https://www.datacamp.com/community/news/how-to-manage-title-confusion-in-data-science-1czj5ebpd29

As anyone who has tried to discern the “true definition” of a data scientist knows, titles can vary. How can you manage role expectations while job searching and know what you're getting into?

How to Manage Title Confusion in Data Science

https://www.datacamp.com/community/news/2018-data-science-hiring-survey-results-4f6br4d8h3f

After examining Attrition and Tenure of data scientists last year, we surveyed hiring managers to ask about their  hiring intentions for the beginning of 2018. The results were interesting!


2018 Data Science Hiring Survey Results

https://www.datacamp.com/community/news/data-science-resources-9juumimyaps

Data Science Resources!

<p><center><a href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603718763/banner_style_1_cruuf7.png" /></a><br></p>
<p>Data engineering continues to be a top priority for enterprises, and in 2021, there will be exciting developments in the data infrastructure space. In a <a href="https://www.datacamp.com/resources/webinars/operationalizing-data-within-large-organizations">recent webinar</a>, Maksim Percherskiy, Data Engineer at The World Bank and former Chief Data Officer of the City of San Diego, highlighted three trends to watch out for in particular: data orchestration platforms, data discovery engines, and data lakehouses.</p>
<h2 id="data-orchestration-platforms">Data orchestration platforms</h2>
<p>Although orchestration platforms have been around for many years to manage computer systems and software, <a href="https://dzone.com/articles/data-orchestration-its-open-source-but-what-is-it">data orchestration</a> is a relatively new concept that abstracts data access across storage systems, virtualizes data, and presents data to data-driven applications. Data orchestration platforms help companies become more data-driven by combining siloed data from multiple data storage locations and making them usable. Examples include <a href="https://airflow.apache.org/">Apache Airflow</a>, <a href="https://www.prefect.io/">Prefect</a>, <a href="https://luigi.readthedocs.io/en/stable/">Luigi</a>, and <a href="https://www.stitchdata.com/">Stitch</a>, which are compatible with modern software approaches like version control, DevOps, and continuous integration.</p>
<p>DataCamp’s course <a href="https://www.datacamp.com/courses/introduction-to-airflow-in-python">Introduction to Airflow in Python</a> is a great place to start to learn how to implement and schedule ETL and data engineering workflows in an easy and repeatable way.</p>
<h2 id="data-discovery-engines">Data discovery engines</h2>
<p>It makes sense that as data increases, companies will invest more time in enabling their teams to find the data they need, document it, and reduce rework. Data discovery engines like <a href="https://eng.lyft.com/amundsen-lyfts-data-discovery-metadata-engine-62d27254fbb9?gi=a4af847eb7fe">Lyft’s Amundsen</a> and <a href="https://eng.uber.com/databook/">Uber’s Databook</a> aim to improve the productivity of data users by providing a search interface for data. These tools rely on metadata, which support productivity and compliance by allowing data infrastructure to scale as companies grow. The goal of data discovery is to make data more FAIR: findable, accessible, interoperable, and reusable.</p>
<p><center><a href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1610377093/datadiscoveryengines_pi2uj7.png" /></a><br></p>
<h2 id="data-lakehouse">Data lakehouse</h2>
<p>There are different use cases for <a href="https://www.datacamp.com/community/blog/data-lakes-vs-data-warehouses">data lakes versus data warehouses</a>. Each is designed for different purposes and for different end users. For example, data warehouses are typically used by analysts for the purpose of collecting business intelligence—they contain flat files that have been explicitly processed for their work. On the other hand, data lakes are set up and maintained by data engineers who integrate them into data pipelines. Data scientists work more closely with data lakes as they contain data of a wider and more current scope.</p>
<p>Data lakehouses are exactly what they sound like: a combination of data lakes and data warehouses. Companies like <a href="https://delta.io/">Delta Lake</a> and <a href="https://www.snowflake.com/guides/what-data-lakehouse">Snowflake</a> have created solutions to provide a single source of truth for data that combines the benefits of data lakes and data warehouses. Data lakehouses implement data warehouses’ data structures and management features for data lakes, which are typically more cost-effective for data storage. At the same time, they retain the schema and versioning of the data. With data lakehouses, data scientists will be able to conduct both machine learning and business intelligence.</p>
<p><center><a href="https://databricks.com/blog/2020/01/30/what-is-a-data-lakehouse.html"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1610377091/datalakehouse_dv35im.png" /></a></p>
<h5 id="source-databricks-https-databricks-com-blog-2020-01-30-what-is-a-data-lakehouse-html-">Source: <a href="https://databricks.com/blog/2020/01/30/what-is-a-data-lakehouse.html">Databricks</a></h5>
<p>All three of these trends will be crucial for enterprises to scale data infrastructure in 2021. They’re useful for companies that were built around data as well as for those that are struggling to sustainably integrate data in their workflows. <a href="https://www.datacamp.com/community/blog/data-driven-enterprise">Find out more</a> about the challenges large companies face to operationalize their data and become data-driven.</p>
<p><center><a href="https://www.datacamp.com/business/demo"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603718763/banner_style_2_m573ef.png" /></a></p>


