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What is data_science_by_khawar_shehzad

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What is data_science_by_khawar_shehzad

  1. 1. Data Science
  2. 2. What is data science Data Science Applying advanced statistical tools to existing data to generate new insights Service Change Converting new data insights into (often small) changes to business processes Smarter Work More efficient and effective use of staff and resources
  3. 3. Data Analysis Has Been Around for a While… R.A. Fisher Howard Dresner Peter Luhn W.E. Deming
  4. 4. Introduction to Data Science  Most organizations are opening it’s door to Data Science.  Companies are focusing on Data Analytics for their growth.  Businesses are focusing on reaping major benefits from the data they already possess.
  5. 5. Importance of Data Science Data Science and Its Growing Importance.  An international field, data science deals with processes and systems, that are used to extract knowledge or insights from large amounts of data.
  6. 6. Data Science  In 1940’s and 1950’s-data storage was a big issue.  Today we have ample data storage opportunities.
  7. 7. History of Data Science. Before we see into the definition of Data science, let’s see the history of Data Science.  It is nothing new that have been introduced today.  Data existing in 1940’s and 1950’s as well however it was not viewed the way we see today.  Statisticians play an important role during this time period and they used to do data analysis manually.  They lacked use of computer for this purpose as such it’s importance we less.
  8. 8. “Big Data” Sources User Generated (Web & Mobile) …..
  9. 9. Education of a Data Scientist  The education requirements for data scientists are among the steepest of all IT occupations.  Approximately 40% of data scientist positions require an advanced degree, such as a Master's, MBA or PhD. ... Research and compare data science training programs and business intelligence degrees online.
  10. 10. Responsibility of a Data Scientist  Data scientist duties typically include creating various machine learning-based tools or processes within the company, such as recommendation engines or automated lead scoring systems.  Usually, it's considered normal to bring people with different sets of skills into the data science team.
  11. 11. Career Possibility of a Data Scientist  Some of the prominent Data Scientist job titles are:  Data Scientist.  Data Engineer.  Data Architect.  Data Administrator.  Data Analyst.  Business Analyst.  Data/Analytics Manager.  Business Intelligence Manager
  12. 12. Skill’s of a Good Data Scientist  Here are some important attributes and skills, according to IT leaders, industry analysts, data scientists, and others.  Critical thinking. ...  Coding. ...  Math. ...  Machine learning, deep learning, AI. ...  Communication. ...  Data architecture. ...  Risk analysis, process improvement, systems engineering. ...  Problem solving and good business intuition.
  13. 13. Technical skills of a Data Scientist  Other technical skills required to become a data scientist include:  Programming: You need to have  the knowledge of programming languages like Python, Perl, C/C++, SQL and Java—with Python being the most common coding language required in data science roles
  14. 14. Data scientist skill  Data scientists are skilled professionals who are responsible for finding out insights from huge volumes of structured and unstructured data. The importance of data scientists is spiking up and businesses are heavily relying on data analytics to boost decision-making capabilities and lean on machine learning as an integral part of IT development.
  15. 15. Benefits of being a Data Scientist The perks of being a Data Scientist  Sexiest job of the century. ...  Freedom to work. ...  A chance to work with big brands. ...  The payoff is handsome. ...  Proper training and certificate course. ...  Data science jobs in demand. ...  Different roles in the industry. ...  A safe career to pursue.
  16. 16. Top Programming Languages For Data Scientists  Python: It remains to one of the most popular languages, both in terms of pay it offers and popularity amongst recruiters looking for Python skills. ...  Java: ...  R: ...  Julia: ...  SAS: ...  SQL: ...  MATLAB: ...  Scala:
  17. 17. Data Science
  18. 18. Data science  All these eight field sets are different from each other.  People who practice Data Science are called Data Scientist.  They analyze complex data problems.

Notas del editor

  • Be specific and direct in the title. Use the subtitle to give the specific context of the speech.
    -The goal should be to capture the audience’s attention which can be done with a quote, a startling statistic, or fact. It is not necessary to include this attention getter on the slide.
  • Fisher: ANOVA (analysis of variance: a collection of statistical models, used to analyze the differences among group means and their associated procedures (such as "variation" among and between groups)), Fisher’s exact test.
    Also credited with quote “correlation does not imply causation” – lifetime pipe smoker, he derided papers showing a link between smoking and cancer.
    Deming – quality control – statistical sampling
    Luhn – indexing, IR principles – use text and data to inform Business Decisions
    Tukey – exploratory data analsysis – led to S, S+ and R
    Howard Dresner – modern proponent of BI
    Tom Mitchell’s ML book – still a best-seller
    Fourth Paradigm – data driven scientific discovery – inspired by Jim Gray’s work at MSR. His primary research interests are in databases and transaction processing systems -- with particular focus on using computers to make scientists more productive. He and his group are working in the areas of astronomy, geography, hydrology, oceanography, biology, and health care.
    Other scientific discovery paradigms: empiricism, analysis and simulation
    Google Inc  alphabet Inc. Google began in January 1996 as a research project by Larry Page and Sergey Brin when they were both PhD students at Stanford University in Stanford, California.[33]
    Peter Norvig – simple models + lots of data > complex models
    Data Deluge – exponential growth in data volume, the Economist 2010, Businesses, governments and society are only starting to tap its vast potential

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