Python for Data and Finance

Python for Data and Finance is a comprehensive course with an emphasis on the practical application of Python to data analysis. Initially developed for Wall Street professionals, this course quickly became popular among people who crunch numbers, analyze big data and simply want to switch from spreadsheets to the faster and more efficient Python programming language.

Starting from scratch we will build a foundational understanding of the Python programming language. Afterwards we will gain understanding of NumPy and Pandas, Python libraries heavily used in data analysis and finance. Finally, we will cover Matplotlib, an essential tool for data visualization.

WHO SHOULD ATTEND:

The main goal of the Python for Data and Finance course is to acquire a modern tool for data analysis and shift from spreadsheets to Python. We spend a great deal of time on gathering data from different sources like Excel, the Web and APIs and manipulating information to solve real-life challenges. During this course we use practical examples, so you can apply Python to daily tasks at work. We are proud to say that many financial giants have used Python for Data and Finance to give their workforce a competitive edge.

What to expect from this program: 

Fundamental introduction to Data Science using Python programming language, practical application of different statistical, analytical and linear algebra models to a variety of data science projects, and feeling comfortable enough to apply acquired knowledge on your own seeking a junior data scientist position.

You will learn: 

• Discover best practices for data analysis and start on the path to becoming a data scientist 
• Learn and practice essential tools for data analytics: NumPy, Pandas and Matplotlib 
• Learn to find solutions to problems by analyzing data using appropriate tools 
• Master your analytical skills by working on real life projects 
• Implement the core Data Science techniques of Linear Algebra, Probability, Gradient Descent, and Linear Regression 
• By the end of this course, you will have a Data Analytics Project to present to potential employers 

Preparation: Laptop

Python for Data Science Syllabus:

Session 1

• Variables
• Data types: strings, integers, floats, lists
• Mutability
• Control Flow statements
• If statements
• For loopsPractical
• Exercises 

Session 2

• Functions
• Data types: tuples, dictionaries, sets
• While loops
• Indexing and slicing
• Reading from CSV and TXT Files
• Writing to CSV and TXT Files
• Analyzing a File’s content
• Practical Exercises

Session 3

• Scientific computing with Python
• NumPy Arrays
• Creating and manipulating NumPy Arrays
• Computation on NumPy Arrays
• Broadcasting and UFuncs
• Sorting and Indexing NumPy Arrays
• Practical Exercises

Session 4

• Python Data Analysis Library - Pandas
• Pandas Data structures
• Aggregating data in Pandas
• Data Indexing and Selection
• Logic, Control Flow and Filtering in Pandas
• Aggregation and Grouping
• High-Performance Pandas
• Visualization with Matplotlib
• Line Plots, Scatter Plots and Histograms
• Customizing Plots
• Final Project