Private Keyword-Based Push And Pull With Applications To: Fill & Download for Free

Here are 100 product ideas for you. My company helps Amazon sellers, but they’re just as applicable to e-commerce in general.And even if you don’t choose any of the 100, this will make you think…Or simply do this process:(1) Excel.Open a spreadsheet.(2) Now, stand up.Take a walk around and find 5 objects around you that fit private label standards. What’s got PL potential?- no bigger than a shoebox- lightweight- profitable to sell (cost leaves you margins)- brandable, customizable- category not dominated by a household name- the design isn’t patented- oh yeah, and it makes money on Amazon with a good RPR. More on thathere.I’ll see you in a sec.(3) Input products.Enter the 5 items in cells B1, C1, D1, E1, and F1 respectively. Here are 5 PL-worthy things I found around the office:cell B1: Microphone (which I use for our YouTube videos)cell C1: Straw (who has time to tilt their head these days?)cell D1: Whiteboard (if you don’t have one, get one. Great for brainstorming)cell E1: Push Up Bars (if you work for too long, you get groggy. Push-ups cleanse the mind)cell F1: Mouse (I’ve got a gaming mouse)(4) Input modifiers.No matter what your products are in the 1st row, input these exact modifiers in Column A.cell A2: minicell A3: largecell A4: personalcell A5: industrialcell A6: singlecell A7: bulkcell A8: for mencell A9: for womencell A10: for kidsHere they are, so you can just copy/paste into your document.minilargepersonalindustrialsinglebulkfor menfor womenfor kids(5) Fill in the matrix and search the terms on Amazon.Fill in the cells of the matrix exactly as they appear. Some of the combinations won’t make any sense, but don’t worry. A laugh is your reward.Actually, can you do me a favor?Leave a comment with your funniest keyword combination.Some gems from my batch include “whiteboard for men” and “single mouse”.Next, copy and paste each cell into Amazon, hit search, and see what you get. The mindset is key: reserve all judgement. At this stage, there are no good or bad ideas.While you’re conducting the search, run JungleScout or some other market analysis software on the results. Look at the amount of sales versus reviews. You want high sales numbers and low review counts (i.e. there’s demand but it’s not too competitive).Let the search results, images, and Amazon auto-complete suggestions stimulate your mind. It’s hard to be creative without external inputs. Write down any cool opportunities you find. Here are the results from this round, written stream-of-consciousness style…MicrophoneMini + Microphone = after searching this, I realized a “mini mic” is more commonly referred to as a “lapel mic” — a great niche! So, I wrote that down.Large + Microphone = introduced me to the microphone parts called “shock mount” and “pop filter”. I don’t know what these are, but suddenly it dawns on me: there’s a whole niche of audiophiles shopping online. Microphones and sound quality for creatives, musicians, and podcasters is a great little niche to start with. Small, high perceived value, easily identifiable audience.Boom.You’re welcome.Get it? Boom? Boom mic? Fine, be that way.Personal + Microphone = lapel mics with voice amplifiers. Pretty interesting.Industrial + Microphone = this search led me to electric earmuffs. Which, while it has a 7k review player in it, may not be too bad.Single + Microphone = I was introduced to “patch cords” through this search, a product pulling $5k monthly and 42 reviews (Revenue per Review = $119.04). And again, RPR is a niche evaluation statistic that HonestFew invented. Check out “How to Pick Winners in JungleScout” to learn more. It’s revenue divided by the number of reviews. A high RPR is good.Bulk + Microphone = the search “bulk microphone” gives us HUGE spools of microphone cables. This would be a pain to import, but the RPR’s are splendid. So if you’ve already got a sea shipping operation and you’re in tech, it’s worth considering.The rest of the modifiers in the microphone category weren’t as eventful. It occurs to me is that you could sell accessories for karaoke. That’s a big, but targetable customer base.Industrial + MicrophoneMens + MicrophoneWomens + MicrophoneKids + MicrophoneLet’s move on to straws.StrawMini + StrawLarge + Straw = I love this one. What an opportunity: selling large straws made for bubble tea. It’s a bulk play. It’s beautiful. Top listing makes $29k/mo with 241 reviews at the time of writing, which makes the RPR $120. This search also introduced me to reusable stainless steel straws which, while I’ve seen them done as PL before, aren’t totally played out.wasPersonal + StrawIndustrial + StrawSingle + StrawBulk + StrawMens + Straw = produced mens straw hats, which (I now know) isn’t a profitable niche.Womens + Straw = see above.Kids + Straw = a little noise being made by silicone drinking straws doing $9.3k with 142 reviews. Not bad. Normally, fighting for space in silicone products is like Rose and Jack at the end of Titanic: someone falls off and dies. But silicone straws for kids are alright.WhiteboardMini + Whiteboard = boring.Large + Whiteboard = unveiled a new product category: whiteboard sheets. The top seller pulls in $17k monthly with 8 reviews (?!). We’re done here.Single + WhiteboardBulk + WhiteboardIndustrial + WhiteboardMens + Whiteboard = there’s a huge double-sided whiteboard there. It’s huge. $20k/month with 50 reviews. RPR is great, but is it a good PL play? No. Shipping is prohibitive.Womens + WhiteboardKids + Whiteboard = there’s a kids easel play here. But only if it’s a physically small easel. Be careful.Push Up BarsMini + Push Up BarsLarge + Push Up BarsPersonal + Push Up BarsIndustrial + Push Up BarsSingle + Push Up BarsBulk + Push Up BarsMens + Push Up BarsWomens + Push Up BarsKids + Push Up BarsThis one sucked because push up bars lack diversity. Sometimes you’ll strike out. Just keep going.MouseMini + Mouse = aside from pics of Mickey’s significant other, it seems mini computer mice are popular (and cut shipping costs). Win-win!Large + Mouse = this exposed me to keywords like “gaming mouse” and something called a “penguin mouse”, which an ergonomic mouse. Pretty cool! Tip: don’t stop with the top results. Scroll down the page. It’s rewarding.Personal + MouseIndustrial + MouseSingle + MouseBulk + MouseMens + MouseWomens + MouseKids + Mouse…the rest were dull.So there you have it. After using the HonestFew matrix, we have 8 actually good ideas. No brain racking, no frustration.Here are the cells that produced results.Mini MicrophoneLarge MicrophoneLarge StrawStraw for KidsLarge WhiteboardMini MouseLarge MouseAnd here are the extrapolated product ideas.1. Lapel mic2. Audiophile accessories e.g. shock mount, pop filter3. Bubble tea straws4. Silicone kids straws5. Whiteboard sheet6. Kids easels7. Mini computer mouse8. Penguin mouseDone.Once again, the steps are:(1) Excel. Crack open a new spreadsheet.(2) Now, stand up. Take a walk and find 5 PL-worthy products.(3) Input products. List them in Row 1.(4) Input modifiers. List the modifiers we give you in Column A.(5) Fill in the matrix and search the terms on Amazon. Use product research software to expedite. Be open to all ideas.Once you choose an idea, it’s time to source. Check out HonestFew’s full super-awesome sourcing guide complete with negotiation templates and examples.

You could use Fed: The Linux Console Editor to run scripts by writing your scripts as plugins for Fed. There is no requirement to install Fed system-wide, and you can build a local copy of Fed in a folder with its own resource file and plugins folder. You could repeat that process to create specialized “Fed” instances on your drive.Grab it from SourceForge: feditScriptsIf you find yourself typing a complex set of instructions over and over, why not create a script that takes parameters either from the command line or an input file? Scripts come in several kinds, and are usually tied to the type of shell you are using. Bourne Shell Scripts run in the bash shell, but there are also csh that are C-like, among many others.Here is one:#!/bin/bash echo hello world It’s a good idea for you to read about the Unix philosophy - Wikipedia (but go to the original sources, for some deep dive)AutocompleteLinux, since 2004, has had command-line auto-complete. I consulted on Linux CLI in a Linux user’s group of developers, and pitched additional features for auto-complete in the early days, after I was inspired by auto-complete on the web.In Bash and sometimes other shells:Use the UP arrow to go back through your recent history of commands.Use the TAB key to autocomplete a partially completed word or filenameUse the Double-TAB (“hit tab twice like a double tap”) at the end of a path parameter to see what’s “in the folder’, examplels /home(Double-TAB)These shortcut key commands also work in CLIs, like MySQL’s mysql CLI tool.ESCIn some terminal programs, ESC is the Meta key, thus ESC-ESC is Meta-Meta ... Meta is sometimes the Alt key in a native OS terminal, and sometimes it's mapped to the "Windows" or ⌘ or ⌥ other OS-specific keyboard keys. This was mentioned in the irssi tip belowCtrl-CSIGINT or "interrupt" which attempts to close a currently running foreground process; I say attempt because some applications block thisCtrl-ZSIGZUP or "suspend" which attempts to pause and place in the background a foreground process; i say attempt because some applications block thisMany Linux administrators and users have written “cheat sheets”, including me.Here is the content from mine:8< —————————————————————————-manthis comMANd lets you read the MANual, MAN, and it lets you rtfm (that's a command for you not a computer)$ man somecommand Typically you can also use --help on most commands like cp --helptopthis command lets you see what processes are taking the highest amount of resources. q to quitgrepGrep is a useful command to search file contents. Use grep --help for more information on how grep works. You can also check out egrep and egrepi for some real confusion.psalone, this command lets you see what the running PIDs (process ID), when coupled with the kill command, you can identify and stop process that are running "amok"$ ps aux | grep myusername The above command(s) would show you just what you're up to. Replace myusername with any keyword to search by.lsMy personal favorite is ls -la which lists the files and their other attributes, size and date of