{"id":6003,"date":"2018-09-20T13:50:14","date_gmt":"2018-09-20T06:50:14","guid":{"rendered":"http:\/\/science-technology.vn\/?p=6003"},"modified":"2018-09-20T13:50:14","modified_gmt":"2018-09-20T06:50:14","slug":"cac-chuc-vu-ve-khoa-hoc-du-lieu","status":"publish","type":"post","link":"https:\/\/science-technology.vn\/?p=6003","title":{"rendered":"C\u00e1c ch\u1ee9c v\u1ee5 v\u1ec1 khoa h\u1ecdc d\u1eef li\u1ec7u"},"content":{"rendered":"

T\u00f4i \u0111\u00e3 nh\u1eadn \u0111\u01b0\u1ee3c nhi\u1ec1u emails t\u1eeb c\u00e1c sinh vi\u00ean h\u1ecfi v\u1ec1 vi\u1ec7c l\u00e0m D\u1eef li\u1ec7u l\u1edbn \u1edf M\u0129 c\u0169ng nh\u01b0 y\u00eau c\u1ea7u k\u0129 n\u0103ng. Ng\u00e0y nay Khoa h\u1ecdc d\u1eef li\u1ec7u hay D\u1eef li\u1ec7u l\u1edbn l\u00e0 m\u1ed9t trong nh\u1eefng ngh\u1ec1 nghi\u1ec7p n\u00f3ng nh\u1ea5t trong c\u00f4ng nghi\u1ec7p c\u00f4ng ngh\u1ec7 do vi\u1ec7c b\u00f9ng n\u1ed5 c\u1ee7a nhi\u1ec1u ki\u1ec3u d\u1eef li\u1ec7u, c\u1ea3 c\u00f3 c\u1ea5u tr\u00fac l\u1eabn phi c\u1ea5u tr\u00fac t\u1eeb Internet, di \u0111\u1ed9ng v\u00e0 m\u1ecdi thi\u1ebft b\u1ecb \u0111i\u1ec7n t\u1eed.<\/p>\n

L\u0129nh v\u1ef1c Khoa h\u1ecdc d\u1eef li\u1ec7u \u0111\u00e3 t\u0103ng tr\u01b0\u1edfng l\u1edbn trong th\u1eadp k\u1ec9 qua; do \u0111\u00f3, c\u00e1c k\u0129 n\u0103ng b\u1eaft \u0111\u1ea7u chuy\u00ean m\u00f4n h\u01a1n. M\u1ed9t c\u00e1ch \u0111i\u1ec3n h\u00ecnh, ng\u01b0\u1eddi t\u1ed1t nghi\u1ec7p \u0111\u1ea1i h\u1ecdc c\u00f3 b\u1eb1ng c\u1eed nh\u00e2n th\u01b0\u1eddng b\u1eaft \u0111\u1ea7u nh\u01b0 k\u0129 s\u01b0 d\u1eef li\u1ec7u hay ng\u01b0\u1eddi qu\u1ea3n l\u00ed k\u1ebft c\u1ea5u n\u1ec1n d\u1eef li\u1ec7u v\u00e0 c\u00f4ng c\u1ee5, ng\u01b0\u1eddi bi\u1ebft c\u00e1ch thu th\u1eadp, t\u1ed5 ch\u1ee9c, l\u01b0u gi\u1eef v\u00e0 nh\u1eadn k\u1ebft qu\u1ea3 t\u1eeb kh\u1ed1i l\u01b0\u1ee3ng d\u1eef li\u1ec7u bao la. Ch\u1ee9c v\u1ee5 Ph\u00e2n t\u00edch d\u1eef li\u1ec7u th\u01b0\u1eddng y\u00eau c\u1ea7u b\u1eb1ng th\u1ea1c s\u0129 t\u1ea1i \u0111\u00f3 ng\u01b0\u1eddi t\u1ed1t nghi\u1ec7p c\u00f3 k\u0129 n\u0103ng ph\u00e2n t\u00edch gi\u1ecfi b\u1eb1ng vi\u1ec7c d\u00f9ng th\u1ed1ng k\u00ea v\u00e0 h\u1ecdc m\u00e1y. Nh\u00e0 khoa h\u1ecdc d\u1eef li\u1ec7u th\u01b0\u1eddng \u0111\u01b0\u1ee3c li\u00ean k\u1ebft v\u1edbi m\u1ee9c ti\u1ebfn s\u0129, v\u1ecb tr\u00ed h\u1ed9i t\u1ee5 ch\u00ednh v\u00e0o nghi\u00ean c\u1ee9u v\u00e0 d\u1ef1 b\u00e1o xu h\u01b0\u1edbng.<\/p>\n

