Data Carpentry

Data Carpentry's aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain.

Data Carpentry https://tess.elixir-europe.org/content_providers/data-carpentry Data Carpentry's aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. /system/content_providers/images/000/000/010/original/DC_logo_vision.png?1469458743
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OpenRefine will automatically track any steps you take in working with your data. Faceting and clustering approaches can identify errors or outliers in data. OpenRefine provides a way to sort and filter data without affecting the raw data. OpenRefine also provides ways to get overviews of...

Open Refine for Ecology: Glossary https://tess.elixir-europe.org/materials/open-refine-for-ecology-glossary OpenRefine will automatically track any steps you take in working with your data. Faceting and clustering approaches can identify errors or outliers in data. OpenRefine provides a way to sort and filter data without affecting the raw data. OpenRefine also provides ways to get overviews of numerical data. All changes are being tracked in OpenRefine, and this information can be used for scripts for future analyses or reproducing an analysis.

Python is a general purpose programming language that is useful for writing scripts to work effectively and reproducibly with data. This is an introduction to Python designed for participants with no programming experience. These lessons can be taught in a day (~ 6 hours). They start with some...

Python for Ecologists https://tess.elixir-europe.org/materials/python-for-ecologists Python is a general purpose programming language that is useful for writing scripts to work effectively and reproducibly with data. This is an introduction to Python designed for participants with no programming experience. These lessons can be taught in a day (~ 6 hours). They start with some basic information about Python syntax, the Jupyter notebook interface, and move through how to import CSV files, using the pandas package to work with data frames, how to calculate summary information from a data frame, and a brief introduction to plotting. The last lesson demonstrates how to work with databases directly from Python. Data Carpentry’s teaching is hands-on, so participants are encouraged to use their own computers to ensure the proper setup of tools for an efficient workflow. These lessons assume no prior knowledge of the skills or tools. To get started, follow the directions in the “Setup” tab to download data to your computer and follow any installation instructions. This lesson requires a working copy of Python. To most effectively use these materials, please make sure to install everything before working through this lesson. 2017-10-09

This lesson assumes no prior experience with the tools covered in the workshop. However, learners are expected to have some familiarity with biological concepts, including nucleotide abbreviations and the concept of genomic variation within a population. Participants should bring their laptops...

Genomics WorkshopWorkshop OverviewTeaching Platform https://tess.elixir-europe.org/materials/genomics-workshopworkshop-overviewteaching-platform This lesson assumes no prior experience with the tools covered in the workshop. However, learners are expected to have some familiarity with biological concepts, including nucleotide abbreviations and the concept of genomic variation within a population. Participants should bring their laptops and plan to participate actively. To get started, follow the directions in the Setup tab to get access to the required software and data for this workshop. Please note that workshop materials for working with Genomics data in R are under development and will become available in June 2018. This workshop uses data from a long term evolution experiment published in 2012: Genomic analysis of a key innovation in an experimental Escherichia coli population by Blount ZD, Barrick JE, Davidson CJ, and Lenski RE. (doi: 10.1038/nature11514) More information about these data will be presented in the first lesson of the workshop.

PIP is referred to in the text but it shouldn’t need to be used. It is assumed that Jupyter notebooks will be used for all of the coding. (The shell is used in explaining REPL) How to start Jupyter is included in the setup instructions. All of the datasets used have been placed in the data...

Python for Social Science Data: Instructor Notes https://tess.elixir-europe.org/materials/python-for-social-science-data-instructor-notes PIP is referred to in the text but it shouldn’t need to be used. It is assumed that Jupyter notebooks will be used for all of the coding. (The shell is used in explaining REPL) How to start Jupyter is included in the setup instructions. All of the datasets used have been placed in the data folder. They should be downloaded to the local machine before use.

SciPy ecosystem for Python provides the tools necessary for scientific computing Jupyter Notebook and the Spyder IDE are great tools to code in and interact with Python with its large community it is easy to find help in the internet FIXME FIXME FIXME

Python for Ecologists: Glossary https://tess.elixir-europe.org/materials/python-for-ecologists-glossary SciPy ecosystem for Python provides the tools necessary for scientific computing Jupyter Notebook and the Spyder IDE are great tools to code in and interact with Python with its large community it is easy to find help in the internet FIXME FIXME FIXME

A single raster file can contain multiple bands or layers. Spatial objects in sf are similar to standard data frames except for a geometry list-column. It is important to know the projection (if any) of your point data prior to converting to a spatial object. CRAN Spatial Task View Geocomputation...

