Data Science πŸ“Š vs Data Analytics πŸ“ˆ: Kiya Farq Hai?

Dr. Aammar Tufail

Assalamualaikum dosto! Aap sab ne zaroor suna hoga terms jaise ‘Data Science’ aur ‘Data Analytics’, magar in dono mein asal mein kiya difference hai? Aaj hum isi topic par baat karenge aur samjhenge in dono terms ke similarities aur differences ko. Chaliye, start karte hain!

1. Data Science: Aik Overview 🌍

Data Science ek broad field hai jo kai tarah ki techniques, algorithms, aur systems ko use karta hai data se insights, knowledge, aur predictions nikalne ke liye. Think of it as a mashriqi bazaar jahan har kona mein kuch na kuch naya milta hai.

2. Data Analytics: Aik Jhalak 🧐

Data Analytics specifically focus karta hai data ko analyze karne par, patterns find out karne par, aur past performance par based hota hai future performance predict karne ke liye. Yeh woh step hai jahan aapko pata chalta hai ke aapka karobar kaise chal raha hai.

3. Major Differences: Asli Farq πŸ”„

  • Purpose: Data Science ka main maqsad hai new questions identify karna aur big picture dekhna. Jabke Data Analytics ka focus hota hai specific questions answer karna.
  • Scope: Data Science ek badi field hai jahan se aap insights, machine learning, predictions aur recommendations nikalte hain. Data Analytics zyada specific hai aur data ko deeply understand karne aur report karne ke liye hota hai.
  • Tools: Dono fields mein tools overlap karte hain, lekin Data Science mein aapko advanced tools jaise machine learning libraries dekhne ko milenge. Data Analytics mein tools zyada focused hote hain visualization aur reporting par.

4. Similarities: Jo Aam Baat Hai 🀝

  • Data-Centric: Dono fields data-centric hain. Matlab, dono ka main focus data par hota hai.
  • Decision Making: Dono fields mein data ko use kiya jata hai behtareen decisions lene ke liye.
  • Tools Use: Bohot saare tools jaise Python, R, SQL dono fields mein use hote hain.

5. Aakhri Khayal: Sochne Wali Baat πŸ€”

Agar hum asaan zaban mein kahein to, Data Science woh jungle hai jahan aap har taraf naye discoveries kar sakte hain, jabke Data Analytics woh guide hai jo aapko batata hai kaunsa rasta sahi hai aur kaunsa galat.

6. Toh Kaunsa Behtar Hai? πŸ†

Dono fields apni jagah important hain. Agar aapko data se naye-naye questions aur solutions nikalne mein interest hai, to Data Science aapke liye hai. Agar aap data ko deeply samajhna chahte hain aur business decisions mein madad karna chahte hain, to Data Analytics aapka saathi hai.

πŸš€ Aakhri Paigham: Agar aap in fields mein career banana chahte hain, toh abhi se shuruat karen! Har field mein opportunities hain, aapko bas decide karna hai ke aap kis mein maharat hasil karna chahte hain.

Khuda Hafiz, aur milte hain next blog mein! πŸŒŸπŸ‘‹

171 Comments.

  2. Behtreen blog, bht e achy andaaz me dono concepts smjhaye hain but still one thing confused me a bit: Data analytics me b past deep analysis ki basis pe FUTURE PREDICTIONS ki jati hain? but after some research I was able to learn that this thing is called predictive analytics and it is a part of both data science and data analytics … the difference is just that data science focuses on many other things and a broder prespective then just the predictive analytics …

    Reply

  3. Well acknowledged blog
    I had confusion related to these terms but now its clear now.

    Reply

  4. all clear, I am interested in data science as well because I have a suspicious nature so I fit best in it

    Reply

  23. good Sir .. Acha Treqa hai Sekhane ka Parr sar Please apni Website User Interface kab theek hoga . 4ro taraf ads hain ads dalin par thoda ache anzad se .. Sorry par ux acha nhi hai .

