DS_Interview_Tips.ipynb

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   "source": [
    "# Unriddle Data Science Interview Process"
   ]
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  {
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   "source": [
    "## Outline\n",
    "\n",
    "* Sub-domains in Data Science\n",
    "* Much needed ingredient: skill-set\n",
    "* Let's align: Skill-set and Job Roles\n",
    "* Acquire skill-set: Schedule and Projects\n",
    "* Dilemma: Certification or Projects\n",
    "* General Interview Process\n",
    "* FAQs in interview\n",
    "    * Topics\n",
    "* Tips and Trick for ML applications\n",
    "    * Discussion: Data points\n",
    "    * Chosse the right ML algorithm\n",
    "    * Architecture / System Design\n",
    "    * Big-Data Frameworks – needed or not?\n",
    "* Communication and Storytelling\n",
    "* General Tips\n",
    "    * Resume\n",
    "    * Use Social-Media platform\n",
    "* Handel: Out-of-the-box or odd questions\n",
    "* Learning Resources\n",
    "* Open house Q/A"
   ]
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  {
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   "source": [
    "## Sub-domains in Data Science\n",
    "\n",
    "\n",
    "<img src=\"https://github.com/raw/jalajthanaki/Get_Jobs_in_Data_Science/master/imgs/Figure_5.png\" \n",
    "align=\"middle\" alt=\"Image_5\" data-canonical-src=\"\" style=\"width:50%;height:50%\">"
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   "source": [
    "## Much needed ingredient: skill-set\n",
    "\n",
    "\n",
    "### Basic Skills\n",
    "* How to search on Google: \"Google is your Friend...!\"\n",
    "* OS: Linux \n",
    "* Math skills\n",
    "\n",
    "    * Basic concepts of Linear Algebra\n",
    "    * Basic concepts of Statistics / Probability\n",
    "    * Basic concepts of Calculus\n",
    "    \n",
    "* Programing Skills\n",
    "    \n",
    "    * Basic concepts of (C++ or Java) and (Python or R)\n",
    "    * Basic concepts of Data Structure and ALgorithms\n",
    "\n",
    "* Data-base:\n",
    "    \n",
    "    * SQL (CURD, filtering, Aggrigation, Sorting, grouping, joins )\n",
    "    * Any structure data bases : Oracle, MySQL and so on...\n",
    "\n"
   ]
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    "## Much needed ingredient: skill-set\n",
    "\n",
    "\n",
    "### Basic Skills\n",
    "\n",
    "* Basic Programming Libraries\n",
    "\n",
    "    * Numpy / Scipy - Scientific Calculation libraries\n",
    "    * Pandas - For data manipulation\n",
    "    * Matplotlib - For data visualization\n",
    "    * GitHub - For version control / Code management \n",
    "    \n",
    "* Visualization \n",
    "* Story-telling"
   ]
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   "source": [
    "## Much needed ingredient: skill-set\n",
    "\n",
    "\n",
    "### Intermediate\n",
    " \n",
    "* Math Skills\n",
    "    * Intermediate concepts of Linear ALgebra\n",
    "    * Intermediate concepts of Statistics / Probability\n",
    "    * Intermediate concepts of Calculus\n",
    "\n",
    "* Programing Skills\n",
    "    * Object oriented concepts of Python\n",
    "    * Unit Test Cases in python\n",
    "\n",
    "* NoSql Databases\n",
    "    * MongoDB\n",
    "    * Cassendra\n",
    "\n",
    "\n"
   ]
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   "source": [
    "\n",
    "## Much needed ingredient: skill-set\n",
    "\n",
    "\n",
    "### Intermediate\n",
    "\n",
    "* ML Skills\n",
    "    * Understand basics of ML algorithms\n",
    "    * Learn about Exploratory Data Analysis (EDA) via small projects\n",
    "    * Implementation of ML algorithms:  Scikit-Learn or Numpy\n",
    "    * Basic about Feature Engineering and Feature Selection\n",
    "\n",
    "* DS Sub-domain specific skiils\n",
    "    * NLP\n",
    "        * Algorithms for NLP\n",
    "        * linguistic\n",
    "        * NLP libraries\n",
    "        \n",
    "    * Computer Vision\n",
    "        * Algorithms for CV\n",
    "        * CV libraries"
   ]
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   "source": [
    "## Much needed ingredient: skill-set\n",
    "\n",
    "\n",
    "### Advance\n",
    "\n",
    "\n",
    "* Math Skills\n",
    "    * Advance statistical techniques\n",
    "    * Concepts of Information Theory\n",
    "* Programming Skill\n",
    "    * Design Pattern\n",
    "* ML optimization\n",
    "    * Feature Engineering\n",