Intro to Geospatial Data with R https://tess.elixir-europe.org/materials/intro-to-geospatial-data-with-r A single raster file can contain multiple bands or layers. Spatial objects in sf are similar to standard data frames except for a geometry list-column. It is important to know the projection (if any) of your point data prior to converting to a spatial object. CRAN Spatial Task View Geocomputation with R

Data Carpentry's aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. The lessons below were designed for those interested in working with ecological data in Python. Data for this lesson is from the...

Data Carpentry Python for Ecologists https://tess.elixir-europe.org/materials/data-carpentry-python-for-ecologists Data Carpentry's aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. The lessons below were designed for those interested in working with ecological data in Python. Data for this lesson is from the Portal Project Teaching Database - available on FigShare. The data files used in this lesson are surveys.csv download link - https://ndownloader.figshare.com/files/2292172 and species.csv download link - https://ndownloader.figshare.com/files/3299483. Requirements: Data Carpentry's teaching is hands-on, so participants are encouraged to bring in and use their own laptops to insure the proper setup of tools for an efficient workflow once you leave the workshop. (We will provide instructions on setting up the required software several days in advance, and the classroom will have computers with the software installed). There are no pre-requisites, and we will assume no prior knowledge about the tools. Participants are required to abide by Software Carpentry's Code of Conduct. Twitter: #datacarpentry 2016-03-07

A relational database is made up of tables which are related to each other by shared keys. Different database management systems (DBMS) use slightly different vocabulary, but they are all based on the same ideas. It is useful to apply conventions when writing SQL queries to aid readability. Use...

SQL for Ecology: Glossary https://tess.elixir-europe.org/materials/sql-for-ecology-glossary A relational database is made up of tables which are related to each other by shared keys. Different database management systems (DBMS) use slightly different vocabulary, but they are all based on the same ideas. It is useful to apply conventions when writing SQL queries to aid readability. Use logical connectors such as AND or OR to create more complex queries. Calculations using mathematical symbols can also be performed on SQL queries.

Never modify your raw data. Always make a copy before making any changes. Keep track of all of the steps you take to clean your data. Organize your data according to tidy data principles. Avoid using multiple tables within one spreadsheet. Avoid spreading data across multiple tabs (but do use a...

Data Organization in Spreadsheets: Glossary https://tess.elixir-europe.org/materials/data-organization-in-spreadsheets-glossary Never modify your raw data. Always make a copy before making any changes. Keep track of all of the steps you take to clean your data. Organize your data according to tidy data principles. Avoid using multiple tables within one spreadsheet. Avoid spreading data across multiple tabs (but do use a new tab to record data cleaning or manipulations).

You can use one set of log-in credentials for many instances Logging off an instance is not the same as turning off an instance Always check a new instance to verify it started correctly Using a program like tmux can keep your work going even if your internet connection is bad No matter which way...

Cloud Genomics: Glossary https://tess.elixir-europe.org/materials/cloud-genomics-glossary You can use one set of log-in credentials for many instances Logging off an instance is not the same as turning off an instance Always check a new instance to verify it started correctly Using a program like tmux can keep your work going even if your internet connection is bad No matter which way you want to move data, it’s easier to start the transfer from your local machine

To install Homebrew, you need have xcode command line tools installed, from the terminal, type: then Run the following command to ensure Homebrew is installed properly: install Python 3:

Python for Ecologists: Instructor NotesChallenge solutions https://tess.elixir-europe.org/materials/python-for-ecologists-instructor-noteschallenge-solutions To install Homebrew, you need have xcode command line tools installed, from the terminal, type: then Run the following command to ensure Homebrew is installed properly: install Python 3: 2017-10-09

Use install.packages() to install packages (libraries). Access individual values by location using []. Access slices of data using [low:high]. Access arbitrary sets of data using [c(...)]. Use logical operations and logical vectors to access subsets of data.

R for Social Scientists: Glossary https://tess.elixir-europe.org/materials/r-for-social-scientists-glossary Use install.packages() to install packages (libraries). Access individual values by location using []. Access slices of data using [low:high]. Access arbitrary sets of data using [c(...)]. Use logical operations and logical vectors to access subsets of data.

