Skip to main content

Can Artificial Intelligence(AI) can replace Data Analytics in future?


 Artificial intelligence (AI) has the potential to automate certain tasks that are performed by data analysts, such as data cleaning, feature selection, and model selection. However, data analysts also perform other tasks that are difficult to automate, such as interpreting results, identifying patterns, and communicating findings to stakeholders.


As AI technology continues to advance, it may be able to perform some of these tasks more effectively, but it is unlikely to completely replace data analysts. Instead, it is more likely that AI will augment the work of data analysts by assisting with the more repetitive and time-consuming tasks, allowing data analysts to focus on more complex and high-level tasks.


Additionally, the field of data analytics is constantly evolving, new techniques and technologies are emerging, and data analytics are required to stay current and continuously learn new skills, which an AI model can't replicate.


So, while AI may be able to automate certain aspects of data analytics, it is unlikely to fully replace data analysts. Instead, it is more likely that data analysts will work alongside AI to augment their abilities and make their work more efficient.

Comments

Popular posts from this blog

All about data analysis

  Data Analysis is the process of examining, cleaning, transforming, and modeling data to extract useful information, draw conclusions, and support decision-making. It involves a wide range of techniques and tools to collect, process, and interpret data, including statistical methods, machine learning, and visualization. The goal of data analysis is to turn raw data into actionable insights that can inform business decisions, improve operations, and drive innovation. Data collection is the first step in data analysis. This involves acquiring data from various sources, such as databases, surveys, and experiments. The data can be structured or unstructured, and it can be numerical or categorical. Once the data is collected, it must be cleaned and preprocessed to ensure that it is accurate and relevant. This process involves removing outliers, filling in missing values, and transforming the data into a format that is suitable for analysis. After the data is cleaned, it can be analyz...

SMART technique to ask question

  Data analysis এর একটি গুরুত্বপূর্ণ অংশ হলো data collection করা। Data collection process এর ৬ টি ধাপ আছে। এর মধ্যে একটি ধাপ হলো, "ASK"। এবং এই ধাপ এর একটি গুরুত্বপূর্ণ টেকনিক হলো "SMART QUESTION" এখানে key word হলো SMART যেখানে S => Specific M => Measurable A => Action Oriented R => Relevant T => Time-bound 1. Specific question বলতে বোঝায় কোন একটি বিষয়ে নির্দিষ্ট প্রশ্ন করা। যে প্রশ্নটি মূল সমস্যাকে তুলে ধরবে, এবং ডাটা এনালাইসিস এর জন্য প্রয়োজনীয় সব তথ্য এর উত্তর নিশ্চিত করবে। 2. Measrable question: Measurable question এমন সব প্রশ্নকে নির্দেশ করে যার উত্তরকে measurament/পরিমাপ করা যায়। একটা উদাহরন দেওয়া যাক, মনে করি সাম্প্রতিক কালের কোন একটি ভাইরাল ভিডিও নিয়ে যদি আমরা তথ্য সংগ্রহ করতে চাই সেক্ষেত্রে আমারা যদি প্রশ্ন করি "Why did a recent video go viral?" এক্ষত্রে আমরা একটী ব্যাখামূলক উত্তর পেতে পারি। সেক্ষেত্রে এই প্রশ্নের ধরন হবে unmeasurable. কিন্তু যদি এর বদলে আমরা প্রশ্ন করতাম "How many times was our video shared on social channels...

Comparison of data science and data analysis

  Data analysis and data science are related, but they are different. Data analysis refers to examining, cleaning, transforming, and modeling data to discover useful information, suggesting conclusions, and support decision-making. Data scientists, on the other hand, are experts in the field of data science and often have a combination of skills including statistical analysis, programming, data visualization, and machine learning. Data scientists use data analysis to help solve complex business problems and drive decision-making. In simple words, Data Analysis is a subset of Data Science.