    "    * Hyper Parameter Tunning\n",
    "    * AutoML\n",
    "    * Start with DL\n",
    "        * Understand various DL algorithms\n",
    "        * Implement DL algos using libraries like TensorFLow and PyTorch\n"
   ]
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   "source": [
    "## Much needed ingredient: skill-set\n",
    "\n",
    "\n",
    "### Advance\n",
    "\n",
    "\n",
    "* Cloud Platform\n",
    "    * AWS\n",
    "    * Google Cloud\n",
    "    \n",
    "* Big data toolkit\n",
    "    * Haddop\n",
    "    * Spark\n",
    "    * Flink\n",
    "    * Flume"
   ]
  },
  {
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   "source": [
    "# Let's align: Skill-set and Job Roles"
   ]
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    "<img src=\"https://github.com/raw/jalajthanaki/Get_Jobs_in_Data_Science/master/imgs/Figure_6.jpg\" \n",
    "align=\"middle\" alt=\"Image_6\" data-canonical-src=\"\" style=\"width:100%;height:100%\">"
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    "<img src=\"https://github.com/raw/jalajthanaki/Get_Jobs_in_Data_Science/master/imgs/Figure_7.jpg\" \n",
    "align=\"middle\" alt=\"Image_7\" data-canonical-src=\"\" style=\"width:100%;height:100%\">"
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    "<img src=\"https://github.com/raw/jalajthanaki/Get_Jobs_in_Data_Science/master/imgs/Figure_8.jpg\" \n",
    "align=\"middle\" alt=\"Image_8\" data-canonical-src=\"\" style=\"width:100%;height:100%\">"
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    "<img src=\"https://github.com/raw/jalajthanaki/Get_Jobs_in_Data_Science/master/imgs/Figure_11.jpg\" \n",
    "align=\"middle\" alt=\"Image_11\" data-canonical-src=\"\" style=\"width:100%;height:100%\">\n"
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  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "<img src=\"https://github.com/raw/jalajthanaki/Get_Jobs_in_Data_Science/master/imgs/Figure_14.png\" \n",
    "align=\"middle\" alt=\"Image_14\" data-canonical-src=\"\" style=\"width:100%;height:100%\">"
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   "source": [
    "## Acquire skill-set: Schedule and Projects\n"
   ]
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   "metadata": {
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    "<img src=\"https://github.com/raw/jalajthanaki/Get_Jobs_in_Data_Science/master/imgs/Figure_15.png\" \n",
    "align=\"middle\" alt=\"Image_15\" data-canonical-src=\"\" style=\"width:80%;height:80%\">"
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    "<img src=\"https://github.com/raw/jalajthanaki/Get_Jobs_in_Data_Science/master/imgs/Figure_16.png\" \n",
    "align=\"middle\" alt=\"Image_16\" data-canonical-src=\"\" style=\"width:80%;height:80%\">"
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    "<img src=\"https://github.com/raw/jalajthanaki/Get_Jobs_in_Data_Science/master/imgs/Figure_17.png\" \n",
    "align=\"middle\" alt=\"Image_17\" data-canonical-src=\"\" style=\"width:80%;height:80%\">"
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  {
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    "<img src=\"https://github.com/raw/jalajthanaki/Get_Jobs_in_Data_Science/master/imgs/Figure_18.png\" \n",
    "align=\"middle\" alt=\"Image_18\" data-canonical-src=\"\" style=\"width:80%;height:80%\">"
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   "source": [
    "## Revision Before Interview\n",
    "\n",
    "![title](imgs/Figure_19.png)"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "### Example Projects\n",
    "\n",
    "* __Analytics__\n",
    "\n",
    "    * Market Segmentation\n",
    "    * Credit Risk Modeling\n",
    "    * Sales Prediction / Time Series Analysis\n",
    "\n",
    "* __NLP__\n",
    "\n",
    "    * Sentiment Analysis\n",
    "    * Summarization\n",
    "    * Chatbot\n",
    "\n",
    "* __Computer Vision__\n",
    "\n",
    "    * OCR Tools\n",
    "    * Digit Recognition\n",
    "    * Object Detection\n",
    "    * Face Recognition\n",
    "    "
   ]
  },
  {
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   "metadata": {
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     "slide_type": "slide"
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   },
   "source": [
    "## Dilemma: Certification or Projects\n",
    "\n",
    "**Certification**\n",
    "\n",
    "* Important to acquire skill-set over certification\n",
    "* Certification provide you credibility and motivation\n",