modification.duDisc Usage (du) lets you see what kind of disk space usage stems from the current directory. Some people have recommended du -sh for its choice of measurement units; you may alsodu -sh /some/path dfDisk freefindA useful command that doesn't always work as expected, the find command lets you find something and supports regular expressions (regex) among other key features.One use: find / -name 'usr'; similar commands with other types of results are locate where, but they aren't always useful or availablenano$ nano somefile.txt nano is a text editor similar to an older editor, pico, and it lets you edit files. the commands in it that I use are: Ctrl-Y, Ctrl-V, Ctrl-O, Ctrl-X, Ctrl-C, Ctrl-W (+Ctrl-R) and Ctrl-_ (underscore)replace$ replace "this text" "with this text" -- *.txt *.c thisfile.bat This command comes with MySQL and lets you mess up your text files.cat$ cat sometext.txt | less A great way to read a text file. The less command is similar to the more command, but lets you use up and down arrow keys, q, and /some phrase to search.These are famous try them with --help -h or -?mkdirmake a folder mkdir /some/path or mkdir folderherecdchange directory cd /some/pathcpcopy file a overwriting b cp a bmvmove (or rename) a overwriting b (or into folder b) mv a brmremove (delete) rm -r /dir deletes a folder! add -f for dangerous non-interactive mode (doesn't ask you to confirm)tarI once thought TAR was "Terrible Archive" but it just means a file that is the glob of a bunch of other files, indexed and stored in a lump on your disk. Tastes funny when you boil it. You can "create a tar" and "extract from a tar" or "extract a tar", like this:$ echo this > somefile.txt $ tar -cvf sometar.tar somefile.txt [output redacted] $ tar -xvf sometar.tar gzipTar too big? Gzip it for some real compression! On most unix systems these days you can zip and unzip too with that other zip file format$ gzip sometar.tar The above command consumes sometar.tar and outputs sometar.tar.gz -- Can't do much other than move a gz file around, and gunzip it:$ gzip -d sometar.tar.gz Be careful with these commands, they overwrite those files without asking all the time.makeA command that "makes" source code that comes with "Makefiles"lynx http://some.url.com/some/pathLynx is a terminal-based textual browser for browsing websites. It's a great way to get banned from MudConnectortfThe linux package tf5 loads TinyFugue by Ken Keys into your system. Invoked with simply tf, TinyFugue is a remarkable MUSH client, that is also useful for MUDs and is built on powerful common libraries, has a scrolling mixed-window terminal viewport and is easy to program to trigger on certain events. One important thing to note about DikuMUD is that the Password: prompt does not always appear in tinyfugue's default connection settings. Included in the example below is how to add a DikuMUD. A few examples of how to use tinyfugue:$ tf http://mytfconfig.tf /connect orcs.biz 1111 /adddiku orcmud orcs.biz 1111 /connect orcmud /fg /fg orcs /fg /help adddiku /help tfrc /help def /help set /load myconfigfile.txt In the above example we first connect directly to the MUD orcs.biz 1111 without naming it. TinyFugue automatically names this "unnamedX", but this is not the best way. The preferred way is to use /addworld or /adddiku, which allows you to name it and example is shown above. The command fg lets you put a session into the ForeGround, allowing you to select from all of your worlds by name, or switching back to the first one by default. You can put multiple world declarations inside a config file, and load it on the command line or within tinyfugue. There is even a way, through use of a .tfrc file, to set up default configuration options unique to your user account. Check out /def and /set if you want to deploy triggers, create macros, and other nifty stuff.irssiRun just like that to enter a nice terminal-based IRC client. Once inside, you could type(irssi) /connect http://chat.freenode.netJust a tip, irssi shouldn't be run as root or main, and more importantly to switch "windows" in irssi you use ESC in most terminals, which maps to the Meta key (see below), this is done for instance ESC-1 and ESC-2 ... ESC-9 .. windows are automatically opened when you receive private messages, join a channel (/join ##php) or when you connect to a server using /connect ... and are limited to 20 I believe. To access window 11, you would do ESC-ESC-1 I think. Check the man irssi or the appropriate website for this tool. Other commands in irssi match common IRC clients like mIRC, with a few differences. All-in-all it's what you wanted from an IRC client for linux terminals.wget http://protocol://some.ip.com:80/some/pathwget will remotely retrieve and save a file (overwriting quite brutally anything locally) - it made Mark Zuckerberg famous and a lot of other people infamous or just geeks.gdb binary pidYou can use the form gdb somefile 1234 to grab on to a running PID, and it will pause such process and let you step through it instruction by instruction. gdb has a lot of commands available in the internal help that allow you to view values of variables stored within memory. This works for code built nearby that has lots of source