Python for Financial Analysis
0 (0 Ratings)
Enrolled:8
$80
$106
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LevelBeginner
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Duration11 hours 9 minutes
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Last UpdatedNovember 13, 2023
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Enrollment validityEnrollment validity: 365 days
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CertificateCertificate of completion
Hi, Welcome back!
About Course
Uncover the compelling reasons behind Python's prominence in this domain: a thriving community, a vast library ecosystem, user-friendly nature, and seamless interoperability. Whether you're a student, data analyst, financial expert, data scientist, or business professional, this course furnishes you with the adeptness to harness the potential of key libraries like NumPy, Pandas, Matplotlib, Plotly, and Seaborn. From refining financial data and crafting visual narratives to effectively communicating insights, this course empowers you to not only navigate data intricacies but also excel in the realm of financial analysis.
What will I learn?
- Learn core Python syntax, coding, and execution.
- Define variables and work with diverse data types.
- Perform operations on data using assignments.
- Take user input and create meaningful outputs.
- Explore arithmetic, comparison, and logical operators.
- Use conditional statements for decision-making.
- Define, call functions, and grasp their parameters.
- Master lists, dictionaries, and tuples for data storage.
- Utilize loops for iteration, with control statements.
- Learn Pandas, NumPy for data manipulation.
- Create plots charts and beautiful visuals using Matplotlib, Seaborn, and Plotly.
- Apply Python for financial analysis and decision-making.
Course Curriculum
Introduction to Python
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00:35
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06:28
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05:09
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Code Editor
06:38
Variables and Data Types
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Using Replit for Coding
08:01 -
Understanding Variables
08:01 -
Variable Types (String)
03:48 -
Variable Types (Integers)
03:48 -
Variable Type (Float)
03:16 -
Variable Type (Boolean)
04:56
Input Function and String Concatenation
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Input Functions
05:16 -
String Concatenation
05:51 -
Converting Data Types
08:45 -
Input Function Return Type
01:58 -
Task 1
01:31 -
Task 1 (Solution)
07:43
Python Operators
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Arithmetic Operator
04:59 -
Order of Operation
07:52 -
Comparison Operators
05:41 -
Logical Operators
04:54
Conditional Statements
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Conditional Statement
06:24 -
Conditional Statement – Exercise
08:27 -
Conditional Statement 2
08:38 -
Conditional Statement 3
06:46
Functions
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Functions
02:34 -
Functions with Parameters
05:12 -
Positional and Keyword Argument
04:59 -
Optional Parameters
07:26 -
Return Functions
04:54 -
Lesson Exercise
01:05 -
Function Exercise Solution
02:14
Lists
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List
04:30 -
Adding Items to a List
05:04 -
Removing Items from List
05:57 -
List Slicing
07:56 -
List Mutability
04:26 -
List Exercise
01:01 -
List Exercise Solution
01:50
Dictionary and Tuples
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Dictionary
03:59 -
Adding and Deleting from Dictionary
04:48 -
Nesting of Dictionary
05:02 -
Dictionary Mutability
02:19 -
Dictionary Exercise
01:09 -
Dictionary Exercise (Solution)
03:03 -
Tuples
02:57 -
Tuples Immutability
03:42 -
Set
03:21
Loops
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For Loops
11:27 -
While Loop
11:50 -
More on For Loop
09:09 -
Module Vs. Package
10:45 -
Random Module
10:58 -
DateTime
13:50
Anaconda Installation and Numpy
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DOWNLOAD ACCOMPANYING FILE & NOTE !
00:00 -
Anaconda Installation
17:22 -
Alternate Installation
05:39 -
Numpy I
08:16 -
Numpy II
09:04 -
Numpy III
13:28 -
Numpy Exercise
01:12 -
Numpy Exercise – Solution
07:45
Pandas
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Pandas Series
08:01 -
Pandas DataFrame
14:14 -
Pandas Exercise
00:39 -
Pandas Exercise – Solution
06:33 -
NaN Values and Groupby
08:41 -
Combining DataFrame
07:59 -
Pandas Methods and Pivot Tables
17:33 -
Times Series
17:52 -
Time Series Exercise
01:00 -
Time Series Exercise – Solution
05:31 -
Reading Data
13:49
Pandas Exercise
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Pandas Exercise
01:35 -
Pandas Exercise – Solution
11:24
Matplotlib
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Matplotlib
21:02 -
Matplotlib – Introduction
22:25 -
Matplotlib Exercise
01:05 -
Matplotlib Exercise – Solution
17:01
Seaborn
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Seaborn Relationship Plot
09:58 -
Seaborn Distribution Plot
20:21 -
Seaborn Categorical Plot
09:54 -
Seaborn Regression Plot
02:33 -
Seaborn Multi-Plot Grid
15:10 -
Seaborn Figure Theming
00:00 -
Seaborn Color Palette
08:32 -
Seaborn Matrix Plot
10:39
Seaborn exercise
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Seaborn Exercise
01:05 -
Seaborn Exercise – Solution
08:31
Plotting with Pandas
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Plotting with Pandas
06:35 -
Plotting with Pandas Exercise
00:53 -
Plotting with Pandas Exercise – Solution
04:47
Plotly Express
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Plotly Express
15:49 -
Plotly Express Exercise
00:29 -
Plotly Express Exercise – Solution
00:00
Financial Theory
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Time Value of Money
10:57 -
Financial Statement Analysis
19:39 -
Portfolio Theory
32:11 -
Asset Pricing Models
03:34
CAPSTONE PROJECT
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Portfolio Analysis & Optimization with Python
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