https://www.datacamp.com/community/blog/data-infrastructure-trends

Get your data engineering function ahead of the curve with orchestration platforms, data discovery engines, and data lakehouses.

Top 3 Trends in Data Infrastructure for 2021 | DataCamp

Top 3 Trends in Data Infrastructure for 2021

<center><a href="https://www.datacamp.com/tracks/tableau-fundamentals"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1610375720/TableauCTA_v2qbt7.png" /></a></center><br>

<p>Tableau is a business intelligence (BI) platform that functions like a supercharged Microsoft Excel. Its user-friendly, drag-and-drop functionality makes it possible for anyone who interacts with data to analyze and create presentation-ready visualizations—from data newbies to analysts, and even business leaders. You definitely don’t need to be a seasoned data scientist to use Tableau!</p>

<h2>Which companies use Tableau?</h2>
<p>Tableau’s roster of customers is a who’s who of the business world, including well-known brands such as Amazon, Experian, and Unilever. But it’s not just global giants using Tableau to democratize their data—over <a href="https://techcrunch.com/2019/06/10/salesforce-is-buying-data-visualization-company-tableau-for-15-7b-in-all-stock-deal/">86,000 businesses</a> are using Tableau to extract and share insights from their data.</p>

<h2>Certification overview</h2>

<center><CALLOUT BOX>
<a href="https://www.tableau.com/learn/certification/desktop-specialist">Tableau Desktop Specialist</a><br>
Fee: $100<br>
Title valid for: No expiration<br>
</CALLOUT BOX></center><br>


<p>Tableau recommends that you have at least three months’ product experience before taking the Desktop Specialist exam. If you don’t yet have this experience, DataCamp recommends that you prepare for the exam by completing our online <a href="https://www.datacamp.com/tracks/tableau-fundamentals">Tableau Fundamentals</a> skill track.</p>

<p>The exam will take place on GoToMeeting or Zoom, and you will be asked to set up your workspace using Google Chrome and confirm that your connection speed meets Tableau’s requirements. Additionally, you will be required to:</p>

<ul>
<li>Present photo identification to the exam proctor via webcam</li>
<li>Close programs, tabs, and remove any paperwork that may help during your exam</li>
<li>Demonstrate to the proctor that you are alone in your room via webcam</li>
<li>Agree to being monitored via webcam for the duration of the exam</li>
</ul>

<p><a href="https://mkt.tableau.com/files/Tableau-Certification-4-steps-to-exam-success.pdf">View the full exam setup requirements</a></p>

<p>Just like the Tableau learning experience <a href="https://www.datacamp.com/tracks/tableau-fundamentals">on DataCamp</a>, the exam is completed using a Windows virtual machine that has Tableau Desktop installed—this may involve a certain amount of toggling between the question and virtual desktop screens which may result in a slightly smaller screen size than you may be used to. It’s also worth noting that you may not have any other windows or programs open for the duration of the test other than those required for the assessment—just like exams in school, you can’t have any notes hidden in your calculator sleeve. The exam is scored automatically and a passing score of 70% or more is required to obtain your certification.</p>

