The main takeaway of the course will be explained, and the capabilities of Python will be described using a business problem.

You will learn how to install Python and PyCharm IDE on Windows and Mac. Also, you will be learning how to run a sample Python program.

You will learn to create variables various data types available in Python.

You will learn various available operators in Python such as arithmetic, relational, logical, etc.

You will learn how to write a decision-making program using Python. You will also learn to iterate an array of data using various loops.

The string is an essential component in any programming language. You will learn how to use string and built-in string functions in Python.

The list is an array in Python. You will learn to create lists in Python. Dictionary is key-value pair storage, which is equivalent to JSON format. A tuple is also like an immutable List. You will learn about Dictionary and Tuple in this section.

In this section, you will learn to write independent functions in Python Programming, including various concepts such as function with parameter, call by reference, call by value, variable-length argument, lambda functions.

In this section, you will learn what class and object are, how to create classes and objects, constructors, generate constructors inside a class, generate function inside a class, and the difference between class function and independent function.

You will learn how to inherit the property of a class to another class using the inheritance concept. While one works with logic, there is a chance of getting logical errors; these errors will not appear during compilation. Instead, they will appear only in run time. These run-time errors are called an exception. The exception will terminate a program abnormally. You will learn how to handle these exceptions without closing the program abnormally.

This section will learn to connect to MySQL Database and pass custom MySQL queries using Python.

Data are available in various formats such as CSV, Spreadsheets, Text Files, JSON and Database, etc. You will learn to read these file formats and make them proper row and column structures using pandas and NumPy Libraries.

This section will learn to create your data frame without reading data sources.

While reading data using pandas, the data is not suitable for addressing business problems. We have to convert data to match our needs using data cleaning and transforming techniques.

We will not be getting data in structured data; in many instances, we will get it in logs and text formats. This section will learn to convert these unstructured data using pandas regular expression libraries with a sample project.

This section will learn various available join types: inner join, left outer join, right outer join, and full outer join.

In this section, you will learn to group data and apply aggregation functions to the grouped data.

This section will learn to create Bar, Pie Chart, Scatter Plot, Box Plot, and Histogram using Matplotlib Library with real-time data.

In this section, candidates will learn Spyder, how to download and install in Python, and how to use Spyder for data science.

In this section, candidates will be taught what data science is, how to get insights from the data, understand machine learning, how it works, and the role of a data scientist. Ideally, candidates will be getting a big picture of data science, machine learning, and applying the machine learning concepts in data sets.

Before applying Machine Learning algorithms on a data set, it is mandatory to perform data cleansing, including fixing missing values, outliers, categorical data manipulation, and feature scaling. Furthermore, candidates will acquire knowledge and know-how of splitting data into train and tests.

In this section, candidates will learn what regression, when and where the regression algorithms can be applied, various regression algorithms such as simple linear regression, multiple regression, support vector regression, polynomial regression, random forest and decision tree is. Candidates will learn how to choose predictor and dependent variables and create a machine learning model on a data set. Furthermore, candidates will learn to evaluate machine learning models using machine learning error metrics.

In this section, candidates will learn what classification is, when and where the classification algorithms can be applied, various classification algorithms such as logistic regression, support vector machine classification, Naïve Bayes classification, K-Nearest Neighbours (K-NN) algorithm, random forest and decision tree. Candidates will learn how to choose predictor and dependent variables and create a machine learning model on a data set. Furthermore, candidates will learn to evaluate machine learning models using machine learning error metrics.

In this section, candidates will learn what clustering, when and where the clustering algorithms can be applied, various clustering algorithms such as K-means Clustering and Hierarchical Clustering is. Candidates will learn how to choose cluster size. Furthermore, candidates will learn how to evaluate machine learning models using machine learning error metrics.

In this section, candidates will learn how to build a market basket machine learning model. Furthermore, candidates will learn to evaluate machine learning models using machine learning error metrics.

This section will teach candidates how to build a machine learning model for scenarios with minimal data sets.

In this section, candidates will learn to gather features from the text files and create a sentiment analysis model.

In this section, candidates will learn to choose the suitable feature (variable) while building a machine learning model.

In this section, the trainer will explain the business problem statement and the various analytical solutions for the same business problem statements. Data for the discussed business problem and the list of analytical questions will be shared.

In this section, candidates will learn how to evaluate machine learning models using machine learning error metrics such as K-fold cross-validation, and Grid Search techniques.