Data Science with Python

In this module, you will come across various histograms and distributions created with the help of the Matplotlib library. Moving further, the tutor talks about how linear regression is connected with discrete and continuous data. Lastly, you will understand some use cases of analytics in organizations. 
 

This module will brief you on the use of the box plot function of the Seaborn library in Python. You will understand different parts of the box plot visualization, such as the median, upper quartile, and lower quartile. Next, you will learn to modify the box plot using its syntax. 

The next module will help you to understand how summarization is done using the crosstab function of Pandas library. Next, you will go through some examples where you needed summarized data as output. Lastly, you will familiarize yourself with the code used to summarize the data. 

The module briefly discusses the pair plot function of the Seaborn library. You will learn to implement this function and what type of visualization will be your output for the data. The tutor briefs about the univariate analysis used in this pair plot visualization. Lastly, you will understand how the histogram is plotted using the pair plot function. 
 

This section introduces you to different types of data by helping you understand their graphical representations. It also explains its different components. 

This section demonstrates working with the Jupyter notebook to derive various statistical plots like distributional plots with real-time examples. It also explains the derivations and answers to questions like why the raw data is insufficient. You will also learn various uses of these plots. 

The module starts with a discussion of bivariate variables in statistics. You will understand the relationship between two variables in a distribution. Next, the tutor will give you the formula to find variance and covariance that helps to derive the nature of the relationship between variables. Further, you will learn to find correlations in the distribution function. 

This module will conceptualize descriptive statistics, including univariate, bivariate, and multivariate distribution. Next, you will understand the prediction and actuality given by the algorithm. Moving ahead, you will. Get familiar with a key concept – Elasticity of Demand. Lastly, you will learn how linear regression works in any distribution. 
 

You will learn how to install Python and Pycharm IDE to get started with this programming language in this module. Next, you will get to know the installation steps of Anaconda Software. Lastly, you will learn to code your first program in Jupyter Notebook. 
 

In this module, we’ll learn about a reserved memory location to store values called a python variable. A Python variable is a symbolic name that is a reference or pointer to an object.

Variables are associated with datatypes. This module will brief you about different data types in Python, such as int, float, Boolean, and string. In the end, you will see the implementation of different data types in Python programming. 
 

Python have three operator types: relational, arithmetic, and logical. The tutor will help you better understand these operators with the help of some examples in Python. 
 

This module begins with an introduction to Python strings. You will learn to implement Python strings in 3 different ways in the Jupyter notebook. You will also be familiarized with some inbuilt string functions of Python. 
 

A tuple is a data structure in Python used to store multiple values in a single variable. In this module, the tutor will brief you about tuples in Python and its coding demonstration. 
 

The list is another functional data structure in Python used to store an ordered collection of elements. The module will familiarize you with the use of lists in Python. Lastly, the tutor will help you learn some valuable functions of lists. 
 

Dictionary is an unordered collection that contains key-value pairs in it. You will learn to use the dictionary to store and define values to the key you declared in this module. The tutor will explain how to access the values using the keys from the dictionary. 
 

Set is also a data structure in Python used to store an unordered and unindexed collection of elements. The module will brief you about sets in Python with a practical demonstration of its use in programming. 
 

Flow control statements provide great use in programming where different situations occur. The tutor will help you understand IF statements in Python followed by Else conditions. You will learn to give various if conditions in Python. 
 

Looping statements are helpful for repeating a task. While and Loop are the two types of looping statements in Python. You will learn to use the while loop with practical implementation in Python programming in this module. 
 

For loop is used when a function needs iterated statements or performs tasks multiple times. In this module, the tutor will brief you on the syntax of For loop with an example. 
 

Programming in Python allows you to create your own functions or use pre-defined functions. Functions help perform specific tasks. The tutor will help you understand functions in Python with a practical demonstration. 
 

The module starts by explaining classes and objects in Python. You will understand the properties and behavior of classes and objects. Next, you will learn about objects in Python that are nothing but instances of classes. 
 

Inheritance means acquiring the properties of another class. You will learn to create an inheritance class that derives the features of the base class. 