OpenRefine will automatically track any steps allowing you to backtrack as needed and providing a record of all work done OpenRefine can import a variety of file types. OpenRefine can be used to explore data using filters. Clustering in OpenRefine can help to identify different values that might...

OpenRefine for Social Science Data: Glossary https://tess.elixir-europe.org/materials/openrefine-for-social-science-data-glossary OpenRefine will automatically track any steps allowing you to backtrack as needed and providing a record of all work done OpenRefine can import a variety of file types. OpenRefine can be used to explore data using filters. Clustering in OpenRefine can help to identify different values that might mean the same thing. OpenRefine can transform the values of a column.

SQL (Structured Query Language) is used to extract data from the tables A schema for a table has to be created before data can be added The schema can be used to provide some data validation on input The DB Browser for SQLite application allows you to connect to an existing database or create a...

SQL for Social Science Data: Glossary https://tess.elixir-europe.org/materials/sql-for-social-science-data-glossary SQL (Structured Query Language) is used to extract data from the tables A schema for a table has to be created before data can be added The schema can be used to provide some data validation on input The DB Browser for SQLite application allows you to connect to an existing database or create a new database When connected to a database you can create new tables

The REPL (Read-Eval-Print loop) allows rapid development and testing of code segments Jupyter notebooks builds on the REPL concepts and allow code results and documentation to be maintained together and shared Jupyter notebooks is a complete IDE (Integrated Development Environment) The Jupyter...

Python for Social Science Data: Glossary https://tess.elixir-europe.org/materials/python-for-social-science-data-glossary The REPL (Read-Eval-Print loop) allows rapid development and testing of code segments Jupyter notebooks builds on the REPL concepts and allow code results and documentation to be maintained together and shared Jupyter notebooks is a complete IDE (Integrated Development Environment) The Jupyter environment can be used to write code segments and display results Datatypes in Python are implicit based on variable values

We organize data in spreadsheets in the ways that we as humans want to work with the data, but computers require that data be organized in particular ways. In order to use tools that make computation more efficient, such as programming languages like R or Python, we need to structure our data...

Data Organization in Spreadsheets https://tess.elixir-europe.org/materials/data-carpentry-spreadsheets-for-ecology We organize data in spreadsheets in the ways that we as humans want to work with the data, but computers require that data be organized in particular ways. In order to use tools that make computation more efficient, such as programming languages like R or Python, we need to structure our data the way that computers need the data. Since this is where most research projects start, this is where we want to start too! In this lesson, you will learn: In this lesson, however, you will not learn about data analysis with spreadsheets. Much of your time as a researcher will be spent in the initial ‘data wrangling’ stage, where you need to organize the data to perform a proper analysis later. It’s not the most fun, but it is necessary. In this lesson you will learn how to think about data organization and some practices for more effective data wrangling. With this approach you can better format current data and plan new data collection so less data wrangling is needed. Data Carpentry’s teaching is hands-on, so participants are encouraged to use their own computers to insure the proper setup of tools for an efficient workflow. These lessons assume no prior knowledge of the skills or tools. To get started, follow the directions in the “Setup” tab to download data to your computer and follow any installation instructions. 2017-10-09

This lesson is optional The challenge with this lesson is that the instructor’s version of the spreadsheet software is going to look different than about half the room’s. It makes it challenging to show where you can find menu options and navigate through. Instead discuss the concepts of quality...

Data Organization in Spreadsheets: Instructor Notes https://tess.elixir-europe.org/materials/data-organization-in-spreadsheets-instructor-notes This lesson is optional The challenge with this lesson is that the instructor’s version of the spreadsheet software is going to look different than about half the room’s. It makes it challenging to show where you can find menu options and navigate through. Instead discuss the concepts of quality control, and how things like sorting can help you find outliers in your data. Provide information on setting up your environment for learners to view your live coding (increasing text size, changing text color, etc), as well as general recommendations for working with coding tools to best suit the learning environment. The main challenge with this lesson is that Excel looks very different and how you do things is even different between Mac and PC, and between different versions of Excel. So, the presenter’s environment will only be the same as some of the learners. 2017-10-09

Note the file types OpenRefine handles: TSV, CSF, *SV, Excel (.xls .xlsx), JSON, XML, RDF as XML, Google Data documents. Support for other formats can be added with OpenRefine extensions. In this first step, we’ll browse our computer to the sample data file for this lesson (If you haven’t...