<p>You can register and book your exam on the <a href="https://www.tableau.com/learn/certification/desktop-specialist">Tableau Product Certification Exam center</a>, managed by Loyalist Exam Services (LES). You can also reschedule your exam using this site.</p>

<h2>Training Resources</h2>
<p>At DataCamp, all our instructor-led courses are fully interactive, with no installation or prior experience required. You will also receive a statement of accomplishment which you can also share on your LinkedIn profile upon completion. Please note that DataCamp’s statement of accomplishment is not the same as the Tableau Desktop Specialist certification:</p>

<p><a href="https://www.datacamp.com/tracks/tableau-fundamentals">Tableau Fundamentals</a>: 14 hours. 4 Courses</p>
<p>Gain the essential skills you need to confidently use Tableau and prepare to pass the Tableau Desktop Specialist certification. No prior experience required.</p>

<ul>
<li><a href="https://www.datacamp.com/courses/introduction-to-tableau">Introduction to Tableau</a>: 4 hours<br>
Learn how to use Tableau’s user-friendly drag-and-drop functionality to quickly clean, analyze, and visualize your team’s data.</li>
<li><a href="https://datacamp.com/courses/analyzing-data-in-tableau">Analyzing data in Tableau</a>: 4 hours<br>
Create detail-rich map visualizations, configure date and time fields to show trends over time, and extend your data using Calculated Fields.</li>
<li><a href="https://datacamp.com/courses/creating-dashboards-in-tableau">Creating Dashboards in Tableau</a>: 4 hours<br>
Apply dashboard best practices, add interactive or explanatory elements, and use actions to make your dashboard fully interactive.</li>
<li><a href="https://www.datacamp.com/courses/connecting-data-in-tableau">Connecting Data in Tableau</a>: 4 hours<br>
Discover how  to connect Tableau to different data sources and prepare the data for a smooth analysis.</li>
</ul>

<p>Tableau also provides an excellent choice of learning resources you can use to prepare for the exam:</p>

<ul>
<li><a href="https://www.tableau.com/learn/training/20204">Tableau Training Videos</a></li>
<li><a href="https://mkt.tableau.com/files/DesktopSpecialist_ExamGuide.pdf">Desktop Specialist Exam Prep Guide (PDF)</a></li>
<li><a href="https://community.tableau.com/s/explore-forums?_ga=2.195627941.886887347.1610106578-1596736181.1607685378">Tableau community forums</a> and <a href="https://community.tableau.com/s/groups?_ga=2.195627941.886887347.1610106578-1596736181.1607685378">user groups</a></li>
</ul>

<p>Found this article useful? If so please share on <a href="https://www.linkedin.com/company/datacampinc/">LinkedIn</a>, <a href="https://twitter.com/DataCamp">Twitter</a>, or <a href="https://www.facebook.com/datacampinc/">Facebook</a>.</p>

<br><center><a href="https://www.datacamp.com/tracks/tableau-fundamentals"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1610375720/TableauCTA_v2qbt7.png" /></a></center>


https://www.datacamp.com/community/blog/Tableau-desktop-specialist-certification

Add Tableau to your data analyst skill set. Learn to use this BI platform and pass the Desktop Specialist Certification exam with DataCamp.