    Reply

  29. data science is a good choice for long run whereas data anyltics is a key tool to move with data science intelligently

    Reply

  37. Agr koi essa banda hy Jo k Computer ki koi degree b ni rakhta kya wo b course kr skta hy? Q k interview mein degree pochi jati hy…

    Reply

  39. Data Science vs Data Analytics: – (https://codanics.com/data-science-vs-data-analytics/)

    Data analytics is a subset of data science, and while they share common elements,
    οƒ˜ Data science is a more comprehensive and multidisciplinary field that includes data analytics as one of its components. Data scientists are equipped to tackle complex problems, solve new problems, make new solutions, make predictions, and create data-driven solutions, while;
    οƒ˜ Data Analysts specifically focus on analyzing (exploring and interpreting) existing large data (means data of past performance) to support decision-making.
    Major Differences:
    Purpose:
    οƒ˜ Data Science mainly focus on identifying New Questions and seeing bigger picture. while;
    οƒ˜ Data Analytics focus on answering specific Question.
    Scope:
    οƒ˜ Data Science in a vast field where we tend to find insights, machine learning, predictions and recommendations etc. while;
    οƒ˜ Data Analytics is more specific and focuses on deep understanding and reporting of Data.
    Tools:
    Although the use of Tools overlaps in both fields;
    οƒ˜ But in Data Science we use advance tools like machine learning, libraries etc. while in;
    οƒ˜ Data Analytics tools are mainly focused on visualization and reporting of data.
    Similarities:
    Data-Centric:
    οƒ˜ Both fields are data-centric means that both fields focus on Data.
    Decision Making:
    οƒ˜ In both fields Data is used for good decision making.
    Tools Use:
    οƒ˜ Varies tools like Python, R and SQL are used in both fields.

    Data Science is Jangal and Data Analyst is a guide to pass-through that Jangal

    Reply

  56. Matlab ye dono Data Science Aur Data Analytics ek dosray ki judwa bhn h mgr data science naam ki bhn agay h bht jiski waja s Data Analytics wali bhn jalti h πŸ˜‚ bht alaww smjh gya apun

    Reply

  64. Well understood, especially Data Science is Jangal and Data Analyst is a guide to pass-through that Jangal.

    Reply

  67. Amazing explanation regarding the differences between both fields. Personally, I’d want to go with DS.

    Reply

  74. alfaz ka Ϊ†Ω†Ψ§Ψ€ kamal h thnk u sir g hmry liye itniii Ω…Ψ­Ω†Ψͺ sy sweet dishes bnany k liyeπŸ˜πŸ€“

    Reply

  78. The best thing about all these blogs are easy to understand for those who can not understand but can understand easily means all the concepts are written in Urdu English.

    Reply

  96. Assignment 1 of day 3rd
    Libraries of python
    1 tensor flow
    2 numpy
    3 pandas
    4 pytorch
    5 open cv
    6 beautiful soup
    7 tk inter
    8 pillow
    9 matplotlib
    10 text blob

    Reply

  101. (ASSSIGHNMENT) 2: Data science vs. data analytics:
    How do organizations use data science and data analytics to inform decisions and increase efficiency and profitability?

    Data science
    Data scientists use programming, math, and statistics to gain insights and drive organizational strategy. Data scientists are highly adept at machine learning, data modeling, and the use of algorithms to automate processes. Since meaningful data is field-specific, data scientists also must have domain expertise, the understanding of their industry or company, to provide context for the data they work with. For example, data science research in healthcare can drive diagnoses, help prevent disease, or teach computers to read X-rays or MRIs.

    Data scientists work closely with sales and marketing, product development, information technology, finance, and business leaders to help identify trends, spot issues, understand consumer behavior, and present solutions that support strategic decision-making.

    Data analytics
    Data analytics professionals are responsible for data collection, organization, and maintenance, as well as for using statistics, programming, and other techniques to gain insights from data. The role of a data analyst is to spot trends and help solve problems. Examples of data analytics in retail include order tracking, recommendation features, and identification of store locations.

    Data analysts tend to respond to requests from decision-makers rather than drive the decision-making process.

