1:51:41
Data Analytics & Business Analytics with AI|ML @ 9:30 AM (IST) by Mr.Sandeep Krishna Day-1
Naresh i Technologies
Overview
This video introduces the field of data analytics, explaining its fundamental concepts and its importance in modern business. The instructor, Sandeep Krishna, begins by understanding the participants' backgrounds, noting that most are freshers or career switchers. He then uses a relatable business owner analogy to illustrate the need for data analysis, especially in large organizations. The session defines data, its types (structured, unstructured, semi-structured), and the process of analytics, emphasizing the generation of actionable insights. Various real-world examples from e-commerce, entertainment, healthcare, and finance demonstrate how companies leverage data analytics to make informed decisions, improve sales, and enhance customer experience. The video also briefly touches upon related roles like Business Analyst, Machine Learning Engineer, and Data Scientist, positioning data analytics as the foundational skill for these domains.
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Chapters
- The instructor introduces himself and his experience in data analytics.
- Participants share their educational backgrounds and career aspirations, revealing a majority of freshers or those seeking a career switch.
- The importance of interaction and asking questions is highlighted for effective learning.
Understanding the audience's background helps tailor the explanation and address specific learning needs, especially for those new to the field.
Participants introduce themselves, mentioning their education (e.g., CSE with AI specialization, B.Tech in Data Science) and current roles (e.g., MIS executive).
- A small business owner can manage inventory based on experience and intuition.
- Large businesses (like hypermarkets) with thousands of products and branches cannot rely on intuition alone.
- Data analysts are crucial for large organizations to track sales, revenue, and product performance across various dimensions (city, product, area).
- The core role of a data analyst is to help businesses make informed decisions by analyzing vast amounts of data.
This analogy clearly demonstrates why data analysis becomes indispensable as business complexity and data volume increase, moving beyond simple guesswork.
Comparing a small shop owner managing five products with a hypermarket like D-Mart that has millions of products and thousands of branches, highlighting the impossibility of manual oversight in the latter.
- Consumers and users are the primary generators of data through their daily activities.
- Data is defined as any information, regardless of format, that provides insights.
- Data is categorized into three types: structured (tabular, like Excel), unstructured (non-tabular, like videos, audio), and semi-structured (key-value pairs, like JSON, XML).
Knowing the different types of data helps in understanding how data is stored, processed, and analyzed, with data analysts primarily focusing on structured and semi-structured data.
Examples of data types: Excel sheets for structured data, videos and images for unstructured data, and JSON or dictionaries for semi-structured data.
- Analytics is the process of collecting, cleaning, transforming, and visualizing data.
- The ultimate goal of analytics is to derive 'insights' from data.
- An insight is not just a statement but a piece of information that includes quantifiable numbers or comparisons (e.g., 'sales increased by 20%').
- Data analysts primarily work with historical data, while predictions and forecasting are tasks for machine learning and data scientists.
This chapter clarifies the distinction between raw data, analysis, and actionable insights, emphasizing that insights are data-driven conclusions with numerical backing.
Visualizing 10,000 sales records in a column chart to easily identify the best-selling product (insight), and then quantifying it with 'Product A selling the most with a 50% increase compared to last month' for a proper insight.
- Companies across various sectors use data analytics to understand customer behavior and improve operations.
- E-commerce platforms (Zomato, Swiggy, Myntra) use data for personalized recommendations, discounts, and targeted notifications.
- Entertainment platforms (Netflix, YouTube) analyze viewing habits to suggest content and increase watch hours.
- Healthcare, insurance, and educational institutions use data for patient management, risk assessment, and resource allocation.
- Trading apps and financial services analyze market data for investment decisions.
These examples illustrate the pervasive impact of data analytics in everyday life and across diverse industries, showcasing its practical value.
Zomato sending personalized discount offers based on a user's order history (e.g., ordering biryani for 7 days leads to a biryani discount offer on the 8th day).
- Data Analysts focus on historical data and generating insights using tools like Excel, SQL, Power BI, and Python.
- Business Analysts act as a bridge between data analysts and stakeholders, using insights to form strategies and make decisions, often using tools like Jira for project tracking.
- Machine Learning involves systems learning from data to identify patterns and make predictions, primarily using Python and statistical concepts.
- Data Science is an overarching field encompassing data analysis, machine learning, and AI, dealing with all aspects of data manipulation and insight generation.
- AI, including Gen AI and Agentic AI, builds upon data analytics foundations but focuses on more advanced capabilities like intelligent decision-making and content generation.
Understanding these different roles and their associated tools provides a roadmap for career progression and specialization within the data field.
A data analyst creating a Power BI dashboard with sales insights, which a business analyst then uses to decide whether to increase advertising for a low-selling product or discontinue it.
Key takeaways
- Data analytics is essential for businesses of all sizes to make informed decisions, especially as data volume and complexity grow.
- Data is generated by user activity and can be categorized as structured, unstructured, or semi-structured.
- The core output of data analytics is actionable insights, which are data-backed conclusions with quantifiable evidence.
- Data analysts primarily work with historical data, while machine learning and data scientists focus on predictions and future outcomes.
- Companies across diverse sectors leverage data analytics to personalize experiences, optimize operations, and gain a competitive advantage.
- Excel remains a fundamental tool for data analysis, even with the advent of more advanced software.
- The data field is broad, encompassing roles like Data Analyst, Business Analyst, Data Scientist, and Machine Learning Engineer, all built on a foundation of data understanding.
Key terms
Data AnalyticsBusiness AnalyticsStructured DataUnstructured DataSemi-structured DataInsightsData AnalystBusiness AnalystMachine LearningData ScienceSQLPower BIExcelPythonStatisticsProbability
Test your understanding
- Why is data analytics crucial for large businesses compared to small ones?
- What are the three main types of data, and what is an example of each?
- How does a data analyst derive 'insights' from raw data?
- What is the primary difference in focus between a data analyst and a machine learning engineer?
- Can you provide an example of how a company like Netflix uses data analytics to improve its services?