Two of the Most Popular Uses of Data Science in 2022

Data science has become one of the most preferred career choices in 2022. A general uptrend in the demand for departmental data analysts can be noticed not just in India but throughout the entire world. Data analysis is an amazing tool for finding patterns in large data sets and taking informed decisions. The multifaceted benefits of data science have put businesses in an urgent position to move on to data-driven operations. The urgency is justified because failing to incorporate data analysis will put a company at a competitive disadvantage.

The rise in demand for data analysts has resulted in a move towards data-driven career choices for a large number of people. Working professionals see data science as a valid upskilling opportunity. This article will go over some popular uses of the general course structure of data science courses and provide a discussion on the challenges of finding the best data science courses in an increasingly crowded market.

Popular uses of data science and analytics 

Data science and analysis are being used for all kinds of things across the board. A better understanding of data will certainly help in making accurate decisions. 

  • Business Analytics 

Business analytics is arguably the most common use of data analysis in the modern business environment. Multiple types of analysis processes come together in the form of business analytics and aim to give a business an edge against its competitors. 

Business analysts generally come from a managerial background. The experience these people gathered during their work as managers played a vital role in becoming successful business analysts. Ultimately, data analysis will help find patterns and correlations between data sets. But only a person with experience in the industry can use those insights and turn them into business divisions that help the business immensely.

Business analysts generally follow a step-by-step and well-established data analysis method to achieve their goals. There are mainly four types of data analysis methods. Descriptive analysis, diagnostic analysis, predictive analysis, and prescriptive analysis Let’s discuss these four types of analysis methods in detail. 

  1. Descriptive analysis  

Descriptive is the most widely used data analysis method. Almost every business uses some form of descriptive analysis to analyse their performance on a quarterly or yearly basis. The results of descriptive analysis for a specific time period are compared with previous timeframes to gauge the general direction or overall health of the business.

  1. Diagnostic analysis 

Diagnostic analysis is an amazing tool for every business. Descriptive analysis is an incredible instrument for understanding and judging the general performance trend, whereas diagnostic analysis helps find the reason behind the performance. The advantages of figuring out what worked in the businesses’ favour and what worked against them are tremendous. It helps the company by providing valuable insights that can be used to increase the overall performance of the company to a greater extent.

  1. Predictive analysis  

Predictive analysis is where things get a little spicy. Only the most advanced and adept analysts are capable of creating machine learning algorithms according to the specific needs of the business. The algorithm is then fed increasingly larger data sets in order to improve the final results’ accuracy.The sole purpose of a predictive analysis is to give an accurate prediction about future performance or events that directly or indirectly affect the business. Python data science course is  a must for understanding and learning the necessary concepts of mahine learning and deep learning. 

  1. Prescriptive analysis 

Prescriptive analysis is considered to be the most sophisticated form of data analysis. In prescriptive analysis, the goal is to find actionable insights from the analysis. Analysts use deep learning algorithms to analyse huge data sets and find patterns between the sets. A business analyst is capable of deciphering the insights in a way that best suits the company’s interests. Python data science course is necessary for learning deep learning algorithms.

In order to become a business analyst, a person not only needs to have superior analytical skills and experience, but they should also have some other important skills, such as leadership, time management, and communication skills. Without these, a person cannot become a successful analyst.

  • Hedge fund management and trading

Hedge fund managers are arguably some of the highest-paid individuals in India and around the world. Hedge fund managers are tasked with running hedge funds that manage people’s money in the capital market. Today, data analysis has become one of the most crucial aspects that define the success of any hedge fund company. 

A great benefit of the stock market is that historical data is available to everyone completely free of charge. A trader can only become profitable if they have a strategy that works. But how does one go about finding the strategy that has the highest probability of working? This is where data analysis comes in, as data analysis allows the person to backtest their strategies and find out if the strategy is viable when put against the backdrop of historical data. 

There are a variety of trading methodologies. Some of the most preferred methods are intraday trading, swing trading, positional trading, and F&O trading. Let’s discuss these and find out how data analysis helps in becoming successful in them. 

  1. Intraday trading  

In intraday trading, all the positions are squared off before the market closes. It is one of the most dangerous trading strategies.The renowned hedge funds have sophisticated algo-trading software that can trade on behalf of the hedge fund and according to the specific strategies they have created. Once again, every strategy requires a rigorous back-testing process that proves that the strategy is capable of generating positive returns in the long run. 

  1. Swing trading  

In swing trading, the trade is generally squared between 2 and 5 days, but there can be exceptions to this. Swing trading, like intraday trading, necessitates a foolproof strategy capable of generating consistent returns.Even though the risk associated with swing trading is lower than that of intraday trading, it is still a high-risk venture. The hedge funds not only use data analysis to create trading strategies but also use it to find the probability of a trade going in their favour or against them. Having insights from a rigorous analysis process allows them to fine-tune their strategy, and the probability of risk allows them to select their position sizing according to the overall risk on any given trade.

  1. Positional trading  

Positional trading is considered to be the safest of all trading methodologies. Positional trading is less about trading and a lot more about investing because the timeframe of a trade can be as high as 5 years or more. The long nature of this type of trading means that only technical analysis will be enough to find consistent profits. An in-depth analysis of the fundamentals of any given company becomes a crucial part of positional investing. Therefore, hedge fund companies deploy data analysis to analyse the fundamentals of a company and compare it with other successful companies in the same sector. It gives them a general idea of the overall health of the company. 

  1. Futures and options trading 

Futures and options trading is the most dangerous type of trading. In it, the hedge fund trades derivatives of a security with weekly and monthly expiries. The F&O market is a zero-sum game, which means a person only earns money when another person loses it. Some have even compared F&O trading with gambling, but the truth is that with proper strategy and the help of data analysis, it has become a lucrative option for not just hedge funds but also for a very small minority of retail traders.