Open Refine for Ecology: Instructor Notes https://tess.elixir-europe.org/materials/open-refine-for-ecology-instructor-noteslesson Note the file types OpenRefine handles: TSV, CSF, *SV, Excel (.xls .xlsx), JSON, XML, RDF as XML, Google Data documents. Support for other formats can be added with OpenRefine extensions. In this first step, we’ll browse our computer to the sample data file for this lesson (If you haven’t already, download the data from: https://ndownloader.figshare.com/files/7823341). In this case, I’ve modified the Portal_rodents.csv file. I added several columns: scientificName, locality, county, state, country and I generated several more columns in the lesson itself (JSON, decimalLatitude, decimalLongitude). Data in locality, county, country, JSON, decimalLatitude and decimalLongitude are contrived and are in no way related to the original dataset. Once OpenRefine is open, you’ll be asked if you want to Create, Open, or Import a Project. Exploring data by applying multiple filters OpenRefine supports faceted browsing as a mechanism for 2017-10-09

This lesson uses mostly combined.csv. The 3 other csv files: plots.csv, species.csv and surveys.csv are only needed for the lesson on databases. combined.csv is downloaded directly in the chapter “Starting with Data” and does not need to be downloaded before hand. It however requires that there...

Instructor notes https://tess.elixir-europe.org/materials/instructor-notes This lesson uses mostly combined.csv. The 3 other csv files: plots.csv, species.csv and surveys.csv are only needed for the lesson on databases. combined.csv is downloaded directly in the chapter “Starting with Data” and does not need to be downloaded before hand. It however requires that there is a decent internet connection in the room where the workshop is being taught. To facilitate the download process, the chunk of code that includes the URL where the csv file lives, and where the file should go and be named is included in the code handout (see next paragraph). Using this approach ensures that the file will be where the lesson expects it to be, and teaches good/reproducible practice of automating the download. If the learners haven’t created the data/ directory and/or are not in the correct working directory, the download.file command will produce an error. Therefore, it is important to use the stickies at this point. The code handout (a link to download it is also available on the top bar of the lesson website) is useful for Data Carpentry workshops. It includes an outline of the lesson content, the text for the challenges, the links for the files that need to be downloaded for the lesson, and pieces of code that may be difficult to type for learners with no programming experience/who are unfamiliar with R’s syntax. We encourage you to distribute it to the learners at the beginning of the lesson. As an instructor, we encourage you to do the live coding directly in this file, so the participants can follow along. Some learners may have previous R installations. On Mac, if a new install is performed, the learner’s system will create a symbolic link, pointing to the new install as ‘Current.’ Sometimes this process does not occur, and, even though a new R is installed and can be accessed via the R console, RStudio does not find it. The net result of this is that the learner’s RStudio will be running an older R install. This will cause package installations to fail. This can be fixed at the terminal. First, check for the appropriate R installation in the library; We are currently using R 3.4.x. If it isn’t there, they will need to install it. If it is present, you will need to set the symbolic link to Current to point to the 3.4.x directory:

The challenge with this lesson is that the instructor’s version of the spreadsheet software is going to look different than about half the room’s. It makes it challenging to show where you can find menu options and navigate through. Instead discuss the concepts of quality control, and how things...

Data Organization in Spreadsheets for Social Scientists: Instructor Notes https://tess.elixir-europe.org/materials/data-organization-in-spreadsheets-for-social-scientists-instructor-notes The challenge with this lesson is that the instructor’s version of the spreadsheet software is going to look different than about half the room’s. It makes it challenging to show where you can find menu options and navigate through. Instead discuss the concepts of quality control, and how things like sorting can help you find outliers in your data. Provide information on setting up your environment for learners to view your live coding (increasing text size, changing text color, etc), as well as general recommendations for working with coding tools to best suit the learning environment. The main challenge with this lesson is that Excel looks very different and how you do things is even different between Mac and PC, and between different versions of Excel. So, the presenter’s environment will only be the same as some of the learners. We need better notes and screenshots of how things work on both Mac and PC. But we likely won’t be able to cover all the different versions of Excel.