code available, as gdb will tell you exactly what line you are on. It also catches processes as they fail, and shows you where they failed. If you 'continue' when attached a PID, and the PID crashes, you'll see the reason why and be able to examine the conditions of the crash.Additionally, you can initiate a process with gdb, and pass it parameters like this:$ gdb somebinary  [stuff removed]  (gdb) run 1234 The above example would execute "somebinary 1234" just like you did it from the command line, but if it crashed you'd get to see the stack trace and investigate why.Other useful gdb commands from within gdb: print, up, down, stepnohupA command which can execute another command and place it into a permanent background state if plausible based on what the command is. Use in conjunction with the & operand, like this:$ nohup ./somescript & This would attempt to perpetuate a given script if the script has an endless loop. This can be used in conjunction with a "cronjob"crontab -eThis command lets you edit your crontab entries. Go search for how to use this information.chownCHange OWNer, lets you modify the "user:group" permissions on a file. -R means recursive and can be used on a folder$ chown someone:someone -R /file/path In the above example, you change ownership exclusively to someone. Had "someone:someone" been "someoneelse:someone" it would allow someoneelse to access something that someone controls. Useful for creating shared areas, along with the upcoming recommended commandchmod$ chmod 777 -R /file/path  $ chmod 655 filename  $ chmod a+rwx filename A common disk access control command, which lets you lock and unlock files that you own, chmod has some special numerics that can be also written as letters (such as u+rwx which means "user read write execute"). Some strange internal mapping of meanings here indicate annoying broken features. For instance, execute=delete and read != enter for folders (you must set "delete" for that). Here are the useful ones (but there are others):chmod 555 - read for everyonechmod 666 - read/write for everyonechmod 755 - i can execute/delete, they can read - use this to turn text files into scriptschmod 777 - we can all execute/delete, read, writeBasically you are saying User-World-Other when you use chmod and a number, and it's proper form to set the sticky bit to 0, like this: chmod 0655 target which is the same as chmod 655 target but is explicit and doesn't "ignore" the sticky bit. You can set this bit to 1 by doing chmod 1655 but this has odd effects. The "sticky" bit, which I think was just a reference to the earlier design mistake in this command and its ramifications, has some significance and its purpose is worth researching further online.The letters correspond to: (a)ll, (u)ser, (g)roup, (o)ther, (w)orld - like u+rw or g-r+wx or w+x etc.Remember x=eXecute(=delete/enter)gitAh, git. A lot of people git, but others say it's just like svn (a.k.a. subversion). Just saying subversion sounds cool. Anyway, git is for gitting source code and you push and pull with git. You also commit with git, because git demands commitment. You can add with git, too. Strangely, it's possible to "fork" with git, which isn't even a verb! Well, I guess that about covers it. Gotta git back to work! See ya!

Updated Aug 2018The following sections will outline five skills that will help you further a career as a Data Analyst:Data Exploration via Excel/Google SheetsData Extraction with SQLData Visualization via TableauData Automation via PythonData Analysis/Science with Python + Stat librariesWho this is for - College students, new graduates, career changers, and new analysts will probably benefit most from this article. It assumes you have minimal analytics, programming, or work experience. This article should help you build a foundation so you can begin or further a career in data analytics.Who I am - I’m a self-taught analyst who has worked at various companies (Netflix, CNET, Zynga) in a variety of analytical roles (Marketing, Finance, Social, Growth) for over a decade.Two notes before proceeding:This article will not outline how to become a data scientist or data engineer (read more about the differences), which generally require degree(s) in statistics or computer science respectfully.While you can learn these in any order, you’ll probably progress most seamlessly by starting with #1 and #2 before #3–51. Data Exploration via Excel / Google SheetsAt most organizations, Microsoft Excel and/or Google Sheets are the most broadly used data applications. While many tools perform a specific function very well (such as Tableau for visualization), few can enable most lightweight data tasks as easily as a spreadsheet. Not only are Gsheets/Excel the Swiss Army knives of data exploration, they also have a relatively shallow learning curve, which make either a great tool to learn first. If you’re dead-set on other analysts skills, don’t spend too much time here--but don’t make the mistake of not becoming familiar with a spreadsheet program either. Many data questions can be answered and communicated with a spreadsheet faster than with other technologies.Start by learning the following:FormulasGeneral Formulas. Once you’ve downloaded the data, see