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A Beginner-Friendly Course

Welcome to Python Basics   [No Prior Experience Required]

Our program is 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.

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HOURS ON-DEMAND

PYTHON-BASED MODELS

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5

PYTHON PROJECTS

BLOGS PUBLISHED

RATING ACHIEVED

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Get Started with the Most Sought-After Skills in Data Science, Data Analytics, and Finance.

Our program is the perfect starting point for anyone looking to break into the exciting field of data science and data analytics. Whether you're a complete beginner or have some programming experience, our course will give you the skills and knowledge you need to succeed.

 

With a focus on hands-on learning, you'll get the opportunity to practice your skills and apply what you've learned in real-world scenarios. Our experienced instructors will guide you through the process, teaching you everything you need to know about Python programming and the use of popular libraries like pandas, numpy, and matplotlib.

In addition to the core curriculum, you'll also have access to a range of bonus materials, including exercises and quizzes, to help you practice and reinforce your skills. You'll also be part of a community of fellow students who are also learning Python, so you can collaborate and learn together.

 

By the end of the program, you'll be proficient in Python programming and ready to start writing your own algorithms. Don't miss out on this opportunity to take your skills to the next level and start your journey in the world of data science, data analytics, and finance.

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Premium Subscription

Welcome to Python Basics

reach out to us at contact@thefinanalytics.com

INR 5,000

USD 65

Inclusions

The Financial Book

INR 100

Ace Your Python Programming Interview

INR 100

No prerequisite!   No prior experience required.

60-days [ 30+ Hours ] training program.

Get hands-on experience on real-world projects [ 5+ Projects ] and team collaboration.

Q&A support incl. python model assistance.

Online instructor-led weekend live batch + recorded sessions available as fallback option.

Downloadable access to python-based models.

Read access to financial book, interview guides, and reference materials.

Get a certificate on completion.

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Our Expert-Led Resources For Your Journey

Unleash your full potential with expert-led resources that focus on practical understanding by taking advantage of our step-by-step self-paced materials to learn and practice at your own pace.

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

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