Discussions can happen between neighbors in a workshop. Then after paired discussion there can be a short general discussion of the types of things that came up in the discussion. You could also have people enter responses to the discussion in the workshop etherpad. Or capture the general...

Genomics Organization: Instructor Notes https://tess.elixir-europe.org/materials/genomics-organization-instructor-notes Discussions can happen between neighbors in a workshop. Then after paired discussion there can be a short general discussion of the types of things that came up in the discussion. You could also have people enter responses to the discussion in the workshop etherpad. Or capture the general responses in that Etherpad. That etherpad is then a resource for learners after the workshop.

Tabular data needs to be structured to be able to work with it effectively Data being sent to a sequencing center also needs to be structured so you can use it. Raw sequencing data should be kept raw somewhere, so you can always go back to the original files. Public data repositories are a great...

Genomics Organization: Glossary https://tess.elixir-europe.org/materials/genomics-organization-glossary Tabular data needs to be structured to be able to work with it effectively Data being sent to a sequencing center also needs to be structured so you can use it. Raw sequencing data should be kept raw somewhere, so you can always go back to the original files. Public data repositories are a great source of genomic data. FIXME

OpenRefine (formerly Google Refine) is a powerful free and open source tool for working with messy data: cleaning it and transforming it from one format into another. This lesson will teach you to use OpenRefine to effectively clean and format data and automatically track any changes that you...

Open Refine for Ecology https://tess.elixir-europe.org/materials/data-carpentry-openrefine-for-ecology OpenRefine (formerly Google Refine) is a powerful free and open source tool for working with messy data: cleaning it and transforming it from one format into another. This lesson will teach you to use OpenRefine to effectively clean and format data and automatically track any changes that you make. Many people comment that this tool saves them literally months of work trying to make these edits by hand. Data Carpentry’s teaching is hands-on, so participants are encouraged to use their own computers to insure the proper setup of tools for an efficient workflow. These lessons assume no prior knowledge of the skills or tools. To get started, follow the directions in the “Setup” tab to download data to your computer and follow any installation instructions. This lesson requires a working copy of OpenRefine (also called GoogleRefine). To most effectively use these materials, please make sure to install everything before working through this lesson. 2017-10-09

VM Image Directories A high-level listing of the directory tree from the dcuser account is shown below. Please note that is may be subject to change over time, but we’ll try to remember to update this doc. We had a couple instances die as we were going through our workshop.

Cloud Genomics: Instructor NotesCloud Genomics Pre-WorkshopDuring the workshop https://tess.elixir-europe.org/materials/cloud-genomics-instructor-notescloud-genomics-pre-workshopduring-the-workshop VM Image Directories A high-level listing of the directory tree from the dcuser account is shown below. Please note that is may be subject to change over time, but we’ll try to remember to update this doc. We had a couple instances die as we were going through our workshop.

for loops let you perform the same set of operations on multiple files with a single command. The options you set for the command-line tools you use are important! Data cleaning is an essential step in a genomics workflow. Bioinformatics command line tools are collections of commands that can be...

Wrangling Genomics: Glossary https://tess.elixir-europe.org/materials/wrangling-genomics-glossary for loops let you perform the same set of operations on multiple files with a single command. The options you set for the command-line tools you use are important! Data cleaning is an essential step in a genomics workflow. Bioinformatics command line tools are collections of commands that can be used to carry out bioinformatics analyses. To use most powerful bioinformatics tools, you’ll need to use the command line.

This lesson will introduce learners to fundamental skills needed for working with their computers through a command-line interface (using the bash shell). They will learn how to navigate their file system, computationally manipulate their files (e.g. copying, moving, renaming), search files,...

