Machine learning and Data Analytics

The Role of Machine Learning in Data Analytics

Data Analytics

The Role of Machine Learning in Data Analytics

Machine learning has started playing a crucial role in data analytics. Even though machine learning has been sound for some time, it has only recently become mainstream. It wasn’t easy to use earlier because it was highly processor-intensive and required an advanced skill set. Machine learning has become more accessible with the developments in cloud computing. Using machine learning is now easier and cheaper. More and more companies are incorporating it into their daily operations, and data analytics is one of them.

Today, businesses are more reliant on data than ever before. Data has become the foundation on which companies create strategies and manage their revenue. With such an emphasis being placed on data, it is natural to want the process of data analysis to be streamlined. Machine learning has the potential to make data analytics substantially easier by automating various processes and aiding in data processing.

Difference Between Machine Learning and Data Analytics

Machine learning and data analytics are different fields that can be combined to achieve great results. Data analytics is the process of discovering, interpreting, and communicating the data. Analytics help in the generation of data from multiple sources. The insights generated from this are used to make various business decisions. From analyzing customers to the efficiency of workflows, data analytics can help companies reduce their costs and improve efficiency.

Machine learning, on the other hand, is one of the applications of Artificial Intelligence. It is used to enable computers to learn and improve the user experience without the need for explicit programming. Machine learning can be used to create programs that keep updating themselves. We can provide historical data to these models and train them accordingly. Then, these models become capable of making predictions and drawing conclusions from the data provided to them. These models can also be used for new data sets to get insights better suited to the present day.

Data Science vs Data Analytics

When people talk about data analytics, they often confuse it with data science. People often use these terms interchangeably without realizing what the actual difference is. Knowing the meaning of data science is crucial to understanding what data analytics is.

Data science is more like a discipline. It is an umbrella term comprising various concepts such as data analytics, machine learning, data mining, etc. Data science is more concerned with forecasting future trends based on previous patterns and trends. On the other hand, data analytics deals with analyzing the data and creating meaningful insights from them. Data science is more about asking questions, while data analytics is more about finding the answers.

Work of a Data Analyst

To better understand the role of machine learning in data analytics, it is essential to know what a data analyst is. Knowing the various responsibilities of a data analyst will give you a better understanding of how machine learning can help streamline these tasks. Refer to the points below to understand what kind of work a data analyst does.

  • Gathering the Data: Analysts have to collect the data by using various methods. These can include conducting surveys, tracking characteristics of users visiting the company website, or buying data sets from others.
  • Cleaning the Data: Just gathering the data is insufficient, as raw data often contains errors, duplicates, or outliers. Hence, it is essential to clean the data one has gathered. The data is cleaned by maintaining its quality in a spreadsheet or through a programming language. This will ensure that your interpretations are accurate and have fewer chances of being wrong.
  • Modeling of Data: Creating and designing the structures of a database is known as data modeling. In this part of the work, one can choose the data types one wants to store and collect. One can also establish categories and define their relationship.
  • Interpreting the Data: This is the most critical work of a data analyst that involves finding patterns that can help you solve a problem and answer the questions posed by data scientists.
  • Presenting the Data: It is important to show what you have found through your analysis. Presenting the data is a crucial task and should be taken seriously. One can use visualization tools like graphs or write their findings in a report. However, it is important to ensure that the data is comprehensible to all the parties involved.

Skills Required to be a Data Analyst

Being a data analyst will require you to have certain skills. They are specialized jobs that require a specific skill set. To become a data analyst and perform your job well, you need to possess the following skills:

  • Should be adept at mathematical statistics
  • Should know about data wrangling
  • Should have an understanding of PIG/ HIVE
  • Should have a comprehensive knowledge of Python and R

Ways Machine Learning Helps in Data Analytics

Now that we know all the basic details about data analytics, we can understand how machine learning is helpful in it. The role of machine learning in data analytics is paramount. Check out the many ways in which machine learning assists data analytics below.

  • Predictive Analysis – Machine learning tools are trained to give accurate predictions using historical data. With the use of machine learning, data analytics can also create predictions based on the data sets that are available to them. Data analysts can use machine learning to create predictions that can help make businesses smart business decisions that enhance profits and reduce risks.
  • Recognizing Patterns – Data analysts perform various functions that enable them to identify patterns and create conclusions. Machine learning can help data analysts make sense of complex data sets that are large in size. This can help them make detailed inferences and understand underlying trends and patterns.
  • Detecting Anomalies – Cleaning data to get rid of inaccuracies is an important part of a data analyst’s job. Machine learning can help with this significantly. With the help of machine learning, one can identify and get rid of anomalies, errors, or outliers quickly.
  • Automation – Machine learning is known for its automation capabilities. With its help, data analysts can automate the processes such as data cleaning and data processing. This can result in a process that saves time, money, and effort. With the help of automation, more time can be spent on the interpretation of data instead of doing repetitive tasks.
  • Data Segmentation – Machine learning is also helpful in segmenting data. One can divide data into specific groups based on patterns that it identifies. Different segments offer better and more personalized approaches to identifying, understanding, and applying data.
  • Boosting the Productivity – Machine learning can help boost the productivity of data analysts as well. Data generation, cleaning, and grouping can be processes that can be automated. This can save time for the analysts, which they can use for other tasks that need more human insights. This will improve their productivity and overall outcome.

To Sum Up

Undoubtedly, there are various ways in which machine learning helps with data analytics. Machine learning tools can transform the way data is used to create predictions that are useful for businesses. From creating strategies that can boost revenue to mitigating risks that can cause losses, machine learning can help companies achieve better results. It can also make the work of a data analyst easy and help them be more productive. There is no denying the fact that machine learning plays a crucial role in data analytics. If you are also considering incorporating machine learning into your business, go for it without hesitation! It will surely reap great results!

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