@supankanlavanathan463
- [00:00:00] 📊 Data Science vs Data Analytics - Data science and data analytics are often confused but serve different purposes. - Data science encompasses tasks like data mining, machine learning model training, and AI deployment. - Data analytics is a specialized field within data science focusing on querying, interpreting, and visualizing datasets. - [00:01:47] 🔄 Data Science Lifecycle - The data science lifecycle consists of seven phases: problem identification, data mining, data cleaning, data exploration analysis, feature engineering, predictive modeling, and data visualization. - It's an iterative process where hypotheses are formed and tested with data. - Essential skills for data scientists include proficiency in Python, R, big data platforms (like Hadoop or Apache Spark), and SQL. - [00:03:30] 📈 Types of Data Analytics - Data analytics involves various types: predictive, prescriptive, diagnostic, and descriptive analytics. - Predictive analytics forecasts trends and outcomes, while prescriptive analytics provides decision recommendations. - Diagnostic analytics identifies causes of events, and descriptive analytics evaluates data qualities and quantities. - [00:05:16] 🧮 Skills for Data Analysts - Data analysts focus on interpreting existing data to derive actionable insights. - Skills required include analytical thinking, programming (especially in statistical tools), database familiarity, and data visualization. - Unlike data scientists, data analysts are less involved in creating new algorithms but rather in interpreting and applying data findings effectively.
@Mirko-Peters
ked, 'What’s the difference between data science and data analytics?' I remember when I started my career, even I wasn’t entirely clear about the distinction. Back then, I thought both were just fancy terms for working with data, but I quickly realized how different the roles are and how much each contributes uniquely to solving problems. This video is such a fantastic breakdown of these two disciplines. I loved how it highlighted that data analytics focuses on finding insights and answering specific questions, while data science is about building models and uncovering patterns for predictive insights. It’s a nuanced distinction that can be confusing, especially for newcomers, but this explanation really hit the mark. For me, it was during my first project in data science that the difference truly clicked. I was building a machine learning model to predict customer churn while my colleague, an analyst, was digging into the historical data to understand why churn was happening in the first place. Both tasks were critical, but they required completely different approaches, tools, and ways of thinking. This kind of collaboration is what makes the data world so fascinating to me. To anyone watching this video and trying to decide between these paths: think about whether you enjoy diving deep into datasets to find actionable insights (data analytics) or if you’d rather experiment with algorithms, code, and predictive models (data science). Either way, both fields are incredibly rewarding, and videos like this are great for helping you find your direction.
@KabirVanipodcast
Data analytics involves examining data to extract meaningful insights, while data science encompasses a wider scope, including data collection, cleaning, analysis, and machine learning modeling for predictive insights and decision-making.
@brianasheri4726
What’s really wild is I wanted to be a scientist when I was a kid and that might actually become a reality someday soon. I never thought I’d be saying that a year ago. Thank you for sharing this video!
@christopher6267
This whiteboard setup is so nice
@blessingiwerumor5390
Thanks for the explanation, been a bit frustrated but these explanations has helped
@ambikeya_tech
Great comparison! Data Analytics provides insights for informed decisions, while Data Science builds predictive models. Both are crucial in leveraging data to drive business success.
@twerner5496
I wish my professors had this setup… being able to face the audience while annotating is great… great vid
@RedJoker9000
I hate I did Computer Science, a lot of my life (even as kid) I liked working and doing data. (At times I did my father's data work) yet attempting to get a professional job, no one will give me a chance. I can get a job in other things easy due to doing STEM. However what I want (Data Analyst) I get ghosted or deny. Closest I got was Data Entry.
@zkproduction2718
Well I think data is the new oil
@Thehouse385
That first minute was the most regrettable minute of my week. “Someone who works in data science is a data scientist” - incredible content.
@olllo-top
This video helps me a lot, so I create some tools for daily work. And I want to share with you, olllo series for dealing with data. The free version is covering most base functions in general.
@thornimation5492
3:29 - Start of 'Data Analytics'
@ZeeDimensionYouTube
We can conclude that there is a strong relationship between the level of customer satisfaction and increased brand loyalty, which justifies investment in improving the customer experience.
@priusskipper
Thanks for this. I’m a frustrated biologist in the cosmetics industry and I’m looking to leave to do data science or analytics. It seems DA is faster to achieve this goal. Then learn DS as I go along.
@priyadarshieeankitaguddy1893
Thanks for explanation its very clear & now a day datascience & Analytics both have a great future nd involves data to build models predict the future while analytics focus more on analysing the past data to inform decision in present.So career in data science is challenging & stimulating to solveproblems which makes a real impact of business & society.This type of work is highly fulfilling demands .In data science the import of work on the organization & society is most of all important.And the main responsiobilities & role is to focus on predictive models & solving complex data problem we can say that,while in data analyzing process,it provide more analyze historical data to provide immediate decesion making.Thankyou😊 & have a nyc day ....
@midshiptom
A lot of companies are liberally calling Data Science (Scientist) and Analytics (analyst) interchangeably. Data analyst roles in the US require SQL and Python and there are hardly true data analytics position anymore.
@apamwamba
Cool. Very clear explanation. I would say data analytics mostly require Database skills [SQL] and a data BI tool like POWERBI. Python is a plus.
@bobanmilisavljevic420
Great motivation to learn more SQL today! 💪🧙♂️🌌⌨️
@ธนธรณ์หอมชิต
summarize Data science - finding patterns in large datasets, training machine learning models and deploying AI applications (hypotheses and experiments to see if a desired outcome can be achieved using available data) - Data science lifecycle - Identify problem - Data mining is extract data relevant to that problem or opportunity from large dataset (consist of bunch of redundancies and errors) - Data cleaning - Data exploration - feature engineering is using domain knowlege to extract detail from the data - predictive modeling - data visualization - Goal : develop deep skills in machine learning and AI - skill: python, R, Hadoop,SQL Data analyst - focusing of querying, interpreting and visualizing dataset - have dataset existing we need to do something with it - predictive analytic: identify trends, correlations and causation within dataset - prescriptive analytics: predicts likely outcome and makes decision recommendation - diagnostic analytics: help pain point the reason an event occurred - descriptive analytic: evaluates the qualities and quantities of a data set - Goal: interpret existion data and offer action insights - skill: Database, statistic, data visualization