{"id":5887,"date":"2015-08-30T11:46:45","date_gmt":"2015-08-30T06:16:45","guid":{"rendered":"http:\/\/ucanalytics.com\/blogs\/?p=5887"},"modified":"2016-09-14T13:07:20","modified_gmt":"2016-09-14T07:37:20","slug":"6-worst-mistakes-for-data-scientists-and-how-to-avoid-them-explained-with-quotable-quotes","status":"publish","type":"post","link":"https:\/\/ucanalytics.com\/blogs\/6-worst-mistakes-for-data-scientists-and-how-to-avoid-them-explained-with-quotable-quotes\/","title":{"rendered":"6 Worst Mistakes for Data Scientists, and How to Avoid Them"},"content":{"rendered":"
\"Titanic\"<\/a>

Titanic on a Collision Course – by Roopam<\/p><\/div>\n

Over the years in my career in data science and predictive analytics, I have noticed some awful\u00a0practices\u00a0that young and sometimes seasoned analysts follow. These bad practices, I believe, throw careers of these data scientists on a collision course similar to the Titanic. I will present the six worst mistakes that I feel are at the root\u00a0of all these bad practices. Additionally, I will try to suggest strategies to avoid these mistakes\u00a0using some memorable quotes. To begin with, let me present the purpose of being a data scientist, which in my opinion is similar to being a detective. The following quote by Sherlock Holmes sums up the\u00a0purpose of being a data scientist:<\/p>\n

My name is Sherlock Holmes. It is my business to know what other people don’t know<\/em>.<\/p>\n

\u2015 Sherlock Holmes<\/p>\n<\/blockquote>\n

Now coming back to the six worst mistakes for data scientists, the following is my list\u00a0for the same:<\/p>\n

    \n
  1. Focus on tools rather than business problems<\/li>\n
  2. Planning communication last<\/li>\n
  3. Data analysis without a question \/ plan<\/li>\n
  4. Don’t read enough<\/li>\n
  5. Fail to simplify<\/li>\n
  6. Don’t sell well<\/li>\n<\/ol>\n

    1) Focus on Tools rather than Business Problems<\/span><\/h4>\n
    \n

    The expectations of life depend upon diligence; the mechanic that would perfect his work must first sharpen his tools.<\/span><\/em><\/p>\n

    \u2015 Confucius<\/p>\n<\/blockquote>\n

    In addition to programming languages such as SAS, R, Python etc. tools for data scientists include statistical and machine learning methods and algorithms . I am certainly not trying to undermine the importance of these tools when I am asking data scientists to shift their focus away from them. Mastering tools, as Confucius suggested, is at the core of being a good craftsman. However to make my point, imagine going to a doctor who is much more confident with her skills with stethoscope than diagnosing patients.\u00a0Some data scientists also focus too much on tools rather than problems these tools are meant to solve.\u00a0In my opinion, a good practice for data scientists is to always question the purpose of using the tool and how it will help solve the problem in hand.<\/p>\n

    \n

    It is the old experience that a rude instrument in the hand of a master craftsman will achieve more than the finest tool wielded by the uninspired journeyman.<\/span><\/em><\/p>\n

    \u2014 Karl Pearson<\/p>\n<\/blockquote>\n

    2) Planning Communication Last<\/span><\/h4>\n

    The most important things are the hardest to say, because words diminish them.\u00a0<\/span><\/em><\/p>\n

    \u2015\u00a0<\/span><\/em>Stephen King<\/p>\n<\/blockquote>\n

    Trust me in your career as a data scientist you will communicate some really important things: communications that\u00a0will challenge\u00a0status-quos and change the way organizations do their\u00a0business. Hence, you can’t leave the task of planning communication towards the end of the analysis. On the contrary, I believe, planning communication along with your investigation \/ analysis actually enhances the quality of your analysis. A good communication flows like a tightly knit and gripping story. When you plan your communication along with the analysis, your analysis also flows like a story. In my opinion, a good practice for data scientists is to take time away from their analysis on a daily basis and structure their results and thoughts in the form of a story.<\/p>\n

    Think like a wise man but communicate in the language of the people.\u00a0<\/span><\/em><\/p>\n

    \u2015\u00a0<\/span><\/em>William Butler Yeats<\/p>\n<\/blockquote>\n

    \u00a03) Data Analysis without a Question \/ Plan<\/span><\/h4>\n
    \n

    If you don’t know what you want, you end up with a lot you don’t.<\/em><\/p>\n

    \u2015\u00a0<\/span><\/em>Chuck Palahniuk<\/p>\n<\/blockquote>\n

    Easy availability of data often makes data scientists\u00a0jump directly towards data without well-defined questions. This is suicidal for any data science project. \u00a0Data science\u00a0is a structured process that starts with well-defined questions and objectives. Then comes the part of setting a few hypotheses to satisfy the grand objective.<\/p>\n