    "\n",
    "**Projects**\n",
    "\n",
    "* Give you hands-on experience\n",
    "* Give you much more details about theoretical concepts"
   ]
  },
  {
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   "metadata": {
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   },
   "source": [
    "## General Hiring Process\n",
    "\n",
    "* Really Company Specific\n",
    "\n",
    "* __General Process - when you apply for the job__\n",
    "    * Basic HR Screening\n",
    "    * Coding Interview \n",
    "    * ML/DL Interview\n",
    "    * Small Toy project\n",
    "    * Final round / Managerial round\n",
    "    * Get offer\n",
    "    \n",
    "* __General Process - Mass recruitment__\n",
    "    * Aptitude Test\n",
    "    * Coding Test\n",
    "    * Coding Interviews \n",
    "        * Number of round 1 to 4\n",
    "        * 30 mins to 1.5 Hour / Each round\n",
    "    * ML/DL Interview\n",
    "    * Final round / Managerial round\n",
    "    * HR Interview\n",
    "    * Get Offer\n",
    "    "
   ]
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   "source": [
    "## FAQs in interview\n",
    "\n",
    "### Topics\n",
    "\n",
    "\n",
    "* Computer Science Basics\n",
    "    * OOPS concepts\n",
    "    * Coding skills - White-board programming\n",
    "        * Coding problem\n",
    "        * Time-space complexity\n",
    "        * Choosing right algorithm\n",
    "        * Data Structure (Stack, queue, array, hashmap, hashtable, set)\n",
    "    * SQL - queries\n",
    "    \n"
   ]
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   "source": [
    "## FAQs in interview\n",
    "\n",
    "### Topics\n",
    "\n",
    "* Data Analysis and ML algorithms\n",
    "    * What is regression,classification and multiclass classification\n",
    "    * Gradient Descent \n",
    "    * Basic concepts of Statistics and probability theory\n",
    "    * Project details (based on portfolio)\n",
    "    * Database details\n",
    "    * Reason of choosing X algorithm and not Y\n",
    "    * Intuition and math of any ML algorithm\n",
    "    * Loss functions\n",
    "    * Performance metrics"
   ]
  },
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   "source": [
    "## FAQs in interview\n",
    "\n",
    "### Topics\n",
    "   \n",
    "* Optimization of ML applications\n",
    "    * When to use which ML algorithm\n",
    "    * Sub-sampling\n",
    "    * Cross-Validation\n",
    "    * Regularization\n",
    "    * Which parameter for what task\n",
    "    * Parameter-tunning\n",
    "    * Overfitting -Underfitting\n",
    "    * Bias - Variance\n",
    "    * Bagging, Boosting and Staking\n",
    "    * Activation functions\n",
    "    * Datascience pipeline and architecture"
   ]
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   "source": [
    "## FAQs in interview\n",
    "\n",
    "### Questions for Data Analyst Profile\n",
    "\n",
    "* Based on resume and project\n",
    "* Basics of Computer Science and Data Structure\n",
    "* Given X problem come up with the hypothesis\n",
    "* P-value\n",
    "* Questions on SQLs\n",
    "* Data types\n",
    "* Data Points - Linearly separable or not?\n",
    "* Data preparation \n",
    "* Data cleaning\n",
    "* Data scrapping\n",
    "* mean, median, mode, Standard deviation, Variance\n",
    "* Statistical distributions\n",
    "* Outlier detection\n",
    "* Probability - Join Probability, Conditional Probability, Marginal Probability\n",
    "* Naive-Bayes\n",
    "\n",
    "\n",
    "Some questions are [here](https://www.analyticsvidhya.com/blog/2017/05/41-questions-on-statisitics-data-scientists-analysts/)"
   ]
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    "## Tips and Trick for ML applications\n",
    "\n",
    "### Discussing Data points\n",
    "\n",
    "* Domain of company\n",
    "\n",
    "* Application details\n",
    "\n",
    "* Current data points\n",
    "\n",
    "* End Users\n",
    "\n",
    "* Proposed data points list\n"
   ]
  },
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   "source": [
    "## Tips and Trick for ML applications\n",
    "\n",
    "### Choosing the right ML algorithm\n",
    "\n"
   ]
  },
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    "<img src=\"https://github.com/raw/jalajthanaki/DataScience_Interview_Seminar/master/imgs/Figure_20.png\" \n",
    "align=\"middle\" alt=\"Image_20\" data-canonical-src=\"\" style=\"width:1200px;height:600px\">"
