Data Science With Python

You will learn how to use programming, mathematics, and domain expertise in data science with Python to extract insights and knowledge from data. You will gain skills in data manipulation, data visualization, statistical analysis, machine learning, and deep learning, and learn how to apply these techniques to real-world problems. You will learn how to load, manipulate, and clean data using Python libraries such as NumPy and Pandas, and how to create static, animated, and interactive visualizations using libraries such as Matplotlib, Seaborn, and Plotly. You will also learn how to perform statistical analysis using NumPy and Pandas and apply machine learning techniques using the Scikit-learn library in Python.

In addition, you will learn about deep learning, a type of machine learning that uses neural networks to process large amounts of data and make predictions, and how to build and train deep learning models using the TensorFlow library in Python. Throughout the course, you will work on real-world data sets and case studies to apply the concepts and techniques you are learning, and learn best practices for working with data, such as how to handle large data sets, choose the right tools and techniques for a given problem, and communicate your findings effectively. By the end of the course, you will have a solid foundation in data science and the skills to apply these techniques to a wide range of data-driven problems.

After completing this 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.

This Data Science with Python course is intended for learners who have basic python knowledge and wants to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data.

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: Error Handling and Testing

• Handling Exception
• Raising exceptions
• Writing tests cases
• Executing tests
• Checking code coverage by tests

Module 4: Working with Files and Directories

• Accessing different types of files
• File handling principles
• Creating and reading Files
• Updating Files
• Deleting files
• Text Files
• CSV Files
• Microsoft Word
• Microsoft Excel
• Regular Expressions
• Extracting data from text files using Regular Expressions
• Creating and deleting directories
• Listing and searching for files

Module 5: Accessing Databases

• Selecting Data
• Inserting and Updating Data
• Deleting data
• Generic database API based on MySQL
• Using the Object Relational Mapper (SQLAlchemy)
• Working with NoSQL databases

Module 6: 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 7: 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 8: Python for Data Analysis - Pandas

• Introduction to Pandas
• Series
• DataFrames
• Missing Data
• Groupby
• Merging Joining and Concatenating
• Operations
• Data Input and Output

Module 9: 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 10: 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 11: Natural Language Processing

• Natural Language Processing Theory
• NLP with Python
• NLP Project Overview
• NLP Project Solutions

Module 12: 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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