    It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts<\/em>.<\/p>\n

    \u2015 Sherlock Holmes<\/p>\n<\/blockquote>\n

    Let me create the distinction between theorizing and hypothesising. Hypotheses are testable where facts support or dispel them. As a data scientist, our job is to be dispassionate about our hypotheses. The idea is to be truth seekers rather than doing a self serving analysis. Additionally, during your analysis, you will come up with several clues that were not part of the hypotheses. You build your story on top of these clues like a true detective. However, having clearly defined questions before the analysis \u00a0is the most import aspect for data scientists.<\/p>\n

    \n

    Judge a man by his questions rather than by his answers.<\/em><\/p>\n

    \u2015 Voltaire<\/p>\n<\/blockquote>\n

    4) Don’t\u00a0Read Enough<\/span><\/h4>\n

    A reader lives a thousand lives before he dies, said Jojen. The man who never reads lives only one.<\/em><\/p>\n

    \u2015 George R.R. Martin, A Dance with Dragons<\/i><\/span><\/p>\n<\/blockquote>\n

    I have found reading extremely helpful throughout my career in data science. The most powerful aspect of reading is the way it helps us generate ideas and also\u00a0communicate those ideas. Data scientists across the globe are doing some really cool work and reading is our gateway to access that work. In addition to books, there are so many other resources for a data scientist to gain knowledge including\u00a0academic articles, research papers, white papers, blogs, LinkedIn articles etc. Reading is a highly disciplined activity and it is easy to slip out of it when there is excessive workload. However, I believe, daily reading should be a part of the job description for every data scientist.\u00a0I recommend that for a successful career in data science you spend\u00a0at least an hour out of you daily job to read.<\/p>\n

    \n

    It is what you read when you don’t have to that determines what you will be when you can’t help it.<\/em><\/p>\n

    \u2015 Oscar Wilde<\/p>\n<\/blockquote>\n

    5) Fail to Simplify<\/span><\/h4>\n
    \n

    Everything should be made as simple as possible, but not simpler\u00a0<\/em><\/p>\n

    \u2015\u00a0<\/em>Albert Einstein<\/p>\n<\/blockquote>\n

    At the core of any data science activity, which is often surrounded with complicated mathematics, hacking, and analysis, lies a simple idea. Simplification is getting at the core of that idea. It is often believed that you must simplify things for others i.e. your business users and audience. On the contrary, I believe, simplification is an activity you must do for yourself. It helps you develop a deeper relationship with your work.<\/p>\n

    \n

    Simplicity is the ultimate sophistication.<\/span><\/em><\/p>\n

    \u2015\u00a0<\/em>Leonardo da Vinci<\/div>\n<\/blockquote>\n

    6) Don’t\u00a0Sell Well<\/span><\/h4>\n

    The story of the human race is the story of men and women selling themselves short.<\/em> <\/span><\/p>\n

    \u2015\u00a0<\/span>Abraham Maslow<\/p>\n<\/blockquote>\n

    Many data scientists believe\u00a0\u00a0that selling is not a part of their job,\u00a0and trust me they can’t be more wrong. Whether you are working with internal or external customers selling is an integral part of your job. \u00a0To explain my point even the greatest scientist had to sell their science:\u00a0Einstein sold Relativity, Darwin had to sell Evolution, and Newton sold Gravity. These greatest creations of the human mind would have stayed in oblivious had it been not for the great salesmanship for their creators.<\/p>\n

    \n

    I am an artisan. I only became an artist when people watch what I do. That is when it becomes art.<\/em><\/p>\n

    \u2015\u00a0Rhys Ifans<\/p>\n<\/blockquote>\n

    The most important aspect for a data scientist is to ensure that their work gets integrated with business processes. Trust me this requires some hard selling. If you believe your solution has the value you need to sell it well to show its promises.<\/p>\n

    \n

    Salesmanship is limitless. Our very living is selling. We are all salespeople.<\/span><\/p>\n

    \u2015 \u00a0James Cash Penney<\/div>\n<\/blockquote>\n

    Sign-off Note<\/span><\/h4>\n

    These are some of the important lessons I have learned in my career in data science. I must say I didn’t know them at the beginning and I hope they will help you with\u00a0your career.<\/p>\n

    \n

    Come, Watson, come!’ [Sherlock Holmes] cried. ‘The game is afoot. Not a word! Into your clothes and come!<\/p>\n

    \u2015 \u00a0Sherlock Holmes<\/p>\n<\/blockquote>\n","protected":false},"excerpt":{"rendered":"

    Over the years in my career in data science and predictive analytics, I have noticed some awful\u00a0practices\u00a0that young and sometimes seasoned analysts follow. These bad practices, I believe, throw careers of these data scientists on a collision course similar to the Titanic. I will present the six worst mistakes that I feel are at the<\/p>\n

    Read More…<\/a><\/p>\n","protected":false},"author":1,"featured_media":5888,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_newsletter_tier_id":0,"jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false}}},"categories":[62,78],"tags":[],"jetpack_publicize_connections":[],"yoast_head":"\n6 Worst Mistakes for Data Scientists, and How to Avoid Them – YOU CANalytics |<\/title>\n<meta name=\"description\" content=\"This article lists six worst mistakes for data scientists, and uses quotable quotes to explain ways to avoid them.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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