1. Understanding the computer architecture.
2. The Arithmetic and Logical Unit
3. Computer processors, clocks and machine code

The architecture of a computer is a hardware made of electronic components. The automation of the same is achieved by a piece of software which regulates and monitors the flow of information according to a standard operating procedure. In this section we will understand the layers of operating systems in the context of general architecture of the underlying hardware.

Data structures are the fundamental units for packing information. Data structures are common for all programming languages. Students will learn to appreciate the need for every standard structure of data such as bit, byte, arrays, lists, pointers, references, stacks, structures, dictionary, tuple and advanced Data Structures such as Linked-List, Stacks, Queues and Trees.

An Algorithm is a set of operations that is designed to get some task done. In the context of computation, the tasks are based on information flow, information manipulation and often, the task are designed in such a way that the computer will do certain tasks repeatedly to achieve the desired output. This section although is a part of advanced computation, will add to a broader understanding of how to write efficient programs and why certain kids of data structures are chosen above other structures to achieve faster and efficient outputs.

Time complexity and Space complexity will be understood in detail, making the student aware of practical issues which they might face in their professional careers as software developers.

Here will understand what a computer program actually does. How it communicates and interacts with the hardware. We will further build on this knowledge and realize the difference between different kinds of computer programming languages and why they are different in nature. This knowledge is important for every software developer for a confident approach to choosing a language for automation.

The OOP language enables the program to be broken down into smaller programs that can be readily solved (one object at a time). The new technology promises increased programmer efficiency, higher software quality, and lower maintenance costs. From smaller to big systems, OOP systems can be readily scaled. In this section we will understand the fundamental concepts of how a object is defined out of class and build the intuition to appreciate advanced concepts in object oriented programming.

Python is around from 20+ years and enjoy tremendous support from developers community. Python also has a very strong documentation available online. Students will be introduced to different platforms where answers to almost every error is found. It is believed that finding answers online is also a key technical skill of a developer.

Python's core data structures are list, set, tuples, and dictionary. Each data structure is distinct in its own manner. Data structures are "containers" that organize and categorize data. The mutability and order of the data structures varies.

1. Lists
2. Arrays
3. Tuple
4. Dictionary

1. Arithmetic operations in Python
2. Comparison Operators in Python
3. Boolean operators
4. Python Statements

1. How to define a function
2. How to pass function as an argument
3. Defining a Python class and object.
4. Hands-On exercise

Loops are useful in Python and other programming languages because they allow you to execute a block of code repeatedly. You will frequently encounter circumstances in which you will need to utilize a piece of code again but do not want to write the same line of code numerous times.

1. While loop
2. For loop
3. Nested loops
4. Difference between using a while loop and a for loop.
5. Using the break and continue keywords.
6. The difference between the xrange() and range() functions

Errors are issues in a program that cause the program to cease running. Exceptions, on the other hand, are raised when some internal events occur that disrupt the usual flow of the program.

Python has two sorts of errors.

1. Syntax errors
2. Logical errors (Exceptions)

1. Decorators
2. Generators
3. Generator expressions
4. Iterators
5. Closures

Assume you need to retrieve a huge amount of data from a website and you need to do it as soon as feasible. How would you do it if you didn't go to each website and personally collect the data? Web scraping is the solution. In this era where data is money, web scrapping is employed all over the companies to collect and further, this data is mined using natural language processing. In this section we will be learning some cool web scrapping techniques which will be handy for you in your python coding career.

Image processing with Python begins with manipulating images using PIL. PIL stands for Python Imaging Library, and it offers image editing capabilities to the Python interpreter. It was created by Fredrik Lundh and numerous other people. Pillow is a easy-to-use PIL fork and a handy library. We'll be collaborating with Pillow to do the following:

1. Open a particular image from a path
2. Retrieve size of image
3. Save changes in image
4. Image Rotating, Cropping, & Resizing
5. Pasting an image on another image
6. Getting a Histogram of an Image
7. Transposing an Image
8. Splitting an image into individual bands
9. Converting an image to an X11 bitmap (A plain text binary image format) using "tobitmap"
10. Creating a thumbnail
11. Get emotions of images using Microsoft emotion API in Python

Receive and send personalized emails and set up an automated system.

1. Why email automation?
2. Getting Started
   • Option 1: Setting up a Gmail Account for Development
   • Option 2: Setting up a Local SMTP Server
3. Sending a Plain-Text Email
   • Starting a Secure SMTP Connection
   • Sending Your Plain-text Email
4. Sending Fancy Emails
   • Including HTML Content
   • Adding Attachments Using the email Package
5. Sending Multiple Personalized Emails

• Make a CSV File With Relevant Personal Info
• Loop Over Rows to Send Multiple Emails
• Personalized Content
• Code Example

Graphical user interface (GUI) is often seen as a last phase of software development. Once your python code is ready for deployment, the developer has to cater to the fact that the user of this code will not always key in the inputs through the raw code. A GUI helps in creating the interface between the User and the raw code.

PySimpleGUI: The Simple Way to Create a GUI With Python

1. Introduction to PySimpleGUI
2. Installing PySimpleGUI
3. Creating Basic UI Elements in PySimpleGUI
4. Creating Simple Applications
5. Creating a PySimpleGUI Image Viewer
6. Integrating Matplotlib With PySimpleGUI
7. Integrating OpenCV With PySimpleGUI
8. Packaging Your PySimpleGUI Application for Windows
9. Conclusion

Matplotlib is an excellent Python visualization toolkit for 2D array charts. Matplotlib is a multi-platform data visualization toolkit based on NumPy arrays and designed to interact with the wider SciPy stack. John Hunter first used it in 2002.