    Reply

  102. Amazing Sir, you’ve provided a clear and straightforward explanation of the differences and similarities between Data Science and Data Analytics, which is fantastic for us. Data Science, as you’ve described it, is like exploring a vast, diverse market to discover new insights and predictions using various techniques and algorithms. On the other hand, Data Analytics focuses on analyzing data, finding patterns, and predicting future outcomes based on past data, helping us understand how things are going in the present. Your breakdown of the major differences, such as purpose, scope, and tools, makes it easy for us to grasp the distinctions between the two fields. Moreover, you’ve emphasized the common ground, like being data-centric and using similar tools. Your simple analogy, comparing Data Science to an adventurous jungle and Data Analytics to a guiding path, simplifies the concepts further. In conclusion, you’ve made it clear that both Data Science and Data Analytics have their unique roles and importance, offering us a great starting point to explore these exciting fields.

    Reply

  104. Very Nice and Easy to understand between both terms.
    Data Science is a huge field and to get a decision we change Raw data into Information (Preprocess) and then we compile whole data and do analyze and get insights from that data . so that we can make proper and right decision accordingly.

    Reply

  105. —–DATA ANALYSTS—–
    Collaborating with organizational leaders to identify informational needs.
    Acquiring data from primary and secondary sources.
    Cleaning and reorganizing data for analysis.
    Analyzing data sets to spot trends and patterns that can be translated into actionable insights.
    Presenting findings in an easy-to-understand way to inform data-driven decisions.

    ——DATA SCIENTIST——
    Gathering, cleaning, and processing raw data.
    Designing predictive models and machine learning algorithms to mine big data sets.
    Developing tools and processes to monitor and analyze data accuracy.
    Building data visualization tools, dashboards, and reports
    Writing programs to automate data collection and processing

    Reply

  106. —–DATA ANALYSTS—–
    Collaborating with organizational leaders to identify informational needs
    Acquiring data from primary and secondary sources
    Cleaning and reorganizing data for analysis
    Analyzing data sets to spot trends and patterns that can be translated into actionable insights
    Presenting findings in an easy-to-understand way to inform data-driven decisions.
    ——DATA SCIENTIST——
    Gathering, cleaning, and processing raw data
    Designing predictive models and machine learning algorithms to mine big data sets
    Developing tools and processes to monitor and analyze data accuracy
    Building data visualization tools, dashboards, and reports
    Writing programs to automate data collection and processing

    Reply

  107. Data Analytics:
    Data Analyst historical data ki janch parhtal karta ha.

    Data Science:
    Data Scientist future k lye prediction karta ha based on the insights produced by Data analyst.

    “Data Analysts derives insights from historical data and answers questions such as β€œWhat is the cause behind decreased sales?”, β€œWhat is the reason behind a particular products failure?”, and shares it with the concerned people for informed business decisions.”

    “On the other hand, Data Scientists, are more forward-looking people. They are more concerned with using these insights combined with Machine Learning, hypothesis, Statistical Tests, A/B testing for further development of products. They are more into asking questions such as β€œWill a larger motor in my product create more demand for it?”, or β€œWill targeting a particular market to enter into help me grow my company?”.”

    Reply

  109. sir, you’re doing very great. Mera question ye hai ke data science ya data analytics ki job hasil karne ke liye bachlors lazmi hai ya low education ke sath be job mil sakti hai. Expert thora guide kar dein. Jazakallah

    Reply

  114. Data science:matlb data main asa bhi hoo sakta hy acha wasa bhi hoo sakta hy or asa ku hoo raha hy
    Data Analytics: matlb app k data ma asa hy or wasa bhi hy or app k data ma asa hoo raha hy

    Main ya samja hun if i am right otherwise correct me pls

    Reply

  115. Data analysis focuses on examining and interpreting data to uncover patterns and insights, typically using traditional statistical methods. Data science, on the other hand, is a broader field that combines statistics, programming, and machine learning to extract insights, build models, and make predictions. Data analysis is more focused on the present and past, while data science incorporates predictive modeling for future projections. Data analysis primarily deals with analyzing existing data sets, while data science involves the entire data lifecycle, from acquisition to visualization.