if you can enhance it with some formulas. The IF statement, boolean logic (AND, OR), and VLOOKUP functions are the most common formulas used across spreadsheets. Afterward, graduate to learning text-based formulas like MID, LEFT/RIGHT, SUBSTITUTE, TRIM. Experiment with the date formulas--such as converting a date (in any format) to the components of a date (year, month, day).Formula References. You should know the difference between an absolute and a relative reference as well as how to input either via editing a formula using the keyboard (F2) as well as toggling either (F4) via the keyboard.Aggregation Formulas. These formulas help you find conditional summary level statistics: SUMIF(s) , COUNTIF(s), and SUMPRODUCT, which are good to learn for reporting purposesInterested in learning more formulas? See this article.Data Filter. The data filter is a key feature which helps end users, sort, filter, and understand a sample from a large data set. Memorize the keyboard shortcut for creating one--you’ll use this often.Pivot Tables. Pivot tables allow an end users to easily get summary level statistics for a given dataset. Learn how to create a pivot table, and scenarios in which to place fields or metrics in the row, column, filter, or value section. Learn how to create formulas at the pivot table level, and understand how creating them on a pivot table is different than at the data table level. Finally, learn the GETPIVOTDATA function, which is especially useful when creating dashboardsCharting and Pivot Charting. Lean how to create bar, line, scatter, and other charts in Excel. Formatting charts is relatively easy--when you want to change something click on it (or right click), and in general the Excel Ribbon or the right click menu will allow you to modify the look and feel of a chart within the ribbon or or menu.Keyboard Shortcuts. As you begin to get more comfortable, begin mastering the keyboard shortcuts rather than use the mouse. Start by learning the basic shortcuts for tactics like find and replace and paste special. Then move onto to navigating using the keypad. Experiment with selecting rows and columns by using a combination of shift and control. You should eventually learn how to add rows/columns, hide rows/columns, delete rows/columns--all by using the keyboard.Excel Dashboard Design. Learn the Data → Pivot → Presentation pattern, in which one separates the source data from summarized data, and summarized data from the viewable dashboard. This pattern will allow you to easily update a report as more data comes in as well as hide complexity from those who just want to see the most important learnings. How? The first tab contains your data, which you should ideally not change. The second tab contains one or many pivot tables that calculate summary statistics needed for the report. The third tab is a dashboard with one or many visuals or data tables that source data primarily from the second tab (and not from first tab). You’ll present just the third tab to end users, but hide the first and second tabs. When displaying summary level statistics, you’ll likely leverage GETPIVOTDATA—instead of using other summary formulas—will has a faster runtime than the summary formulas. This article explains how to create a dashboard using GETPIVOTDATA such that an end user can select various input options and see a visualization change---Some notes:Excel or Google Sheets? Google Sheets performs best with smaller datasets (<10k rows). It’s also free. Out of the box, Gsheets is also more collaborative, and a good solution if your dataset will be viewed or modified by multiple stakeholders. For larger datasets, spreadsheets with lots of formulas, or the use of esoteric features, Excel is usually the preferred optionDon’t learn Excel VBA. If you’re interested in programming, skip to the Data Programming section and consider Python instead.2. Data Extraction with SQLExcel allows you to slice and dice data, but it assumes you have the data readily available. As you become a more seasoned analyst, you'll find that a better way to get at data is to pull it directly from the source, which often means authoring SQL.The great news about SQL is that unlike a procedural based programming language like Python, SQL is a declarative language. In most cases, instead of writing step-by-step syntax to perform an operation, you describe what you want. As a result, you should be able to learn SQL faster than learning most programming languages.I’m not going to outline all of the flavors of data storage solutions (to start, learn about relational vs non-relational databases) but instead focus on what you’re most likely to encounter--a relational database which supports some flavor of SQL.Start by learning the big six reserved keywords:SELECTFROMWHEREGROUP BYHAVINGORDER BYNext, you’ll want to learn common sql functions, such as the CASE statement, boolean operators (AND, OR, NOT), and IFNULL/COALESCE. Next, learn string functions such as INSTR, SUBSTR, and REPLACE.As you begin to write summary level queries which use the GROUP BY keyword, experiment with the aggregate functions such as SUM, COUNT, MIN, and MAX. Following that, learn how to join to other tables. Know the difference between