Pass Tableau’s Desktop Specialist Certification | DataCamp

Tableau Desktop Specialist Certification: Learn How to Use Tableau and Ace the Exam

<p>After learning on an online interactive training and education platform like Datacamp, one of the next steps is taking that skill gained in Python, R, Git, or Unix Shell and using it on your local computer. It is not always easy to know what you need to install for the various projects you have in mind. This tutorial will let you know what packages, what softwares you need to install to get started with the various technologies. This tutorial will include:</p>
<p><nav></p>
<ul>
<li><p><a href="#anaconda">The benefits of the Anaconda Python Distribution as well as how to install it on your operating system.</a></p>
</li>
<li><p><a href="#R">The benefits of using R and RStudio together as well as how to install them on your operating system.</a></p>
</li>
<li><p><a href="#Unix">The benefits of a Unix Shell as well as how to use it on your operating system.</a></p>
</li>
<li><p><a href="#Git">The benefits of using Git as well as how to install it on your operating system. </a>
</nav>
<div id="scoped-content">
<style type="text/css">:target:before { content:&quot;&quot;; display:block; height:150px; margin:-150px 0 0; } h3 {font-weight:normal; margin-top:.5em} h4 { font-weight:lighter }
</style>
With that, let&#39;s get started!</p>
</li>
</ul>
<h2 id="python">Python</h2>
<p>To be able to use Python on your local computer, you first need to install it. There are many different python distributions, but for data science, the Anaconda Python Distribution is the most popular.</p>
<p><a id="anaconda"></a></p>
<h3 id="benefits-of-anaconda">Benefits of Anaconda</h3>
<p>Anaconda is a package manager, an environment manager, and Python distribution that contains a collection of many open source packages. An installation of Anaconda comes with many packages such as numpy, scikit-learn, scipy, and pandas preinstalled and it is also the <a href="http://jupyter.org/install.html">recommended way to install Jupyter Notebooks</a>. The image below shows a Jupyter Notebook in action. Jupyter notebooks contain both code and rich text elements, such as figures, links, and equations. You can learn more about Jupyter Notebooks <a href="https://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook">here</a>.</p>
<p><img src="http://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1528918831/jupyterNotebook_oawzyh.gif" /></p>
<p>Some other benefits of Anaconda include:</p>
<ul>
<li><p>If you need additional packages after installing Anaconda, you can use Anaconda&#39;s package manager conda or pip to install those packages.This is highly advantageous as you don&#39;t have to manage dependencies between multiple packages yourself. Conda even makes it easy to switch between Python 2 and 3 (you can learn more <a href="https://towardsdatascience.com/environment-management-with-conda-python-2-3-b9961a8a5097">here</a>).</p>
</li>
<li><p>Anaconda comes with Spyder, a Python Integrated Development Environment. An Integrated Development Environment is a coding tool which allows you to write, test and debug your code as they typically offer code completion, code insight by highlighting, resource management and debugging tools among many other features. It is also possible to integrate Anaconda with other  Python Integrated Development Environments including PyCharm and Atom. You can learn more about different Python Integrated Development Environments <a href="https://www.datacamp.com/community/tutorials/top-python-ides-for-2019">here</a>.</p>
</li>
</ul>
<h3 id="how-to-install-anaconda-python-">How to Install Anaconda (Python)</h3>
<p>Here are some links to guides below on how to install Anaconda on your operating system.</p>
<p><a href="https://www.datacamp.com/community/tutorials/installing-anaconda-mac-os-x">Install Anaconda on Mac</a></p>
<p><a href="https://medium.com/@GalarnykMichael/install-python-on-windows-anaconda-c63c7c3d1444">Install Anaconda on Windows</a></p>
<p><a id="R"></a></p>
<h2 id="r-programming-language">R Programming Language</h2>