You will be introduced to the Numpy package of Python in this module. You will learn to use this library to create single and multi-dimensional arrays using the Jupyter notebook. 
 

You will learn to use the NumPy shape function in this module. This function will help you to find the shapes of NumPy arrays. 
 

This module starts with some essential mathematical functions of Numpy arrays. The tutor will also brief you on implementing some arithmetic functions of Numpy arrays. 

There are three different modules to join Numpy arrays: vstack, hstack, and column stack. You will learn all these methods one by one in this module. 
 

In this module, you will be introduced to another Python package, Pandas. You will learn to use multi-dimensional data structures in Pandas with some examples. 
 

In this module, you will understand another important data structure in Python: Pandas DataFrame. A dataframe can be defined as a two-dimensional data structure that you will be using in this module. The tutor will brief some built-in functions of dataframe such as head(), shape(), describe(), and tail(). You will also see the implementation of these functions in the Python Jupyter notebook.
 

This module covers a package called Matplotlib, which is used to visualize the data using several visualizations such as bar plots, scatter plots, histograms, and others. You will learn to use some of these visualizations in Python. The tutor will also help you learn to make some modifications to the visualizations in programming. 
 

In this module, you will understand linear regression and the different models that incorporate linear regression models. The tutor will familiarize you with some concepts around linear regression. 
 

This module will help you learn different data frames for the data models described by the tutor. You will also be familiarized with some functions used in linear regression. 
 

In this module, the tutor will discuss the relationship between independent and target variables in the plane. You will understand some use cases where the target and independent variables are related to each other by some correlation. 
 

The module begins with an introduction to Supervised Machine Learning, where you will have to find the correlation of possible extreme values in linear regression models. You will also learn the relationship between variance and central value. Next, you will be familiarized with the coefficient of correlation and Pearson’s coefficient. 
 

In this module, you will understand two types of variance: stochastic and deterministic. Next, you will be introduced to quadratic equations. Further, you will learn several errors in the coefficient of determinant. Lastly, you will briefly understand the coefficient of determinant with some examples. 
 

Here in this module, you will understand some linear regression assumptions, such as the assumption of linearity and the assumption of normality of the error distribution. Lastly, you will understand various dimensions to plot the linear regression models. 
 

Here in this module, you will learn some advantages and disadvantages of a linear regression model. The tutor will give you some examples to help you better understand the concepts. 
 

This module will demonstrate you to pre-process the data using the Matplotlib library. You will be familiarized with data pre-processing steps using the Jupyter notebook. You will also learn the transformations of data before you make any visualization. 
 

The module is entirely based on the demo given on density graphs. You will be using the pair plot function of the Seaborn library to implement the various types of density graphs. Later on, the tutor will help you to recognize patterns in the graphs. 
 

You will be introduced to the statistics module, which contains some useful functions for our data analysis in this module. You will understand the importance of a z-score in standard deviation. Demonstrating the stats module will familiarize you with the statistical functions used in this module. 
 

The module will introduce you to logistic regression. You will learn to classify logistic regression with the help of a linear regression model. The tutor will show you the binary classification of this regression model that can also be used for multi-class classification. At last, you will learn to transform a line graph into a curved graph with the help of logistic regression classification. 
 

In this module, you will understand the sigmoid curve with the help of prediction and actuals drawn from an example. Later, you will see how a linear model is passed to a logistic function in supervised machine learning. At last, you will be introduced to logloss function of the logistic regression model. 
 

This module cover four cases of linear regression models wherein the tutor will brief each case in detail with examples. You will learn to use different equations for different classifications of logistic regression. The tutor will brief you about the logloss function in the latter part of this module. 

Moving ahead, you will understand the advantages and disadvantages of the logistic regression model. The tutor will brief each point in detail to make every concept clearer to you. You will also be given some supporting examples of its advantages and disadvantages. 
 

The last module is a hands-on demonstration of a logistic regression model to predict diabetes among Pima Indians. You will also get the data set in the course materials that you can use to practice along with the course. The tutor will brief you on the data set's attributes to help build this machine-learning model more easily in the demonstration. You will get exposure to different functions and transformations of the data set to get desired visualizations.