{"id":491,"date":"2015-08-28T07:28:43","date_gmt":"2015-08-28T04:28:43","guid":{"rendered":"http:\/\/kusuaks7\/?p=96"},"modified":"2025-02-10T14:25:44","modified_gmt":"2025-02-10T14:25:44","slug":"become-data-scientist","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/become-data-scientist\/","title":{"rendered":"Ingredients in the making of a Data Scientist"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\tHow does one prepare for a career in data science? \u00a0What credentials enable you to become a data scientist? \u00a0These are frequently asked questions. \u00a0Swami Chandrasekaran, the\u00a0Executive Architect at IBM Watson, offers a roadmap<\/a>. Chandrasekaran\u0092s suggested curriculum is compelling, and his analogy of a metro map is a useful one. \u00a0He presents us with ten metro lines comprising of:\n
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  1. Fundamentals<\/li>\n \t
  2. Statistics<\/li>\n \t
  3. Programming<\/li>\n \t
  4. Machine Learning<\/li>\n \t
  5. Text Mining \/ Natural Language Processing<\/li>\n \t
  6. Data Visualization<\/li>\n \t
  7. Big Data<\/li>\n \t
  8. Data Ingestion<\/li>\n \t
  9. Data Munging<\/li>\n \t
  10. Toolbox<\/span><\/li>\n<\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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    Fundamentals<\/h4><\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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      \n \t
    1. Metrics<\/a>\u00a0&\u00a0Linear Algebra<\/a>\u00a0Fundamentals<\/li>\n \t
    2. Hash Functions<\/a>,\u00a0Binary Tree<\/a>,\u00a0O(n)<\/a><\/li>\n \t
    3. Relational Algebra<\/a>,\u00a0DB Basics<\/a><\/li>\n \t
    4. Inner<\/a>,\u00a0Outer<\/a>,\u00a0Cross<\/a>,\u00a0Theta Join<\/a><\/li>\n \t
    5. CAP Theorem<\/a><\/li>\n \t
    6. Tabular Data<\/a><\/li>\n \t
    7. Entropy<\/a><\/li>\n \t
    8. Data Frames<\/a>\u00a0&\u00a0Series<\/a><\/li>\n \t
    9. Sharding<\/a><\/li>\n \t
    10. OLAP<\/a><\/li>\n \t
    11. Multidimensional Data Model<\/a><\/li>\n \t
    12. Extract\/Transform\/Load(ETL)<\/a><\/li>\n \t
    13. Reporting<\/a>\u00a0vs\u00a0BI<\/a>\u00a0vs\u00a0Analytics<\/a><\/li>\n \t
    14. JSON<\/a>\u00a0&\u00a0XML<\/a><\/li>\n \t
    15. NoSQL<\/a><\/li>\n \t
    16. Regex<\/a><\/li>\n \t
    17. Vendor Landsacpe<\/li>\n \t
    18. Env Setup<\/li>\n<\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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      Statistics<\/h4><\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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        \n \t
      1. Pick a\u00a0Dataset<\/a>\u00a0(UCI Repo<\/a>)<\/li>\n \t
      2. Descriptive Statistics<\/a>(mean<\/a>,\u00a0median<\/a>,\u00a0range<\/a>,\u00a0SD<\/a>,\u00a0Var<\/a>)<\/li>\n \t
      3. Exploratory Data Analysis<\/a><\/li>\n \t
      4. Histograms<\/a><\/li>\n \t
      5. Percentiles<\/a>\u00a0&\u00a0Outliers<\/a><\/li>\n \t
      6. Probability Theory<\/a><\/li>\n \t
      7. Bayes Theorem<\/a><\/li>\n \t
      8. Random Variables<\/a><\/li>\n \t
      9. Cumulative Distribution Function (CDF)<\/a><\/li>\n \t
      10. Continuous Distributions<\/a>\u00a0(Normal<\/a>,\u00a0Poisson<\/a>,\u00a0Gaussian<\/a>)<\/li>\n \t
      11. Skewness<\/a><\/li>\n \t
      12. Analysis of Variance (ANOVA)<\/a><\/li>\n \t
      13. Probability Density Function (PDF)<\/a><\/li>\n \t
      14. Central Limit Theorem<\/a><\/li>\n \t
      15. Monte Carlo Method<\/a><\/li>\n \t
      16. Hypothesis Testing<\/a><\/li>\n \t
      17. p-Value<\/a><\/li>\n \t
      18. Chi-square Test<\/a><\/li>\n \t
      19. Estimation<\/a><\/li>\n \t
      20. Confidence Interval (CI)<\/a><\/li>\n \t
      21. Maximum Likelihood Estimation (MLE)<\/a><\/li>\n \t
      22. Kernel Density Estimate<\/a><\/li>\n \t
      23. Regression<\/a><\/li>\n \t
      24. Covariance<\/a><\/li>\n \t