T\u00f4i \u0111\u00e3 t\u00ecm nhi\u1ec1u vi\u1ec7c l\u00e0m \u0111\u01b0\u1ee3c \u0111\u0103ng t\u1eeb Facebook, Google, Microsoft, v\u00e0 Amazon v\u00e0 \u0111i t\u1edbi m\u00f4 t\u1ea3 chung nh\u01b0 sau:<\/p>\n

Ch\u1ee9c v\u1ee5 k\u0129 s\u01b0 d\u1eef li\u1ec7u \u0111i\u1ec3n h\u00ecnh y\u00eau c\u1ea7u ng\u01b0\u1eddi t\u1ed1t nghi\u1ec7p:<\/p>\n

    \n
  1. C\u00f3 tri th\u1ee9c v\u1ec1 h\u1ec7 th\u1ed1ng t\u00ednh to\u00e1n ph\u00e2n b\u1ed1, bi\u1ebft c\u00e1ch qu\u1ea3n l\u00ed c\u1ee5m Hadoop, v\u1edbi m\u1ecdi d\u1ecbch v\u1ee5 c\u1ee7a n\u00f3.<\/li>\n
  2. Th\u00e0nh th\u1ea1o d\u00f9ng Hadoop v2, MapReduce, HDFS v\u00e0 c\u00f3 kh\u1ea3 n\u0103ng gi\u1ea3i quy\u1ebft c\u00e1c v\u1ea5n \u0111\u1ec1 v\u1edbi vi\u1ec7c v\u1eadn h\u00e0nh c\u1ee7a c\u1ee5m<\/li>\n
  3. C\u00f3 tri th\u1ee9c t\u1ed1t v\u1ec1 c\u00e1c c\u00f4ng c\u1ee5 truy v\u1ea5n d\u1eef li\u1ec7u l\u1edbn, nh\u01b0 Pig, Hive, v\u00e0 Impala<\/li>\n
  4. C\u00f3 kinh nghi\u1ec7m v\u1edbi c\u01a1 s\u1edf d\u1eef li\u1ec7u NoSQL, nh\u01b0 HBase, Cassandra, MongoDB<\/li>\n
  5. C\u00f3 kinh nghi\u1ec7m v\u1edbi Spark v\u00e0 vi\u1ec7c t\u00edch h\u1ee3p d\u1eef li\u1ec7u t\u1eeb nhi\u1ec1u ngu\u1ed3n d\u1eef li\u1ec7u<\/li>\n
  6. C\u00f3 tri th\u1ee9c v\u1ec1 c\u00e1c k\u0129 thu\u1eadt ETL \u0111a d\u1ea1ng v\u00e0 c\u00e1c khu\u00f4n kh\u1ed5, nh\u01b0 Flume<\/li>\n
  7. C\u00f3 kinh nghi\u1ec7m v\u1edbi c\u00e1c h\u1ec7 th\u1ed1ng th\u00f4ng b\u00e1o \u0111a d\u1ea1ng, nh\u01b0 Kafka hay RabbitMQ<\/li>\n
  8. C\u00f3 kinh nghi\u1ec7m v\u1edbi c\u00e1c b\u1ed9 c\u00f4ng c\u1ee5, nh\u01b0 Mahout, SparkML, hay H2O<\/li>\n
  9. C\u00f3 kinh nghi\u1ec7m v\u1edbi Cloudera\/MapR\/Hortonworks<\/li>\n
  10. C\u00f3 kinh nghi\u1ec7m v\u1edbi vi\u1ec7c x\u00e2y d\u1ef1ng c\u00e1c h\u1ec7 th\u1ed1ng x\u1eed l\u00ed lu\u1ed3ng, d\u00f9ng c\u00e1c gi\u1ea3i ph\u00e1p nh\u01b0 Storm hay Spark-Streaming<\/li>\n<\/ol>\n

