Course Description
This course is designed for developers with experience in other languages who need to get up to speed on Python. At the end of this course, the students will know the essentials of the Python language, how to use Python’s module system to structure code, and how to approach the development of Python programs.
We’ll cover the core Python language and the standard library in detail. This data science with practical approach covers various skills including text manipulation, modular programming, working with and retrieving data, interacting with files on your computer, and using some of the more popular third-party libraries (and getting them installed when and where we need them).
Importance of the Course
- Understand how to install and use Python
- Understand the fundamental Python programming techniques and tools.
- Discover the history of Python and how it compares to other programming languages.
- Discuss its applications and the types of problems it can solve.
- Analyse the Natural Language Processing.
Curriculum
Module 1: Python – Introduction, Tools & Syntaxes
Module 2: Numpy ,Pandas and Google Colab Notebook
Module 3: Introduction to Matplotlib and Scikit-Learn
Module 4: Keras , SVM Algorithm and Artificial Neural Networks
Module 5: Capstone Project
Module 1: Python – Fundamentals, Tools & Syntaxes
Lesson 1: Python Programming – Fundamentals
Types of Programming Languages – Advantages of Python – Installation of Python – Data types &Operators in Python – Numerical Operations on Python – String Operations on Python – Loops in Python
Assignment 1: Swapping of Numbers Assignment 2: Password Authentication
Go the Extra Mile – Try Looping within a Loop for any specific operation
Lesson 2: Python – Tools | Syntaxes & Data Structures
Python Lists & Tuples – Functions – Conversions – Arguments – Function Calls – Void statements – Functions with Multiple Arguments – Building Custom Functions in Python
Assignment 3: Palindrome Assignment 4: Tower of Hanoi
Go the Extra Mile – Write a python program that takes a list of strings as an input and using while loop, display the strings in the descending order.
Module 2: Numpy ,Pandas and Google Colab Notebook
Lesson 3: Pandas library
Pandas Introduction – Features Of Pandas – Pandas Data Structures – Series and DataFrames with examples – Operations on a DataFrame – Panels
Lesson 4: Numpy library
Introduction – Pre – Requisites – Array operations – Array objects – Datatypes – Array attributes – Flags – Indexing and Slicing – Array manipulations – Transpose Operations – String Functions – Mathematical functions.
Assignment 5: 1.Write a program to perform the following operations using numpy:
- Create an array.
- Create an array filled with ones.
- Create an array filled with the same value throughout the entire array.
- Print a range of numbers from 20 to 30.
Write a program to create a data frame using pandas library which contains the names of different students and their corresponding ages.
Go the Extra Mile – Read a csv file using pandas and then calculate the sine of the angle for various values contained in the csv file using numpy.
Lesson 5: Google Colab Notebook
Google Colab Introduction – Colab Notebook Creation – Various Colab Operations – Markdown Examples – Google Colab Functions – Mounting Drive –
Colab – Graphical outputs – Function list – Colab Magics – Colab – Adding Forms – Various form operations.
Assignment 6: a. Explain how to use Google Colab and write a simple python program using google colab.
Add a form and illustrate what are the different operations that can be performed using forms.
Go the Extra Mile – Use the various functionalities of Google Colab and how we can develop a project using any one of the ML algorithms.
Module 3: Introduction to Matplotlib and Scikit-Learn
Lesson 6: Matplotlib Library
Matplotlib – Dependencies – Features – PyPlotAPI – Types Of Plots – Image Functions – Axis Functions – Figure Functions – Simple Plot Example – PyLab Module – Object Oriented Interface – Figure Class – Axes Class – Color Codes – Marker Codes – Line Styles – Multiplot – Subplot() – Subplot2Grid() – Grids – Formatting Axes.
Assignment 7: a. Write a program to create a bar plot which takes states along the x-axis and population along the y-axis.
Write a program to create a histogram using Matplotlib.
Go the Extra Mile – Read a weather dataset(csv file) , create a subplot using Matplotlib and then create a density plot for various attributes present in the dataset.
Lesson 7: Scikit – Learn
Introduction – Pre – Requisites – Installation – Features – Modelling Process – Dataset Loading – Splitting the Dataset – Sample Example.
Assignment 8: Write a program to load a dataset using sklearn and split the data into training data and testing data using sklearn.
Go the Extra Mile – Take any real time project and then illustrate how sklearn can be employed in order to evaluate the performance of the model.
Module 4: Keras , SVM Algorithm and Artificial Neural Networks
Lesson 8: Keras
Keras – features and Benefits Of Keras – Installation of Keras and TensorFlow – Keras Layers – Keras Layers Example.
Assignment 9: Employ Keras models and how it can be used to predict the crop yield.
Go The Extra Mile : Illustrate how Keras Models can be employed in predicting the sales of a company.
Lesson 9: SVM Algorithm , ANN
SVM – Working Of SVM – SVM Concepts – SVM Kernels – SVM Example – ANN – Types Of ANN – Working Process Of ANN
Assignment 10: a. Write a program to predict the stock market prices using LSTM RNN.
Write a program to predict liver disease using SVM algorithm.
Go the Extra Mile – Explain how the medicinal value of plants can be evaluated with the help of neural networks and evaluate the performance of the neural network.
Lesson 10: Neural networks , CNN
Neural networks – Implementation Demo – CNN Algorithm – Implementation Demo – Image Classification Using CNN
Assignment 11: Medical Image Classification using CNN.
Go the Extra Mile – Employ various neural network techniques and develop a model in order to prevent any frauds happening in banks.
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I would like to learn Data science