an inner and outer join.Next, take a break from writing SQL and invest in learning more about how relational databases are structured. Know the difference between a fact and dimension table, understand why database indexes (or partitions) are leveraged, and read about why traditional database adhere to 1st, 2nd, and 3rd normal forms. If someone says they have a high cardinality dataset, a snowflaked schema, or slowly changing dimension--you should know what they mean.As you work with larger datasets, you’ll discover that more involved SQL queries require issuing several SQL queries in sequence. For example, the first query may create a table; the second one will insert data into that table; and the third will extract such data. To get started here, read more about temporary tables. Then you’ll want to learn about column data types as well as how to create traditional database tables and indexes/partitions to support more performant querying.---Some notes:SQL Bolt has a great interactive tutorial to help you learn SQL by doingToptal’s top SQL interview questions can help you get your next job that requires knowing SQLThis section only covered data extraction. As you become more senior, you’ll need to know how to build intermediary tables for analysis, or even construct source tables to store non-temporal data. Read more about SQL DML and DDLIf you’re interested in learning more about dimensional modeling, purchase Kimball’s The Data Warehouse Toolkit, which was originally published in 1996 but still relevant for traditional relational databases today.Try creating your own database locally by downloading and installing mysql or postrgres. Or do so via google cloud.This section only covered relational databases. See this article to learn more about non-relational databases3. Data Visualization via TableauIn the past decade, Tableau has become the leading enterprise tool for visualization. If you’re familiar with pivot tables, you’ll find that creating lightweight visualizations and dashboards with Tableau is relatively easy. To spreadsheet users, Tableau feels like working with an enterprise version of Pivot Tables and Pivot Charts. While keeping your analyses private requires a purchased Tableau Desktop license, Tableau public--which stores any saved analyses to the publicly accessible Tableau portal--is free and a great way to get started learning.Let’s start with Tableau Public--begin by creating an account and downloading the software, and then import a dataset into Tableau. Next, learn more about the panels within the tool. You’ll see the data you’ve added broken up into Dimensions and Measures. Try dragging a given dimension into the columns shelf, and a given measure into the Rows shelf. Tableau will analyze the structure of your data, and automatically generate a visualization (without you selecting one). You can easily change the visualization displayed by changing the type, or by shifting the data between Rows and Columns.After you’ve created a couple of different visualizations across multiple worksheets, create a dashboard. A dashboard can contain one or many views (worksheets) and also allow an end user to manipulate such a view via buttons, filters, and other controls. Start by adding one view to your new Dashboard. Then, add a Filter for a given measure or dimension. Once added, you can change the nature of each filter. For example, you can create a slider to change the range of dates included, or add a radio form to allow an end user to select a given measure. Once you have a functional dashboard, feel free to save it to Tableau Public so you can both view it as an external user would as well as modify it later. For inspiration, see some existing dashboards.From here, there’s a lot more you can do and learn. Tableau’s learning curve quickly steepens as you produce more advanced visualizations and deal with more complex datasets. If you want to continue learning, your best bet is to watch Tableau’s series of free training videos.---Some notes:While Tableau is the current Enterprise visualization market leader, it may not be five years from now. Tableau started as a desktop application and then grew to support web-based reporting, and now many upstarts are producing Tableau-like tools that are 100% browser based (See alternatives to Tableau), responsive by default, and built to work in the cloud as well as integrate with other sources.4. Data Programming via PythonNow you can source data from a database with SQL, manipulate it with a spreadsheet, and publish visualizations via a Tableau dashboard. A next natural step is to learn a programming language. Python is the most utilized programming language in the data community as well as the most common language taught at universities. With it you can achieve a number of data-related tasks such as extracting data from a website, loading said data into a database, and emailing the results of a SQL select statement to a set of stakeholders. If you’re interested in building web application, you could use Python and Flask to create an API as well as create a website leveraging the Flask HTML templating engine Jinja2. Or, you can leverage Python Notebooks for iterative development, the PANDAS library to