    Reply

  119. Data Science or Data Analytics dono ka chooli daman ka sath hai. Dono ka aik dosray k baghair guzara nahi ho sakta.

    Reply

  127. Assignment one:::

    (Types Of Learning In Artificial Intelligence)
    Artificial Narrow Intelligence
    Artificial General Intelligence
    Artificial Super Intelligence

    Types Of AI
    Reactive Machines AI
    Limited Memory AI
    Theory Of Mind AI
    Self-aware AI

    Artificial Intelligence (AI) has several subsets, i.e:
    Machine Learning
    Deep Learning
    Natural Language processing
    Expert System
    Robotics

    Assignment 2:::
    (Data Science vs Data Analytics)

    Data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions.

    Data scientists, on the other hand, design and construct new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis.

    Working in Data Analytics
    The responsibility of data analysts can vary across industries and companies, but fundamentally, data analysts utilize data to draw meaningful insights and solve problems. They analyze well-defined sets of data using an arsenal of different tools to answer tangible business needs: e.g. why sales dropped in a certain quarter, why a marketing campaign fared better in certain regions, how internal attrition affects revenue, etc.

    Data analysts have a range of fields and titles, including (but not limited to) database analyst, business analyst, market research analyst, sales analyst, financial analyst, marketing analyst, advertising analyst, customer success analyst, operations analyst, pricing analyst, and international strategy analyst. The best data analysts have both technical expertise and the ability to communicate quantitative findings to non-technical colleagues or clients.

    Skills and Tools
    Top data analyst skills include data mining/data warehouse, data modeling, R or SAS, SQL, statistical analysis, database management & reporting, and data analysis.

    Working in Data Science
    Data scientists, on the other hand, estimate the unknown by asking questions, writing algorithms, and building statistical models. The main difference between a data analyst and a data scientist is heavy coding. Data scientists can arrange undefined sets of data using multiple tools at the same time, and build their own automation systems and frameworks.

    Skills and Tools
    These include machine learning, software development, Hadoop, Java, data mining/data warehouse, data analysis, python, and object-oriented programming

    Roles and Responsibilities
    Data scientists are typically tasked with designing data modeling processes, as well as creating algorithms and predictive models to extract the information needed by an organization to solve complex problems.

    Assignment 3:::

    (LLM details)

    top 12,

    1. GPT-4 (OpenAI)
    – Feature: Multimodal, improved factuality
    – Fees: $20 for ChatGPT Plus
    – Cost: Paid

    2. Cohere
    – Feature: Enterprise-focused, accuracy
    – Fees: $15 per 1 million tokens
    – Cost: Paid

    3. Claude v1 (Anthropic)
    – Feature: High performance, 100k token context
    – Fees: Not specified
    – Cost: Not specified

    4. PaLM 2 (Bison-001, Google)
    – Feature: Multilingual, commonsense reasoning
    – Fees: Not specified
    – Cost: Not specified

    5. LLaMA (Meta)
    – Feature: Open-source, research use
    – Fees: Free for research
    – Cost: No cost

    6. Vicuna 33B (LMSYS)
    – Feature: 33B parameter model
    – Fees: Free
    – Cost: No cost

    7. Guanaco-65B (Open-source)
    – Feature: Fine-tuned performance
    – Fees: Free
    – Cost: No cost

    8. WizardLM
    – Feature: Complex instruction following
    – Fees: Free
    – Cost: No cost

    9. 30B-Lazarus (CalderaAI)
    – Feature: Improved benchmark performance
    – Fees: Free
    – Cost: No cost

    10. MPT-30B (Mosaic ML)
    – Feature: Open-source, 8K token context
    – Fees: Free
    – Cost: No cost

    11. Falcon (TII)
    – Feature: Open-source
    – Fees: Free for commercial use
    – Cost: No cost

    12. GPT-3.5 (OpenAI)
    – Feature: General-purpose, fast response
    – Fees: Free
    – Cost: No cost .

    Reply

  147. Excellent exhibition of overlapping concepts in Roam Urdu to explain in a crystal clear way. Very much helpful for every level of readers. Fine explanation of thin line concepts. Keep up the good work sir !!!

    Reply

  150. amazing
    kash clg or school men bhi teachers esy hi samjhyn her cheez her student toper hu ga or clg jany ka maza bhi ay ga

    Reply

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