<p>Most people generally install RStudio alongside the R programming language. The RStudio integrated development environment (IDE) is generally considered the easiest and best way to work with the R Programming language.</p>
<h3 id="benefits-of-rstudio">Benefits of RStudio</h3>
<p>An install of the R programming language gives you a set of functions and objects from the R language and an R interpreter that allows you to build and run commands. RStudio gives you an integrated development environment that works <b>alongside</b> the R interpreter.</p>
<p><img src="http://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1528918830/RStudio_abcd_pp8pks.png" /></p>
<p>When you open RStudio, an screen like the one above appears. A few features in contained in the four RStudio Panes are: (A) a Text Editor. (B) Dashboard to Work Environment. (C) R Interpreter. (D) Help Window and Package Management System. All these features make it so RStudio is all you really need after installing R.</p>
<h3 id="how-to-install-r-and-rstudio">How to Install R and RStudio</h3>
<p>Here are some links to guides below on how to install R and RStudio on your operating system.</p>
<p><a href="https://medium.com/@GalarnykMichael/install-r-and-rstudio-on-mac-e911606ce4f4">Install R and RStudio on Mac</a></p>
<p><a href="https://medium.com/@GalarnykMichael/install-r-and-rstudio-on-windows-5f503f708027">Install R and RStudio on Windows</a></p>
<p><a id="Unix"></a></p>
<h2 id="unix-shell">Unix Shell</h2>
<p>Navigating directories, copying files, using virtual machines, and more are a regular part of a data scientist&#39;s job. You will often find the Unix Shell utilized to accomplish these tasks.</p>
<h3 id="some-uses-of-a-unix-shell">Some Uses of a Unix Shell</h3>
<p>1 - Many Cloud Computing Platforms are Linux based (utilize a flavor of Unix Shell). For instance, if you want to <a href="https://www.datacamp.com/community/tutorials/google-cloud-data-science">Setup a Data Science Environment on Google Cloud</a>, or do <a href="https://www.datacamp.com/community/tutorials/deep-learning-jupyter-aws">Deep Learning With Jupyter Notebooks In The Cloud (AWS EC2)</a> it requires some Unix Shell knowledge. There are times when you may have a use for a <a href="https://www.datacamp.com/community/tutorials/aws-ec2-beginner-tutorial">Windows virtual machine</a>, but it is less common.</p>
<p>2 - Unix Shell provides a number of useful commands such as: <code>wc</code> command which counts the number of lines or words in a file, <code>cat</code> command which concatenates/merges files, <code>head</code> and <code>tail</code> commands which help you subset large files. You can learn more about this in <a href="https://www.datacamp.com/community/tutorials/shell-commands-data-scientist">8 Useful Shell Commands for Data Science</a>. Also, check out DataCamp&#39;s course <a href="https://www.datacamp.com/courses/data-processing-in-shell">Data Processing in Shell</a>.</p>
<p>3 - You will often find Unix Shell integrated with other technologies as you will see throughout the rest of the article.</p>
<h3 id="integration-with-other-technologies">Integration with Other Technologies</h3>
<p>You will often find Unix Shell commands integrated in other technologies. For example, it is common to find shell commands in Jupyter Notebooks alongside Python code. In Jupyter Notebook, you can access shell commands by escaping to the shell by using an <code>!</code>. In the code below, the result of the shell command <code>ls</code> (which lists all the files in the current directory) is assigned to the Python variable myfiles.</p>
<pre><code>myfiles = !ls
</code></pre><p>The image below shows some Python Code integrated in a workflow to combine multiple datasets. Notice a Unix Shell command (enclosed in the red rectangle) integrated in a Jupyter Notebook.
<img src="http://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1528918829/escapeJupyter_cyppxl.png" /></p>
<p>Keep in mind that the code in the image above isn&#39;t some unique way to do a task, but just a small example of how you may see Unix utilized. If you want to learn how to use Unix for Data Science, Datacamp has a free course <a href="https://www.datacamp.com/courses/introduction-to-shell-for-data-science">Introduction to Shell for Data Science</a> which I highly recommend. It is a skill that lots of aspiring data scientists forget about, but it is a very important skill in the workplace.</p>