see the results of a model you’re building as you develop it.The best way to build a strong programming foundation is to start by learning computer science fundamentals. For example, I was introduced to many computer programming concepts via the book Structure and Interpretation of Computer Programs (SICP) at university. Although originally authored in 1979, the book’s concepts are still relevant today and are still leverage today used at UC Berkeley to teach introductory computer science. Once you learn many of the fundamentals, you should be able to apply them to learn any computer programming language. However, learning the fundamentals can take a lot of time--and the content in SICP is academically dense (this review describes it well). Sometimes the better tactic to get started is to learn by doing.I learned python syntax years ago via Learn Python the Hard Way. The online course costs $30 now--and there are plenty of other free alternatives--but when I took the course (at the time it was free), I found it to be one of the better tutorials for learning the Python syntax. If you’re looking for a free option, head to Learn Python or Code Academy.You will have covered python basics when you’re familiar with python variables, control-flow, data structures (lists, dictionaries), classes, inheritance, and encapsulation. A good way to solidify your knowledge is to think of a project you’d like to implement and begin developing—this site has a couple of datasets that you can use to get started.Now that you have the basics down, you’ll want to learn more about how to become a more productive programmer by improving your development environment. The next three sub-sections will cover how to save/share/iterate your work using Github, author Python scripts using Jupyter Notebooks, and make changes to projects using the command line.4a. Learn version control using GitHub/git.GitHub allows you to host, update, document, and share your projects easily online. You’ll soon discover that GitHub will likely be where you end up when you’re discovering new programming libraries. Start by creating a GitHub account (almost all developers have one). Then spend time iterating through the GitHub tutorials, which will outline all of the capabilities of git. Once complete, you should be familiar with how to git clone an existing repository, how to create a new repository, git add files to a commit, prepare a set of changes with git commit, and push changes to a branch via git push. As you invest time in any project, make a habit of committing it to github to ensure that you won’t lose your work. You’ll know that you’re progressing with git once you feel comfortable using the above commands for both managing your own projects as well as cloning other projects to augment your development efforts.4b. Author Python scripts using Jupyter Notebooks As you’re learning Python, you’ll discover that there are multiple ways to author python code. Some developers will use IDEs built specifically for programming such as PyCharm, others elect rich text editors with a focus specifically on coding such as Sublime, and a small minority will edit code exclusively through a shell using VIM. Increasingly, data professionals are gravitating toward using notebooks--specifically Jupyter Notebooks--to author scripts in a web browser for exploration purposes. A key feature within notebooks is the ability to execute code blocks within each notebook rather than all at once, allowing the developer to gradually tweak a data analaysis. Moreover, since the output is in the web browser versus a shell, notebooks can display rich outputs, such as an annotated datatable or timeseries graph beneath the code that generated it. This is incredibly helpful when you’re writing a script to perform a data task and want to see the progress of our script as it executes without leaving the browser.There are a variety of ways to get started with Notebooks. One way is to download Jupyter and run an instance on your local machine. Another option is to use Google’s free version of notebooks or Microsoft Azure Notebooks. I prefer to use notebooks hosted on pythonanywhere, which is the same service I use to host python-based web applications. The free service will let you create your own python apps but you can’t run notebooks--the most affordable tier is $5/month.A good way to learn some of the key value adds of developing with Notebooks is to explore a dataset using the Python Data Analyst library, PANDAS. This site has a great getting started tutorial. Start by importing a dataset and print it out. Learn more about the data-frame storage structure, and then apply functions to it just like you would with another dataset. Filter, sort, group by, and run regressions. Try leveraging seaborn, a statistical visualization library which leverages matplotlib to explore your datasets visually. You’ll quickly discover that the framework allows for repeatable data operations with option for data exploration against a moderate cardinality dataset. Notebooks are often the preferred prototyping interface for data scientists, and thus worth learning how to use if you’re interested in learning more about statistics.4c. The Command Line - using