      25. Correlation<\/a><\/li>\n \t
      26. Pearson Coeff<\/a><\/li>\n \t
      27. Causation<\/a><\/li>\n \t
      28. Least Squares Fit<\/a><\/li>\n \t
      29. Euclidean Distance<\/a><\/li>\n<\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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        Programming<\/h4><\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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          \n \t
        1. Python Basics<\/a><\/li>\n \t
        2. Working in Excel<\/a><\/li>\n \t
        3. R<\/a>\u00a0Setup,\u00a0R Studio<\/a><\/li>\n \t
        4. R Basics<\/a><\/li>\n \t
        5. Expressions<\/a><\/li>\n \t
        6. Variables<\/a><\/li>\n \t
        7. IBM SPSS<\/a>,\u00a0Rapid Miner<\/a><\/li>\n \t
        8. Vectors<\/a><\/li>\n \t
        9. Matrices<\/a><\/li>\n \t
        10. Arrays<\/a><\/li>\n \t
        11. Factors<\/li>\n \t
        12. Lists<\/a><\/li>\n \t
        13. Data Frames<\/a><\/li>\n \t
        14. Reading\u00a0CSV<\/a>\u00a0Data<\/li>\n \t
        15. Reading\u00a0RAW Data<\/a><\/li>\n \t
        16. Subsetting Data<\/li>\n \t
        17. Manipulate Data Frames<\/a><\/li>\n \t
        18. Functions<\/a><\/li>\n \t
        19. Factor Analysis<\/a><\/li>\n \t
        20. Install Pkgs<\/li>\n<\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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          Machine Learning<\/h4><\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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            \n \t
          1. What is\u00a0ML<\/a>?<\/li>\n \t
          2. Numerical Var<\/li>\n \t
          3. Categorical Variable<\/a><\/li>\n \t
          4. Supervised Learning<\/a><\/li>\n \t
          5. Unsupervised Learning<\/a><\/li>\n \t
          6. Concepts<\/a>,\u00a0Inputs<\/a>\u00a0&\u00a0Attributes<\/a><\/li>\n \t
          7. Training & Test Data<\/a><\/li>\n \t
          8. Classifier<\/li>\n \t
          9. Prediction<\/li>\n \t
          10. Lift<\/a><\/li>\n \t
          11. Overfitting<\/a><\/li>\n \t
          12. Bias & Variance<\/a><\/li>\n \t
          13. Trees & Classification<\/li>\n \t
          14. Classification<\/a>, Classification Rate<\/li>\n \t
          15. Decision Trees<\/a><\/li>\n \t
          16. Boosting<\/a><\/li>\n \t
          17. Na\u00efve Bayes Classifiers<\/a><\/li>\n \t
          18. K-Nearest Neighbor<\/a><\/li>\n \t
          19. Logistic Regression<\/a><\/li>\n \t
          20. Regression, Ranking<\/li>\n \t
          21. Linear Regression<\/a><\/li>\n \t
          22. Perceptron<\/a><\/li>\n \t
          23. Clustering<\/a>,\u00a0Hierarchical Clustering<\/a><\/li>\n \t
          24. K-means Clustering<\/a><\/li>\n \t
          25. Neural Networks<\/a><\/li>\n \t
          26. Sentiment Analysis<\/a><\/li>\n \t
          27. Collaborative Filtering<\/a><\/li>\n \t
          28. Tagging<\/li>\n<\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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            Text Mining\/Natural Language Processing<\/h4><\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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              \n \t
            1. Corpus<\/a><\/li>\n \t
            2. Named Entity Recognition<\/a><\/li>\n \t
            3. Text Analysis<\/li>\n \t
            4. UIMA<\/a><\/li>\n \t
            5. Term Document Matrix<\/a><\/li>\n \t
            6. Term Frequency & Weight<\/a><\/li>\n \t
            7. Support Vector Machines<\/a><\/li>\n \t
            8. Association Rules<\/a><\/li>\n \t
            9. Market Based Analysis ( Market Basket Analysis ? )<\/li>\n \t
            10. Feature Extraction<\/a><\/li>\n \t
            11. Using\u00a0Mahout<\/a><\/li>\n \t
            12. Using\u00a0Weka<\/a><\/li>\n \t
            13. Using\u00a0Natural Language Toolkit (NLTK)<\/a><\/li>\n \t