<h3 id="unix-shell-on-mac">Unix Shell on Mac</h3>
<p>Mac comes with a Unix shell so you usually don&#39;t need to install anything! An important point is that there is a variety of Unix systems that have different commands. Sometimes you find that you don&#39;t have a Unix command (like <code>wget</code>) found on another Unix system. Similar to how you have package managers through RStudio and Anaconda, Mac can have a package manager called Homebrew if you install it. The link below goes over how to install and use Homebrew.</p>
<p><a href="https://www.datacamp.com/community/tutorials/homebrew-install-use">How to Install and Use Homebrew</a></p>
<h3 id="unix-shell-commands-on-windows">Unix Shell Commands on Windows</h3>
<p>Windows does not come with a Unix Shell. Keep in mind that what Unix Shell does for you is give you useful commands for Data Science. There are many different ways to get these useful commands on Windows. You can <a href="https://hackernoon.com/install-git-on-windows-9acf2a1944f0">Install Git on Windows</a> with the optional Unix tools so that you can have Unix commands on your Command Prompt. Alternatively, you could <a href="https://github.com/bmatzelle/gow">install Gnu on Windows (GOW)</a> (10mb), Cygwin (100mb minimum), among many other options.</p>
<p><a id="Git"></a></p>
<h2 id="git">Git</h2>
<p>Git is the most widely used version control system. A version control system is something that records changes to a file or set of files over time so that you can recall specific versions later. Git is an important technology as it really helps you work with others and it is something you will find in a lot of workplaces. Some of the benefits of learning Git include:</p>
<ul>
<li><p>Nothing version controlled using Git is ever lost, so you can always go back to see previous versions of your programs.</p>
</li>
<li><p>Git notifies you when your work conflicts with someone else&#39;s, so it&#39;s harder (but not impossible) to accidentally overwrite work.</p>
</li>
<li><p>Git can synchronize work done by different people on different machines, so it scales as your team does.</p>
</li>
<li><p>Knowing  Git makes it easier to contribute to open source development of packages in R and Python.</p>
</li>
</ul>
<h3 id="integration-with-other-technologies">Integration with Other Technologies</h3>
<p>One of the cool things about Git is you often find it integrated with other technologies. Earlier I mentioned that the RStudio integrated development environment (IDE) is generally considered the best way to work with the R Programming language. <a href="https://support.rstudio.com/hc/en-us/articles/200532077-Version-Control-with-Git-and-SVN">RStudio offers version control support</a> and most many Python Integrated Development Environments (IDE) (learn more <a href="https://www.datacamp.com/community/tutorials/data-science-python-ide">here</a>) offer version control support.</p>
<p>If you want to learn how to use Git for Data Science, DataCamp has a free course <a href="https://www.datacamp.com/courses/introduction-to-git-for-data-science">Introduction to Git for Data Science</a> which I highly recommend.</p>
<h3 id="how-to-install-git">How to Install Git</h3>
<p>Here are some links to guides below on how to install Git on your operating system.</p>
<p><a href="https://hackernoon.com/install-git-on-mac-a884f0c9d32c">Install Git on Mac</a></p>
<p><a href="https://hackernoon.com/install-git-on-windows-9acf2a1944f0">Install Git on Windows</a></p>
<h2 id="conclusion">Conclusion</h2>
<p>This tutorial provides a way to setup a local data science environment on your local computer. An important point to emphasize is that these technologies can and are often integrated together. If you any questions or thoughts on the tutorial, feel free to reach out in the comments below or through <a href="https://twitter.com/GalarnykMichael">Twitter</a>. Also, feel free to check out my other installation based tutorials located on my <a href="https://github.com/mGalarnyk/Installations_Mac_Ubuntu_Windows">Github</a> or my <a href="https://medium.com/@GalarnykMichael">Medium blog</a>.</p>