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   "source": [
    "## Tips and Trick for ML applications\n",
    "\n",
    "### Architecture / System Design\n",
    "\n",
    "* Modular\n",
    "* Easy-to-maintain\n",
    "* Loosely coupled\n",
    "* Choose tools and design - based on application requirements\n",
    "* One Architecture not fit to all\n",
    "* Customized Architecture\n"
   ]
  },
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   "source": [
    "## Tips and Trick for ML applications\n",
    "\n",
    "### Lambda architecture\n",
    "\n",
    "* __Lambda architecture__ - Where everything is Batch + Streaming and approximate Real-time\n",
    "    \n",
    "    * Healthcare - Clinical Data\n",
    "    \n",
    "    * Weather - Intelligent Weather forecasting and other analysis for climate change\n",
    "    \n",
    "![title](imgs/LambdaKappa1_1.png)"
   ]
  },
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   "metadata": {
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   },
   "source": [
    "## Tips and Trick for ML applications\n",
    "\n",
    "### Kappa architecture\n",
    "\n",
    "* __Kappa architecture__ - Where everything is streamed and Real-time\n",
    "    \n",
    "    * Oil & Gas - real time drilling, connected wells, smart refineries\n",
    "\n",
    "    * Healthcare - wearables and patient tracking (IoTs)\n",
    "    \n",
    "    * Manufacturing - smart factories\n",
    "    \n",
    "    * Communications - consumer behavior and new service deployment\n",
    "    \n",
    "![title](imgs/LambdaKappa1_2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "## Tips and Trick for ML applications\n",
    "\n",
    "### Big-Data Frameworks – needed or not?\n",
    "\n",
    "* Not necessary at first go (for Data Analyst role)\n",
    "* If you wrote in resume then you must need to prove the skill\n",
    "* Demonstrate - Willingness to learn"
   ]
  },
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   "source": [
    "## Communication and Storytelling\n",
    "\n",
    "* Express your skills, experience and belief via story telling\n",
    "* Non-fiction, effective and to the point\n",
    "* __Why__, __What__, __How__ and __For Whom__ are the important aspects\n",
    "* Must have versions for stories"
   ]
  },
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   },
   "source": [
    "## General Tips\n",
    "### Resume\n",
    "\n",
    "* To the point\n",
    "* Honest\n",
    "* Customized details \n",
    "    * based on job roles \n",
    "    * based on company profile\n",
    "* Max 2 pages\n",
    "\n",
    "\n",
    "\n"
   ]
  },
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   "source": [
    "## Resume Formats\n",
    "\n",
    "### Students\n",
    "\n",
    "* Contact details on Header\n",
    "* Education and coursework details\n",
    "* Skills\n",
    "* Project Work (reverse chronological order)\n",
    "    * Accomplished [X] as measured by [Y] by doing [Z]\n",
    "* Achievements\n",
    "* Personal Identification\n",
    "\n",
    "\n",
    "### Experienced\n",
    "\n",
    "* Contact details on Header\n",
    "* Skills\n",
    "* Education details\n",
    "* Experience (reverse chronological order)\n",
    "    * Accomplished [X] as measured by [Y] by doing [Z]\n",
    "* Achievements\n",
    "* Personal Identification\n"
   ]
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   "source": [
    "## General Tips\n",
    "\n",
    "### Use Social-Media platform\n",
    "\n",
    "* LinkedIn / Twitter: Keep **yourself up-to-date**\n",
    "* Subreddits: Give you **details about new tools and explanation about research papers**\n",
    "* GitHub: **Resume of new era** where you **put your work portfolio**\n",
    "* Share your knowledge with others\n"
   ]
  },
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   "metadata": {
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   "source": [
    "## General Tips\n",
    "\n",
    "### Handling: Out-of-the-box or odd questions\n",
    "\n",
    "* No right or wrong answer\n",
    "* Ask for clarification\n",
    "* Use common-sense backed by reasonable logic \n",
    "* Judge your reaction rather then skills"
   ]
  },
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   },
   "source": [
    "## Learning Resources\n",
    "\n",
    "\n",
    "* All learning resources are [Here](https://github.com/jalajthanaki/DataScience_Interview_Seminar/blob/master/Learning_resources.ipynb)\n"
   ]
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