            14. Classify Text (\u00a0Document Classification<\/a>? )<\/li>\n \t
            15. Vocabulary Mapping<\/li>\n<\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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              Data Visualization<\/h4><\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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                \n \t
              1. Data Exploration in R (Hist<\/a>,\u00a0Boxplot<\/a>\u00a0etc)<\/li>\n \t
              2. Uni, Bi & Multivariate Viz<\/li>\n \t
              3. ggplot2<\/a><\/li>\n \t
              4. Histogram & Pie (Uni)<\/li>\n \t
              5. Tree &\u00a0Tree Map<\/a><\/li>\n \t
              6. Scatter Plot<\/a>\u00a0(Bi)<\/li>\n \t
              7. Line Charts<\/a>\u00a0(Bi)<\/li>\n \t
              8. Spatial Charts<\/li>\n \t
              9. Survey Plot<\/li>\n \t
              10. Timeline<\/li>\n \t
              11. Decision Tree<\/a><\/li>\n \t
              12. D3.js<\/a><\/li>\n \t
              13. InfoVis<\/a><\/li>\n \t
              14. IBM ManyEyes<\/a><\/li>\n \t
              15. Tableau<\/a><\/li>\n<\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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                Big Data<\/h4><\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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                  \n \t
                1. Map Reduce<\/a>\u00a0Framework<\/li>\n \t
                2. Hadoop Components<\/a><\/li>\n \t
                3. HDFS<\/a><\/li>\n \t
                4. Data Replication<\/a>\u00a0Principles<\/li>\n \t
                5. Setup\u00a0Hadoop<\/a>\u00a0( IBM \/ Cloudera \/ HortonWorks )<\/li>\n \t
                6. Name & Data Nodes<\/a><\/li>\n \t
                7. Job<\/a>\u00a0&\u00a0Task Tracker<\/a><\/li>\n \t
                8. M\/R Programming<\/a><\/li>\n \t
                9. Sqoop<\/a>\u00a0: Loading Data in HDFS<\/li>\n \t
                10. Flume<\/a>,\u00a0Scribe<\/a>\u00a0: For Unstructured Data<\/li>\n \t
                11. SQL with\u00a0Pig<\/a><\/li>\n \t
                12. DWH with\u00a0Hive<\/a><\/li>\n \t
                13. Scribe<\/a>,\u00a0Chunkwa<\/a>\u00a0For Weblog<\/li>\n \t
                14. Using Mahout<\/a><\/li>\n \t
                15. Zookeeper<\/a>,\u00a0Avro<\/a><\/li>\n \t
                16. Storm<\/a>\u00a0: Hadoop Realtime<\/li>\n \t
                17. Rhadoop<\/a>,\u00a0RHIPE<\/a><\/li>\n \t
                18. rmr<\/a><\/li>\n \t
                19. Cassandra<\/a><\/li>\n \t
                20. MongoDB<\/a>,\u00a0Neo4j<\/a><\/li>\n<\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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                  Data Ingestion<\/h4><\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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                    \n \t
                  1. Summary of Data Formats<\/li>\n \t
                  2. Data Discovery<\/li>\n \t
                  3. Data Sources & Acquisition<\/a><\/li>\n \t
                  4. Data Integration<\/a><\/li>\n \t
                  5. Data Fusion<\/a><\/li>\n \t
                  6. Transformation<\/a>, Enrichment<\/li>\n \t
                  7. Data Survey<\/li>\n \t
                  8. Google\u00a0OpenRefine<\/a><\/li>\n \t
                  9. How much Data?<\/li>\n \t
                  10. Using ETL<\/li>\n<\/ol>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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                    Data Munging<\/a><\/h4><\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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                      \n \t
                    1. Dimensionality<\/a>\u00a0& Numerosity Reduction<\/li>\n \t
                    2. Normalization<\/a><\/li>\n \t