https://www.datacamp.com/community/tutorials/setup-data-science-environment

Learn about the various options you have to setup a data science environment with Python, R, Git, and Unix Shell on your local computer.

How to Setup a Data Science Environment

Setup a Data Science Environment on your Computer

<p>Data Science has its own language. So, if you want to have at least a slight chance of surviving in the enterprise world of tomorrow -with its obsessive focus on collecting and analyzing data- you better have started yesterday with learning this terminology.</p>

<p>
Luckily, and inspired by the <a href="http://techcrunch.com/2014/12/15/how-to-speak-startup/" target="_blank">&ldquo;How To Speak Startup&rdquo;</a> article on TechCrunch, the online data science school&nbsp;<a href="https://www.datacamp.com/?utm_source=How%20to%20speak%20data%20science&amp;utm_medium=blog&amp;utm_campaign=How%20to%20speak%20data%20science" target="_blank" title="Online R and Data Science Training">DataCamp</a> is here to help you with a great list of often-heard&nbsp;data science terminology and quotes:</p>

<p style="padding-left: 30px;"><em>(Note: This is an attempt to be funny. Whether this is a successful attempt is an opinion and open for debate. That it shouldn&rsquo;t be taken serious nor offensive is a fact.)</em></p>
<br>
<ul>
<li><b>Data scientist - </b>Average programmer looking for a job that pays as much as what a top programmer would get. Sometimes also goes by the name &quot;data analyst&quot;. <a href="http://ctt.ec/fd39T">Click To Tweet.</a></li>

<li><b>Statistician<b> - </b></b>Mathematician who can&rsquo;t program. <a href="http://ctt.ec/5GeK0">Click To Tweet</a></li>

<li><b>&quot;Our company is big data ready&quot;<b> - </b></b>My software vendor has done a great up-sell. <a href="http://ctt.ec/e60r2">Click To Tweet</a></li>

<li><b>&quot;We measure everything&quot;<b> - </b></b>We have absolutely no clue what to measure. <a href="http://ctt.ec/agQBd">Click To Tweet</a></li>

<li><b>Data savvy manager<b> - </b></b>Title used by managers active in marketing, sales or HR whom put pie charts in their powerpoint presentation.<a href="http://ctt.ec/yb5CO"> Click To Tweet</a></li>

<li><b>&quot;Correlation does not imply causation&quot;<b> - </b></b>We looked at the wrong data set and can&rsquo;t draw any conclusions from it. Often represented in a graph to create the illusion of adding value. <a href="http://ctt.ec/bYG2e">Click To Tweet</a></li>

<li><b>Machine Learning<b> - </b></b>Statistical technique used by the sales and marketing department of big data vendors to secure their yearly bonus. (also see &quot;Our company is big data ready&quot;) <a href="http://ctt.ec/V03xb">Click To Tweet</a></li>

<li><b>Chief Data Scientist</b> (CDS)<b> - </b>Former CTO (also see data scientist) <a href="http://ctt.ec/aIF5E">Click To Tweet</a></li>

<li><b>Hadoop<b> - </b></b>Open-source software used for distributed computing. Data Scientists seem to have a quota to drop the name every two sentences when talking big data, but most only know the logo is a yellow elephant.&nbsp;<a href="http://ctt.ec/SK6Z6">Click To Tweet</a></li>

<li><b>&ldquo;Being a data scientist is the sexiest job in the 21st century&quot;<b> - </b></b>Although a quote often used in the data science industry, statistical underpinned proof remains missing. <a href="http://ctt.ec/R5tvh">Click To Tweet</a></li>

<li><b>Data science bootcamp<b> - </b></b>Headhunting firm marketing itself as a school. <a href="http://ctt.ec/2p735">Click To Tweet</a></li>

<li><b>&ldquo;We booked these results with a small sample&quot;<b> - </b></b>Our financial budget wasn&rsquo;t large enough to perform a statistical significant data analysis.
<a href="http://ctt.ec/8Ura6">Click To Tweet</a></li>

<li><b>&ldquo;We implemented a data-driven decision making process&rdquo;<b> - </b></b>In the past we didn&rsquo;t have a clue what we were doing. <a href="http://ctt.ec/1cPGk">Click To Tweet</a></li>

<li><b>&quot;We use cutting edge predictive modeling techniques to forecast our results&rdquo;<b> - </b></b>We run a linear regression model and then ignore the result. <a href="http://ctt.ec/BacF3">Click To Tweet</a></li>

<li><b>&quot;There is a significant effect but ...&quot;<b> - </b></b>Sentence-start used by data scientists or statisticians when they&rsquo;ve put weeks of work into their analysis, the results look fishy and not as expected, and there is no time to redo the analysis. <a href="http://ctt.ec/UXfJd">Click To Tweet</a></li>
</ul>
<br>
<p>Hope this was helpful! Good Luck.</p>


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