One of the most important advantages of visualization is that it provides us with visual access to massive volumes of data in readily consumable images. Matplotlib has a variety of plots such as line, bar, scatter, histogram, and others.

1. Installation
2. Importing matplotlib
3. Basic plots in matplotlib
4. Line plot
5. Bar plot
6. Histogram
7. Scatter Plot
8. Violin plot in Python using axes class of Matplotlib

Seaborn is a Python module that is primarily used for statistical charting. It is built on Matplotlib and has stunning default styles and color palettes to make statistics graphs more visually appealing.

In this section, we will study Python-Seaborn from the ground up, utilizing a massive dataset containing seaborn basics, ideas, and graphs that may be displayed.

1. Getting Started

2. Using Seaborn with Matplotlib

3. Customizing Seaborn Plots

   • Changing Figure Aesthetic
   • Removal of Spines
   • Changing the figure Size
   • Scaling the plots
   • Setting the Style Temporarily

4. Color Palette

   • Diverging Color Palette
   • Sequential Color Palette
   • Setting the default Color Palette

5. Multiple plots with Seaborn.

   • Using Matplotlib

   • Using Seaborn

   • Creating Different Types of Plots

   • Relational Plots

   • Categorical Plots

   • Distribution Plots

   • Regression Plots

1. Introduction

   • Introduction to Pandas
   • How to Install Python Pandas

2. Creating Objects

   • Pandas DataFrame
   • Creating a Pandas DataFrame
   • Pandas Series
     • Creating a Pandas Series

3. Viewing Data

   • View the top rows of the DataFrame
   • View the bottom rows of the DataFrame
   • View basic statistical details
   • Convert the pandas DataFrame into a numpy Array
   • Convert the pandas Series into a numpy Array
   • Convert series or dataframe object to Numpy-array using .as_matrix().

4. Selection

   • Dealing with Rows and Columns in Pandas DataFrame
   • How to select multiple columns in a pandas dataframe
   • Python | Pandas Extracting rows using .loc[]
   • Python | Extracting rows using Pandas .iloc[]
   • Indexing and Selecting Data with Pandas
   • Boolean Indexing in Pandas
   • Label and Integer based slicing technique using DataFrame.ix[ ]

5. Manipulating Data

   • Adding new column to existing DataFrame in Pandas
   • Python | Delete rows/columns from DataFrame
   • Truncate a DataFrame before and after some index value
   • Truncate a Series before and after some index value
   • Iterating over rows and columns in Pandas DataFrame
   • Working with Missing Data in Pandas
   • Sorts a data frame in Pandas | Set-1
   • Sorts a data frame in Pandas | Set-2

1. Grouping Data

   3. Pandas GroupBy
   4. Grouping Rows in pandas
   5. Combining multiple columns in Pandas groupby with dictionary

2. Merging, Joining and Concatenating

   9. Python | Pandas Merging, Joining, and Concatenating
   10. Concatenate Strings
   11. Append rows to Dataframe
   12. Concatenate two or more series
   13. Append a single or a collection of indices
   14. Combine two series into one
   15. Add a row at top in pandas DataFrame
   16. Join all elements in list present in a series
   17. Join two text columns into a single column in Pandas

3. Working with Date and Time

   21. Python | Working with date and time using Pandas
   22. Timestamp using Pandas
   23. Current Time using Pandas
   24. Convert timestamp to ISO Format
   25. Get datetime object using Pandas
   26. Replace the member values of the given Timestamp
   27. Convert string Date time into Python Date time object using Pandas
   28. Get a fixed frequency DatetimeIndex using Pandas

4. Working With Text Data

   32. Python | Pandas Working With Text Data
   33. Convert String into lower, upper or camel case
   34. Replace Text Value
   35. Replace Text Value using series.replace()
   36. Removing Whitespaces
   37. Move dates forward a given number of valid dates using Pandas

5. Working with CSV and Excel files

   41. Read csv using pandas
   42. Saving a Pandas Dataframe as a CSV
   43. Loading Excel spreadsheet as pandas DataFrame
   44. Creating a dataframe using Excel files
   45. Working with Pandas and XlsxWriter | Set – 1
   46. Working with Pandas and XlsxWriter | Set – 2
   47. Working with Pandas and XlsxWriter | Set – 3

6. Visualization

   53. Pandas Built-in Data Visualization
   54. Data analysis and Visualization with Python | Set 1
   55. Data analysis and Visualization with Python | Set 2
   56. Box plot visualization with Pandas and Seaborn

1. Downloading the data that will be used in this module
2. Understanding MS SQL Management Studio
3. Uploading data and related short-cuts

1. Using SELECT statement to select data
2. Using WHERE statement to filter data
3. Using Wildcards/Regular expressions to SQL for advanced filtering and pre-processed filtering
4. Comments in SQL
5. Order By
6. Data Types in SQL
7. Implicit Data Conversion in SQL
8. Using Cast() vs Convert()
9. Working with NULLs
10. Understanding how LEFT, RIGHT, INNER, and OUTER joins work
11. Joins with duplicate values
12. Joining on multiple fields
13. Practicing Joins