Nelay Apu

  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...

  Big data:   Big data is an increasingly important aspect of the modern world, as organizations generate and collect an ever-growing amount of information. This information can provide valuable insights and help companies make informed decisions, but it can also be overwhelming to deal with. In this blog post, we'll explore what big data is, why it's important, and how you can use it to your advantage. What is big data? Big data refers to extremely large datasets that cannot be processed by traditional data processing tools. This data can come from a variety of sources, including social media, e-commerce, and machine-generated data from IoT devices. The characteristics of big data are volume, velocity, and variety.   Volume refers to the sheer size of the data, velocity refers to the speed at which it's generated, and  variety refers to the different types of data being collected. Why is big data important? Big data provides organizations with the opportunity to ...

Data has become an essential part of modern life, and its impact on businesses, organizations, and individuals is undeniable. With the increasing availability of data and the tools to analyze it, companies are leveraging data to make better decisions, improve operations, and gain a competitive edge. One of the most important aspects of data is its ability to provide insights that can be used to improve business operations. This can include identifying areas where costs can be reduced, improving customer service, or developing new products and services. For example, companies can use data to analyze customer behavior and preferences, which can help them create targeted marketing campaigns or develop new products that better meet customer needs. Data also plays a crucial role in risk management. By analyzing data, organizations can identify potential risks and take proactive measures to mitigate them. For example, banks can use data to identify fraudulent activity and prevent financial l...

A data scientist is a professional who utilizes data to extract insights and make informed decisions. They often work in industries such as technology, finance, healthcare, and marketing, and are responsible for analyzing large amounts of data to identify patterns and trends that can be used to improve business operations, increase revenue, and reduce costs. The role of a data scientist typically involves several key responsibilities, including: Collecting and analyzing large amounts of data from various sources, including structured and unstructured data Cleaning and preprocessing data to ensure its accuracy and quality Building and testing models to identify patterns and trends in data Communicating findings and insights to stakeholders in a clear and concise manner Developing and implementing data-driven solutions to improve business operations To be successful in this role, data scientists typically possess a strong background in computer science, mathematics, statistics, and dat...

  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...