Introduction
Data science is an interdisciplinary subject that involves the knowledge of mathematics, statistics and programming to get started. In addition to this, it is also important to have knowledge of subjects like business intelligence, artificial intelligence, machine learning, data analytics and the like. Data Science as a discipline has completely transformed itself in the last few years. Python for Data Analytics has been successfully used by different data scientists to execute a large number of projects. The scope and nature of projects differs from the beginner level to the intermediate and the advanced level.
That said, it is essential that students are aware of the basics of exploratory data analysis because this acts as one of the basic prerequisites for different types of projects. With the help of exploratory data analytics, it becomes possible to make sense of data and derive significant insights from it. In addition to this, exploratory data analytics also helps in better analysis of the entire research problem which enables us to achieve the project goals and complete the project in time.
Let us briefly examine the different projects that are most commonly used in the domain of data analytics. These include sentiment analysis detection, detection of financial frauds, training of chatbots and the like.
1- Sentiment analysis as a basic project
Sentiment analysis is one of the most important machine learning projects that has numerous elements of data analytics involved with it. Strictly speaking, sentiment analysis is a natural language processing technique that is used to segregate data into three main types. The first type is positive, the second is negative and the third is neutral. If the classification is based on the overall opinion of the users, it helps in determining their feedback related to a particular theme or project.
This particular project is extremely helpful for determining the notion of the audience about a particular movie. In this project, the researchers may use a data set from an IMDB database containing thousands of comments and reviews about a particular movie. After this, it may be treated as a classification problem and reviews may be segregated into three primary types as described above.
2- Detection of disinformation
Detection of disinformation or fake news is one of the most popular projects in the domain of data science and data analytics due to its relevance in the present time. Disinformation keeps on spreading like wildfire on the internet. It is extremely important that such type of information is tackled at an early stage so that it does not cause any harm later on. It is also extremely important to counter the consequences of fake news at an early stage. For this, Python can be used as one of the most important programming languages for countering fake news. The libraries that we use in this project include Pandas and numpy.
Logic behind the project
The logic behind the project is to come up with an algorithm that helps in matching the disinformation with the authenticity of the source website. A comparison can be made between a particular article, the authenticity of which is doubted and the news website that sources authentic news. If we find that the original article differs from the news articles of the source website, we may classify it as fake news. We may use a counter article to tackle this piece of fake news by running it along with the fake news which has already been circulated.
3- Detection of financial frauds
The two projects that we mentioned above are usually suitable for beginners or data scientists and data analysts who are at the intermediate level of their training. For professionals, the project of financial fraud detection is quite suitable. This particular project is very challenging because it involves very large data sets and even sensitive information of customers and credit card companies around the world. In the present time, there are certain gaps in the financial system that are exploited by fraudsters so that they can take advantage of these loopholes and cause losses to the customers.
Getting started with the project
To get started with this project, the first thing that you need to do is the analysis of the transaction history of the customers. You can make use of different variables and parameters like location to determine the instances of financial fraud. For instance, we may set up an alert that lets us know if a particular transaction is being carried out at a location that is different from the locations in which the user carried out the previous transactions. The libraries that we make use of in this project include Pandas, numpy and scikit learn. Different types of algorithms that find application in this particular project include support vector machine, decision tree and random forest models.
4- Training of chatbots
Training of chatbots is also one of the complex projects that involves the comprehension of human conversation with the help of different commands. Chatbot technology relies on artificial intelligence and machine learning methodologies to make sense of the voice commands that are fed into the system.
The system develops a temporary memory so that different commands can be stored and later on utilized depending upon the need. As a chatbot processes more and more information, the prospects of training improve and the accuracy to respond to conversations also increases.
Different types of chatbots find applications for domestic purposes like smart assistance, switching off lights, starting particular devices and smart gadgets. Much more advanced chatbots are used in application domains like telecommunication and giving quick response to customers through an online grievance redressal system.
5- Customer recommendation system
Customer recommendation system is also one of the most advanced data analytics projects that makes use of the programming language of python. In this project, the data set is fed into the system based upon the previous browsing history of the customer.
We may also supplement this data set by sourcing data from the transactions that the customer has performed previously. The basket of products that a customer is interested in is sketched out with the help of advanced algorithms.
The customer recommendation system proposes the products to the customer and simultaneously updates the data in the database with the help of new products that a customer may browse in future. This automatically updates the entire product recommendation system that is aligned with the interest of the customers.
The customer recommendation system has enormous applications in the domain of e-commerce and the marketing potential and prospects of the e-commerce firm is directly proportional to the strength of its product recommendation system.
The way ahead
There are a large number of projects that can be executed with the help of python and data analytics. It is extremely important to cherry-pick the perfect projects depending upon the level of expertise and application domain. Apart from the above mentioned ones, other prospective projects in the domain of data analytics include processing of sales data, insurance pricing forecast, stock market analytics, monitoring of airline traffic and analysis of carbon dioxide emissions.
The bottom line
All these projects are highly application oriented and have great commercial prospects as well. To get started with the above mentioned projects, it is extremely important that you inculcate the knowledge of the programming language of python at an early stage.
