Course Detail
Data Science using Python Course
Around 2008 people started hearing term “data scientist”, this term has been used to cover a wide range of functionalities. But data science at the core, is the use of various tools, algorithms, and techniques to identify hidden patterns in large volumes of data. Hence, Python is the top language to work in such scenarios, so, this creates the perfect combo of Python and Data Science.
Python Content
Core Concepts
- Python language characteristics
- The Python execution model
Leveraging Python Built-in Types
Manipulating string and numeric literals
- Declaring and initializing variables
- Performing arithmetic calculations
- Making decisions and performing iterations
- Formatting and slicing strings
Aggregating related data
- Accessing positional information in lists
- Representing ordered data with tuples
- Consistently handling data collections with iterators
Organizing and Structuring Code
Defining and calling functions
- Positional, keyword and default arguments
- Implementing variable-length argument lists
- Iterating with generator functions
Grouping code into modules
- Importing and reloading modules
- Referencing functions from modules by qualification
- Combining modules into packages
Implementing Classes and Objects
Declaring and modifying objects
- Encapsulating attributes and methods in classes
- Initializing objects with constructors
- Accessing derived data with properties
- Overloading operators
Inheritance and polymorphism
- Reusing functionality through inheritance
- Extending methods from base classes
- Overriding methods for dynamic behavior
- Tracing the scope in the namespace
- Enhancing functionality with class decorators
Manipulating the File System
Managing files
- Reading and writing text and binary files
- Importing the OS module for directory management
Increasing program robustness through handling exceptions
- Maintaining program control with error handlers
- Detecting errors and raising exceptions
Interfacing with Relational Databases
Establishing communication
- Creating a SQL database connection
- Instantiating cursors to access a database
Executing SQL statements within a Python program
- Retrieving desired data sets
- Updating the database with action statements
Constructing a GUI with Tkinter/Any
Building the user interface
- Defining a window layout
- Placing widgets
Listening for interface events
- Providing menu items
- Responding to mouse clicks
- Binding event handlers
Data Science Content
Intro Data Science
Data science related Libraries
Panda
- Intro & installation of panada Library(Module)
- Basics of the Pandas library
- How to import data using Pandas: Read data into Python
- How to write data using Pandas
- Perform data analysis and manipulation using Pandas:
- Select columns and rows in Pandas
- Manipulate columns in Pandas - Rename columns, sort data in Pandas dataframe, binning data using Pandas, etc.
Numpy
- intro & installation of Numpy library(module)
- Python List vs numpy array
- The Shape and Reshaping of NumPy Array
- Expanding and Squeezing a NumPy Array
- Indexing and Slicing
- Stacking and Concatenating
- Maths with NumPy Arrays
- Sorting in NumPy Arrays
- NumPy Arrays and Images
Matplotlib
- intro & installation of matplotlib library(module)
- Bar Graph
- Pie Chart
- Box Plot
- Histogram
- Line Chart and Subplots
- Scatter Plot
Linear Regression
Logistic Regression
Time Series
Final project and lots of real-life assignments and tasks