18 and older
Data Science Immersive is a week-long comprehensive course with an emphasis on the practical application of Python to data analysis. In the first two days we cover Python's built-in data types, explaining differences in the behavior of data structures, laying a foundation for the more complex NumPy and Pandas structures.
A deep understanding of data types prepares users to solve real-life challenges with the right tools in the most efficient manner. Also, it is important to understand why some of the types are faster and some are just an extension of others.
In this course we are not covering in-depth mathematical and statistical concepts, rather we master practical usage of the Python programming language and its extensions NumPy and Pandas.
During this course we solve many real-world financial, statistical, and analytical problems. We are always open to new challenges and asking aspiring programmers to bring their projects so we could solve them together.
When you sign up for this course, you should also be prepared to work hard. While we do explain all of the concepts thoroughly and have notes on each lesson that you can refer back to, we follow up each lesson with practice problems to reinforce the material. We code almost seven hours a day with breaks for lunch and coffee.
At the end of this course, you will have a fundamental understanding of the Python programming language and its analytical libraries: NumPy, Pandas, and Matplotlib. You will learn the best practices and understand how to write clean and efficient Python code. Also, you will gain essential knowledge of data structures needed to solve financial, analytical, and statistical problems in a fast and efficient manner.
Moreover, you will have a portfolio of sample code solutions done in a professional style.
Data Science Immersive Syllabus
- Built-in data types: strings, integers, floats, lists
- Introduction to built-in functions
- Behavior of Data Structures
- Control flow statementsIf, Elif, Else statements
- Definite loops: For loopsPractical exercises
- How to write custom Python functions
- Built-In data types: tuples, dictionaries, sets
- Indefinite loops: While loops
- Indexing and slicing
- Reading data from CSV and TXT Files
- Writing to CSV and TXT Files
- Analyzing a file’s content
- Practical exercises
- List comprehensions
- Scientific computing with Python
- NumPy arrays
- Creating and manipulating NumPy arrays
- Computation on NumPy arrays
- Broadcasting and UFuncs
- Sorting and Indexing NumPy arrays
- Aggregating data in Pandas
- Data Indexing and Selection
- Logic, Control Flow and Filtering in Pandas
- Grouping by for analytics
- Combining datasets and Merging datasets
- High-performance Pandas: Eval and Query
- Hierarchical indexing
- Handling missing data
- Python Data Analysis Library - Pandas
- Pandas data structures
- Practical exercises
- Web scraping and Data Mining
- Visualization with Matplotlib
- Line Plots, Scatter Plots and Histograms
- Customizing Plots
- Multiple Subplots
- Density and Contour Plots
- Python best practices
- How to write fast and efficient code – Big-O notation