    Hi\u1ec7n th\u1eddi (9\/2018), c\u00f3 6,500 v\u1ecb tr\u00ed m\u1edf ra \u1edf Thung l\u0169ng Silicon (t\u00f4i th\u01b0\u1eddng t\u1eadp trung \u1edf \u0111\u00e2y v\u00ec t\u00f4i c\u00f3 th\u1ec3 truy nh\u1eadp v\u00e0o nh\u1eefng b\u00e0i \u0111\u0103ng vi\u1ec7c l\u00e0m). C\u00e1c ch\u1ee9c v\u1ee5 ch\u1ea1y t\u1eeb k\u0129 s\u01b0 d\u1eef li\u1ec7u, ng\u01b0\u1eddi ph\u00e2n t\u00edch d\u1eef li\u1ec7u, v\u00e0 nh\u00e0 khoa h\u1ecdc d\u1eef li\u1ec7u. \u00a0L\u01b0\u01a1ng h\u00e0ng n\u0103m cho nh\u00e0 khoa h\u1ecdc d\u1eef li\u1ec7u: $125,000 t\u1edbi $210,000. Ng\u01b0\u1eddi ph\u00e2n t\u00edch d\u1eef li\u1ec7u: $110,000 t\u1edbi $145,000 v\u00e0 k\u0129 s\u01b0 d\u1eef li\u1ec7u: $95,000 t\u1edbi $120,000. \u0110\u00f3 l\u00e0 t\u1ea5t c\u1ea3 m\u1ee9c v\u00e0o ngh\u1ec1 cho ng\u01b0\u1eddi m\u1edbi t\u1ed1t nghi\u1ec7p g\u1ea7n \u0111\u00e2y. Thung l\u0169ng Silicon c\u00f3 l\u1ebd c\u00f3 nhi\u1ec1u v\u1ecb tr\u00ed h\u01a1n c\u00e1c th\u00e0nh ph\u1ed1 kh\u00e1c nh\u01b0 Seattle, Boston, New York cho d\u00f9 chi ph\u00ed s\u1ed1ng c\u0169ng cao h\u01a1n.<\/p>\n

    Do nhu c\u1ea7u cao v\u00e0 thi\u1ebfu h\u1ee5t c\u00f4ng nh\u00e2n, c\u00e1c c\u00f4ng ti nh\u01b0 Apple, Google, IBM, Ernst and Young s\u1ebd KH\u00d4NG y\u00eau c\u1ea7u b\u1eb1ng \u0111\u1ea1i h\u1ecdc, v\u1edbi gi\u1ea3 \u0111\u1ecbnh r\u1eb1ng ng\u01b0\u1eddi xin v\u00e0o c\u00f3 nh\u1eefng k\u0129 n\u0103ng n\u00e0y m\u00e0 h\u1ecd \u0111\u00e3 thu nh\u1eadn b\u00ean ngo\u00e0i c\u00e1c \u0111\u1ea1i h\u1ecdc truy\u1ec1n th\u1ed1ng (qua MOOC hay nh\u1eefng b\u00e0i h\u1ecdc tr\u1ef1c tuy\u1ebfn.) Ph\u00f3 ch\u1ee7 t\u1ecbch c\u1ee7a Google, \u00f4ng Laszlo Bock \u0111\u00e3 tuy\u00ean b\u1ed1: “Khi c\u00e1c b\u1ea1n nh\u00ecn v\u00e0o nh\u1eefng ng\u01b0\u1eddi kh\u00f4ng v\u00e0o tr\u01b0\u1eddng v\u00e0 l\u00e0m ra con \u0111\u01b0\u1eddng c\u1ee7a h\u1ecd trong th\u1ebf gi\u1edbi, nh\u1eefng ng\u01b0\u1eddi \u0111\u00f3 l\u00e0 ng\u01b0\u1eddi ngo\u1ea1i l\u1ec7. V\u00e0 ch\u00fang t\u00f4i ph\u1ea3i l\u00e0m m\u1ecdi \u0111i\u1ec1u ch\u00fang t\u00f4i c\u00f3 th\u1ec3 l\u00e0m \u0111\u1ec3 t\u00ecm ra nh\u1eefng ng\u01b0\u1eddi n\u00e0y.”<\/p>\n