                    3. Data Scrubbing<\/a><\/li>\n \t
                    4. Handling Missing Values<\/li>\n \t
                    5. Unbiased Estimators<\/a><\/li>\n \t
                    6. Binning<\/a>\u00a0Sparse Values<\/li>\n \t
                    7. Feature Extraction<\/a><\/li>\n \t
                    8. Denoising<\/a><\/li>\n \t
                    9. Sampling<\/a><\/li>\n \t
                    10. Stratified Sampling<\/a><\/li>\n \t
                    11. Principal Component Analysis<\/a><\/li>\n<\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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                      Toolbox<\/h4><\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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                        \n \t
                      1. MS Excel w\/\u00a0Analysis ToolPak<\/a><\/li>\n \t
                      2. Java<\/a>,\u00a0Python<\/a><\/li>\n \t
                      3. R<\/a>,\u00a0R-Studio<\/a>,\u00a0Rattle<\/a><\/li>\n \t
                      4. Weka<\/a>,\u00a0Knime<\/a>,\u00a0RapidMiner<\/a><\/li>\n \t
                      5. Hadoop<\/a>\u00a0Dist of Choice<\/li>\n \t
                      6. Spark<\/a>,\u00a0Storm<\/a><\/li>\n \t
                      7. Flume<\/a>,\u00a0Scribe<\/a>,\u00a0Chukwa<\/a><\/li>\n \t
                      8. Nutch<\/a>,\u00a0Talend<\/a>,\u00a0Scraperwiki<\/a><\/li>\n \t
                      9. Webscraper<\/a>,\u00a0Flume<\/a>,\u00a0Sqoop<\/a>\u00a0(Flume Dup?)<\/li>\n \t
                      10. tm<\/a>,\u00a0RWeka<\/a>,\u00a0NLTK<\/a><\/li>\n \t
                      11. RHIPE<\/a><\/li>\n \t
                      12. D3.js<\/a>,\u00a0ggplot2<\/a>,\u00a0Shiny<\/a><\/li>\n \t
                      13. IBM Languageware<\/a><\/li>\n \t
                      14. Cassandra<\/a>,\u00a0MongoDB<\/a><\/li>\n<\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
                        \n\t\t\t\t\t\t
                        \n\t\t\t\t\t
                        \n\t\t\t
                        \n\t\t\t\t\t\t
                        \n\t\t\t\t
                        \n\t\t\t\t\t\t\t\t\tThe only thing that we would add to this extensive framework is, of course, domain expertise within a specific industry, without which one may not be able ask the right questions.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

                        How does one prepare for a career in data science? \u00a0What credentials enable you to become a data scientist?<\/p>\n","protected":false},"author":11,"featured_media":2785,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[187],"tags":[94],"ppma_author":[1606],"class_list":["post-491","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bigdata-cloud","tag-data-science"],"authors":[{"term_id":1606,"user_id":11,"is_guest":0,"slug":"cameron-turner","display_name":"Cameron Turner","avatar_url":{"url":"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2024\/09\/cameron.jpeg","url2x":"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2024\/09\/cameron.jpeg"},"user_url":"","last_name":"Turner","first_name":"Cameron","job_title":"","description":""}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/491","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/11"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=491"}],"version-history":[{"count":4,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/491\/revisions"}],"predecessor-version":[{"id":37276,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/491\/revisions\/37276"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/2785"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=491"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=491"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=491"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=491"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}