Python for finance could help learners develop a range of valuable skills and knowledge, including an understanding of data science basics, proficiency in using Python libraries and tools, and the ability to use machine learning and statistical modeling to forecast financial trends and make predictions. This course would also provide learners with an understanding of key financial concepts and how to apply them in a data-driven context. Overall, this course could provide a strong foundation for pursuing further study or a career in data science for finance.
Training Duration: 3 Days
Minimum private group training class: 5 Participants
- Certificate Of Completion Available
- Group Private Class
- VILT Class Available
- SBL-Khas Claimable
Related training:
- Python Essentials
- Python Programming
- Data Science With Python
- PTN-107: Artificial Intelligence, Data Science and Machine Learning with Python
- PTN-108: Advanced Python Scripting
For Technical Finance:
For more details, may check out the entire series and blogs at Learn Python with GemRain
In data science with Python for finance, you will learn how to use Python programming and statistical and machine learning techniques to analyze and understand financial data. This includes learning how to work with and manipulate large datasets using libraries such as NumPy and Pandas, as well as how to implement machine learning and deep learning algorithms using libraries such as scikit-learn and TensorFlow. You will also learn how to visualize financial data using tools such as Matplotlib and Seaborn, and how to communicate the insights that you have extracted from data to a non-technical audience.
In addition to learning these technical skills, you will also learn how to apply data science to a variety of financial tasks and problems. This might include analyzing financial markets, predicting stock prices, analyzing risk and performance, and automating and optimizing financial processes. You will learn how to build predictive models, identify patterns in data, and make data-driven decisions.
Overall, data science with Python for finance is a highly relevant and in-demand field that allows you to combine your interests in finance and data science to drive business success. By learning these skills, you will be well-prepared to take on a variety of roles in finance, such as a data scientist, a risk manager, or an algorithmic trader. It is important to have a strong foundation in both Python programming and statistical and machine learning techniques in order to succeed in this field, and a commitment to learning and staying up-to-date with the latest developments is essential.
After completing this Python course, you should be able to:
- Explore Python fundamentals, including basic syntax, variables, and types
- Create and manipulate regular Python lists
- Use functions and import packages
- Build Numpy arrays, and perform interesting calculations
- Create and customize plots on real data
- Supercharge with control flow, and get to know the Pandas DataFrame
- Use Python to read and write files
- Illustrate Supervised Learning Algorithms
- Identify and recognize machine learning algorithms around us
There are no prerequisites for this course but python knowledge with a little programming background is preferred.
Data science with Python for finance is a course or program intended for individuals interested in using Python and data science techniques to analyze financial data and make informed decisions in the finance industry. This may include professionals working in finance or related fields, such as banking, accounting, investment, or insurance, as well as students or individuals who are interested in pursuing a career in finance and want to learn more about using data science techniques to analyze financial data.
The course or program may cover topics such as financial data analysis, financial modeling, machine learning, and data visualization, and may involve the use of Python libraries and tools such as Pandas, NumPy, and Matplotlib.
Module 1: Python Crash Course
- Introduction to the Course
- Environment Set-Up
- Virtual Environments
- Data types and Operators
- Integers, Floats, Strings, Bytes, Tuples and Lists
- Dictionaries and Ordered Dictionaries
- Sets and frozen sets
- Flow control - if, elif statements
- Flow control - while loops
- Creating and using functions
- Creating modules and packages
- Distributing code to repositories
Module 2: Python Object Oriented- Creating Classes
- Creating Objects and Instances
- Data Encapsulation
- Class Inheritance
- Multiple Inheritance
- Decorators
Module 3: 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
Module 4: Python Data Distribution- Introduction to Series
- Introduction to Pandas
- DataFrames
- Read From CSV
- Methods: head, shape, info, mean, median mode
- Histogram
- Methods: min, max, range, sqrt
- Methods: sorted, std, hist, correlation, heatmap
- Methods: skew, kurt, cov, quantile
Module 5: 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
Module 6: 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
- File IO
Module 7: Python for Data Analysis - Pandas- Introduction to Pandas
- Series
- DataFrames
- Missing Data
- Groupby
- Merging Joining and Concatenating
- Operations
- Data Input and Output
Module 8: Python for Data Visualization- Matplotlib
- Seaborn
- Distribution Plots
- Categorical Plots
- Matrix Plots
- Grids
- Regression Plots
- Pandas Built-in Data Visualization
- Plotly and Cufflinks
- Geographical Plotting
- Choropleth Maps
Module 9: Machine Learning- Introduction
- Machine Learning with Python
- Linear Regression
- Logistic Regression
- K Nearest Neighbours
- Decision Trees and Random Forests
- Support Vector Machines
- K Means Clustering
Module 10: Natural Language Processing- Natural Language Processing Theory
- NLP with Python
- NLP Project Overview
- NLP Project Solutions
Module 11: Neural Nets and Deep Learning- Neural Network Theory
- What is TensorFlow
- Installing Tensorflow
- TensorFlow Basics
- MNIST with Multi-Layer Perception
- Tensorflow with ContribLearn
- Deep Learning Project
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