     <\/p>\n

    —English version—<\/p>\n

     <\/p>\n

    Data Science Positions<\/p>\n

    I have received several emails from students asking about Big data jobs in the U.S. as well as the skill requirements. Today Data Science or Big Data is one of the hottest careers in the technology industry due to the explosion of multiple types of data, both structured and unstructured from the Internet, mobile and all the electronic devices.<\/p>\n

    Data Science field has grown significantly during the last decade; therefore, the skills started to be more specific. Typically, college graduates with a Bachelor\u2019s degree often start as a Data Engineer or the person who manages data infrastructure and tools, who know how to collect, organize, store and get results from these vast amounts of data. Data Analysis position usually requires a Master\u2019s degree where the graduates have strong analysis skills using statistics and machine learning. A Data Scientist is often associated with a Ph.D. level where the main focus is on research and predicting trends.<\/p>\n

    I searched several jobs posting from Facebook, Google, Microsoft, and Amazon and come up with a general description as follows:<\/p>\n

    A typical Data Engineer position requires graduates to:<\/p>\n

      \n
    1. Have knowledge of distributed computing systems, know how to manage a Hadoop cluster, with all its services.<\/li>\n
    2. Proficiency with Hadoop v2, MapReduce, HDFS and ability to solve issues with operating the cluster<\/li>\n
    3. Good knowledge of Big Data querying tools, such as Pig, Hive, and Impala<\/li>\n
    4. Experience with NoSQL databases, such as HBase, Cassandra, MongoDB<\/li>\n
    5. Experience with Spark and integration of data from multiple data sources<\/li>\n
    6. Knowledge of various ETL techniques and frameworks, such as Flume<\/li>\n
    7. Experience with various messaging systems, such as Kafka or RabbitMQ<\/li>\n
    8. Experience with toolkits, such as Mahout, SparkML, or H2O<\/li>\n
    9. Experience with Cloudera\/MapR\/Hortonworks<\/li>\n
    10. Experience with building stream-processing systems, using solutions such as Storm or Spark-Streaming<\/li>\n<\/ol>\n

      Currently (Sep 2018), there are 6,500 open positions in Silicon Valley (I only focus here since I can access some job posting). Position range from Data Engineer, Data Analyst, and Data Scientist. \u00a0Annual salary for Data Scientist: $125,000 to $210,000. Data Analyst: $110,000 to $145,000 and Data Engineer: $95,000 to $120,000. Those are all entry levels for recent graduates. Silicon Valley probably has more positions than other cities such as Seattle, Boston, New York even the cost of living is also higher.<\/p>\n

      Due to the high demand and shortage of workers, companies like Apple, Google, IBM, Ernst, and Young will NOT require a college degree, assume that applicants have these skills that they acquired outside of traditional universities (MOOCs or some tutorial online.) A Vice President of Google, Mr. Laszlo Bock has declared: “When you look at people who don’t go to school and make their way in the world, those are exceptional human beings. And we should do everything we can to find those people.”<\/p>\n

       <\/p>\n","protected":false},"excerpt":{"rendered":"

      T\u00f4i \u0111\u00e3 nh\u1eadn \u0111\u01b0\u1ee3c nhi\u1ec1u emails t\u1eeb c\u00e1c sinh vi\u00ean h\u1ecfi v\u1ec1 vi\u1ec7c l\u00e0m D\u1eef li\u1ec7u l\u1edbn \u1edf M\u0129 c\u0169ng nh\u01b0 y\u00eau c\u1ea7u k\u0129 n\u0103ng. … <\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[26],"tags":[],"class_list":["post-6003","post","type-post","status-publish","format-standard","hentry","category-xu-huong-cong-nghe"],"_links":{"self":[{"href":"https:\/\/science-technology.vn\/index.php?rest_route=\/wp\/v2\/posts\/6003","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/science-technology.vn\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/science-technology.vn\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/science-technology.vn\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/science-technology.vn\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=6003"}],"version-history":[{"count":1,"href":"https:\/\/science-technology.vn\/index.php?rest_route=\/wp\/v2\/posts\/6003\/revisions"}],"predecessor-version":[{"id":6004,"href":"https:\/\/science-technology.vn\/index.php?rest_route=\/wp\/v2\/posts\/6003\/revisions\/6004"}],"wp:attachment":[{"href":"https:\/\/science-technology.vn\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6003"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/science-technology.vn\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6003"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/science-technology.vn\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6003"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}