple FX Prices, Interest Rates, Commodities, and Cryptocurrencies | Extract Financial Statements - Balance Sheets, Income Statements, Cashflow Statements, and Analyst Reports
Data Visualization for Data Science
→ Installation of Data Visualization Libraries
→ Plotting Charts & Sub-Charts - Line | Histogram | Scatter | Pie | Bar | Box | Heatmaps | Conditional Formatting
→ Multiple SubPlots | Images | Three-Dimensional
→ Customizing Plots - Title | Axis Labels & Ticks | Data Labels | Legends | Resizing | Font Style | Font Color | Alpha | Line Style | Alignments | Colors | Styles | Markers
→ Handling Missing Data | Performing Calculations | Reassigning Values | Adding Annotations
→ Highlighting Data Points/Results & Labels | Plotting Time-Series Data | Continuous & Categorical Data