shells and editing with vimIf you’ve read this far, you’ve probably already used a shell, a command-line based user interface for interacting with a computer. You’ve likely used shells to execute python code, download code libraries, and commit changes to git. Knowing how to execute a file, navigate within a shell, and monitor an active process will help you become a stronger data analyst. A great place to learn more about shells is following this interactive tutorial. You know that you’re becoming more proficient with shells when you can easily navigate within a directory, create aliases, change file permissions, search for files and/or contents using grep, and view the head/tail of a file.VIM is a unix-originated command-line text editor which is run in a shell. It’s especially useful when you want to view or edit a file—such as a log or a data output—on a remote server. Initially, you’ll likely find that learning VIM is a bit cumbersome because you primarily interact with the application without a mouse. However, over time you’ll begin to develop the muscle memory needed to toggle between edit-mode, view-mode, and executing commands. A great place to get started with VIM is to go through this interactive tutorial. You’ll know that you’re becoming more comfortable with VIM once you can easily navigate between input and edit mode, go to a row by a number, add or delete a row or character, search and replace text, and easily exit and save files you’ve edited.5. Data Analysis/Science with Python + Stat librariesWhile the goal of this article is not to describe how to be a data scientist--that typically requires a undergraduate and/or graduate level education in statistics--having a solid foundation in statistics will help any analyst make statistically sound inferences from most data sets.One way to get started is to take an online course in descriptive statistics--such as this free one from Udacity--which will teach you how to communicate summarized observations from a sample dataset. While you may be tempted to jump to other hotter industry topics such as machine learning, start with the basics. A solid foundation in descriptive statistics is a prerequisite for machine learning as well as many other statistics applications. After going through Udacity or other tutorials, you should be able to describe various types of distributions, identify skews, and how to describe central tendency, variance, and standard deviation.Next up, graduate to learning inferential statistics (such as Udacity’s free course), which will enable you to draw conclusions by making inferences from a sample (or samples) of a population. Regardless with the learning path you take, you should learn how to develop hypothesis as well as become familiar with tactics for validating such hypothesis using t-tests, understand when to leverage different types of experiments, as well as compute a basic linear regression with one more more dependent variables.The two most popular languages for applying statistics are R and Python. If you’re just getting started, I’d recommend using Python over R. Python is generally considered an easier language to learn. Moreover, Python is typically understood by most teams who build data products. There are more libraries available in Python that can be applied to a wider set of data applications--such as deploying a website or creating an api. This means you can often start an exploratory analysis in Python and easily append a few more libraries to deploy a tool / product leveraging such data, which can reduce the time to release. Finally, data applications continue to gravitate to Python over R as the preferred applied statistics language, so by learning the statistical libraries on Python you’ll be riding this latest adoption trend.Regardless of which language you choose, both Python and R can be executed via Jupyter Notebooks, which allow for more easy visualization and communication as you’re getting started.Next, try learning more about machine learning (Udacity’s free ML course is here). Following any course you should be more familiar with how to differentiate a supervised vs unsupervised learning, understand bayes theorem and how it’s used in ML applications, and outline when decision trees are leveraged. Once you’ve learned the concepts, try cementing your understanding by implementing one of these 8 machine learning projects.Finally, Python has a wealth of free libraries commonly leveraged by data scientists. One way to become more familiar with data scientist tactics are to try experimenting with data science libraries. For example, scikit-learn provides standard algorithms for machine learning applications, and NLTK is a library which can help you process and analysis text using NLP.Wrap UpNow you can write a python script to extract data (#4), store it in a database with SQL (#2), build a model to predict future observations with a python data science library (#5), and share what you learn via a spreadsheet (#1) or a Tableau Dashboard (#5). During that process, you may have committed your code to git, authored in a Jupyter Notebook, and published it on your python-hosted server. Congratulations! You’re well on your way to becoming a data analyst.