Комментарии:
As someone with a computer science background, looking for a perfect masters course, you really helped me mate. Big W 👍
ОтветитьTold by a Data Scientist ? ???
Bro grammatical error
Said left the chat
This is definitely the most concise, clear and informative video about this topic i've come across so far. Being a college student this video was very helpful and entertaining.
ОтветитьActually half of the problems, insights and knowledge can be created via MS Excel without even touching python etc
ОтветитьLoved your video. Thanks a lot!!!
Ответитьbeautiful video, thanks a lot for the panoramic of the DATA stuff... Actually, I'm in a transition from DBA to Data Science. And this type of video is really helpful 👏👏👏
ОтветитьI really appreciate, it's very insighful
ОтветитьDo you have to have a masters degree or PhD to be a Data scientist?
ОтветитьData Science Analytics
ОтветитьData Science Analytica
ОтветитьSo helpful!
ОтветитьI speak with a ghetto accent, can I be a data scientist?
ОтветитьAs a person who is searching for a new part in tech I really appreciate your video breakdown
ОтветитьThanks for sharing. I am working as a DS and 100% agree with your definition
ОтветитьNice video!
ОтветитьGafa is slang for clumsy in Spanish 😅
ОтветитьGreat vid! thanks for posting.
ОтветитьThanks for the neat insights.
Ответитьwatching the, like, first minute, ending with the twerk, I am just think: You must be kidding. No-one is posing like _that_. Next Mr. Steve Jobs incoming..
This is making fun of Neistat, right?
Thank you for the explanation!
ОтветитьWhat a masterful video. Your experience shows
ОтветитьBro , I need to know about the difference btw computer science and data science . Plz help me
ОтветитьI think it would be fascinating if you went more in depth into A/B testing & experimentation, since this seems like that area where a substantial amount of insights are made, or at least the guiding principles of a project
ОтветитьWhy would you make a video about your confusion of Data Science? lol
Ответить5 years later, how this view is holding up?
ОтветитьBro was ahead of his time 😂
ОтветитьSuch a First principle video on Data science, Still refreshing every time I watch. We are basing this to create "Solving a problem" Curriculum for kids across world to experience the joy of solving problems & creating impact
ОтветитьYear 2023
Chat gpt is launched
Do you think data science job will be in demand in future?
Data Science is a multidisciplinary field that involves extracting knowledge and insights from various types of data. It combines techniques from statistics, mathematics, computer science, and domain expertise to analyze and interpret complex datasets. The primary objective of data science is to gain meaningful information, patterns, and trends from the data to inform decision-making and solve real-world problems.
Key components of Data Science include:
Data Collection: Gathering data from different sources, which could be structured (e.g., databases, spreadsheets) or unstructured (e.g., text, images, videos).
Data Cleaning and Preprocessing: Preparing the data for analysis by handling missing values, removing inconsistencies, and transforming it into a usable format.
Exploratory Data Analysis (EDA): Performing initial analysis to understand the data's characteristics, distribution, and relationships between variables.
Data Modeling: Developing mathematical and statistical models to represent patterns and relationships in the data. This can include machine learning algorithms for prediction, classification, clustering, etc.
Evaluation and Validation: Assessing the performance and accuracy of the models using various metrics and validating them to ensure they generalize well to new data.
Visualization: Communicating the findings and insights effectively through visual representations like graphs, charts, and dashboards.
Interpretation and Decision-Making: Deriving actionable insights from the analysis and using them to make informed decisions and solve problems.
Data Science is widely applicable across various industries, including finance, healthcare, marketing, technology, and more. It plays a crucial role in optimizing processes, understanding customer behavior, detecting anomalies, predicting future trends, and improving overall efficiency.
In summary, Data Science is the practice of using data-driven methodologies and tools to discover valuable knowledge and make data-informed decisions, driving innovation and advancements across a broad range of fields.
Thanks from🇮🇳
ОтветитьThe need for MLEs has certainly seemed to have expanded in recent years to support DS Analytics in addition to DS Core.
There's such large swaths of tooling being developed in the testing, monitoring, and experimentation space, that it's more efficient to have those with an engineering focus that understand the needs of data science to develop the infrastructure that all takes place in.
I think the most interesting aspect Is understanding the difference between relational database and not relational database. In this sense understanding what cleaning actually Is would be more simple. From that, this lead to a good understanding of what analysis truly Is about. Lot of Grey area in this one
ОтветитьData Science is a class in College that needs to be passed asap
ОтветитьThank you for this amazing video. Really help me understand about data scientists and different company data structures
ОтветитьThat poor data scientist explaining the only fans CEO why deleting p*rn isnt a good idea 😖
Ответитьthat was a great video, it's great to give history of things to understand them even properly
ОтветитьI am a totql beginner how and where can i learn data science? Please help!
ОтветитьOh sh**! In less than 30 secs, I know I have to subscribe to your channel! Thanks for making this video! 😆😍🙏New subscriber!! 🙋♀💙
Ответитьyou, like me
DS ❤
Ahh you were actually not on the road of MEME
ОтветитьThanks so much for this video.
I am more interested in AI and Deep Learning.
This was super helpful! Thanks for explaining the nuances between the various responsibilities and how they play out in different roles at different size companies.
ОтветитьData science is just a buzzword. Millennials and Gen Z just want money to consume things in their pathetic useless loserz life.
My job , as a Gen Y, is to destroy them and make their life impossible
This is the most complete explanation I have seen. Thank you so much. I am planning on going back to school and was a bit confused regarding DS but thank you again for putting this into a more complete, synthesize, and easy to understand explanation.
Ответитьwhat is missing out is the salary range across the pyramid
Ответитьwell keeping data science aside, the introo was sooo fuckin coool 🤣🤣
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