Shell Genomics: Instructor Notes https://tess.elixir-europe.org/materials/shell-genomics-instructor-notes This lesson will introduce learners to fundamental skills needed for working with their computers through a command-line interface (using the bash shell). They will learn how to navigate their file system, computationally manipulate their files (e.g. copying, moving, renaming), search files, redirect output and write shell scripts. By the end of the lesson, learners will be prepared to move on to using more advanced bioinformatic command line tools (see the lesson on Data Wrangling and Processing). This lesson is meant to be taught in its entirety. For novice learners, schedule around 4 hours for this lesson. If your learners are already somewhat familiar with the bash shell, the earlier episodes can be condensed. This lesson uses data hosted on an Amazon Machine Instance (AMI). Instructors will be sent information on how to log-in to the AMI by the workshop coordinator a few days before the workshop. If you are running a self-organized workshop, register the workshop with our self-organized workshop form and send us an email at mailto:team@datacarpentry.org with information on how many people you expect to have at the workshop, and we’ll start instances for you to use in the workshop. The day before the workshop, we’ll send you the login information for your learners. Learners will work through an Amazon Web Service (AWS) instance for this lesson. The workshop coordinator will set up AWS instances for your workshop a few days ahead of time. Put the links for all instances on your workshop Etherpad and have learners put their name next to the instance they will use. This prevents learners from accidentally messing up another learner’s filesystem. The workshop coordinator usually sets up more AWS instances than needed for the registered learners. If a learner accidentally deletes or overwrites data files, you can have them change to a different AWS instance.

Never modify your raw data. Always make a copy before making any changes. Keep track of all of the steps you take to clean your data. Organize your data according to tidy data principles. Record metadata in a separate plain text file. Avoid using multiple tables within one spreadsheet.

Data Organization in Spreadsheets for Social Scientists https://tess.elixir-europe.org/materials/lesson-title-glossary Never modify your raw data. Always make a copy before making any changes. Keep track of all of the steps you take to clean your data. Organize your data according to tidy data principles. Record metadata in a separate plain text file. Avoid using multiple tables within one spreadsheet.

This lesson uses SAFI_clean.csv. The direct download link for this file is: https://ndownloader.figshare.com/files/11492171. When time comes in the lesson to use this file, we recommend that the instructors, place the download.file() command in the Etherpad, and that the learners copy and paste...

R for Social Scientists: Instructor Notes https://tess.elixir-europe.org/materials/r-for-social-scientists-instructor-notes This lesson uses SAFI_clean.csv. The direct download link for this file is: https://ndownloader.figshare.com/files/11492171. When time comes in the lesson to use this file, we recommend that the instructors, place the download.file() command in the Etherpad, and that the learners copy and paste it in their scripts to download the file directly from figshare in their working directory. . If the learners haven’t created the data/ directory and/or are not in the correct working directory, the download.file command will produce an error. Therefore, it is important to use the stickies at this point. Some learners may have previous R installations. On Mac, if a new install is performed, the learner’s system will create a symbolic link, pointing to the new install as ‘Current.’ Sometimes this process does not occur, and, even though a new R is installed and can be accessed via the R console, RStudio does not find it. The net result of this is that the learner’s RStudio will be running an older R install. This will cause package installations to fail. This can be fixed at the terminal. First, check for the appropriate R installation in the library; We are currently using R 3.x.y If it isn’t there, they will need to install it. If it is present, you will need to set the symbolic link to Current to point to the 3.x.y directory: Then restart RStudio.

Useful commands for navigating your file system include: ls, pwd, and cd. Most commands take options (flags) which begin with a -. Tab completion can reduce errors from mistyping and make work more efficient in the shell. The /, ~, and .. characters represent important navigational shortcuts....

Shell Genomics: Glossary https://tess.elixir-europe.org/materials/shell-genomics-glossary Useful commands for navigating your file system include: ls, pwd, and cd. Most commands take options (flags) which begin with a -. Tab completion can reduce errors from mistyping and make work more efficient in the shell. The /, ~, and .. characters represent important navigational shortcuts. Hidden files and directories start with . and can be viewed using ls -a.

Note that the figshare download is an archive (.zip) file that rudely explodes all of the files into your current directory. See this slide deck as a sample intro for the lesson: SQL Intro Deck Key points: If you’ve written up a diagram of the data analysis pipeline (raw data -> clean data...

SQL for Ecology: Instructor Notes https://tess.elixir-europe.org/materials/sql-for-ecology-instructor-notes Note that the figshare download is an archive (.zip) file that rudely explodes all of the files into your current directory. See this slide deck as a sample intro for the lesson: SQL Intro Deck Key points: If you’ve written up a diagram of the data analysis pipeline (raw data -> clean data -> import and analyze -> results -> visualization), it can be helpful to identify that you’re now somewhere between clean data and analysis. Tips 2017-10-09
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