http://datacamp-community-prod.s3.amazonaws.com/29aa28bf-570a-4965-8f54-d6a541ae4e06

spaCy is a popular Natural Language Processing library with a concise API. This cheat sheet shows you how to load models, process text, and access linguistic annotations, all with a few handy objects and functions.

spaCy Cheat Sheet: Advanced NLP in Python

https://datacamp-community-prod.s3.amazonaws.com/50d31142-3de0-4159-89b9-18b718a728ef

A cheat sheet that covers several ways of getting data into Python: from flat files such as .txts and .csv to files native to other software, such as Excel, SAS, or Matlab, and relational databases such as SQLite & PostgreSQL.

Importing Data: Python Cheat Sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Cheat+Sheets/Importing_Data_Python_Cheat_Sheet.pdf

https://www.datacamp.com/community/podcast/critical-thinking-in-data-science

<p>Debbie Berebichez, Chief Data Scientist at Metis, speaks with Hugo about what type of critical thinking skills are necessary in data science.</p>

Debbie Berebichez speaks with Hugo about the importance of critical thinking in data science.

Critical Thinking in Data Science

https://www.datacamp.com/community/blog/critical-thinking-in-data-science

https://www.datacamp.com/community/podcast/credibility-crisis-in-data-science

<p>Skipper Seabold, a Director of Data Science at Civis Analytics, discusses the current and looming credibility crisis in data science.</p>

Skipper Seabold, a Director of Data Science at Civis Analytics, discusses the current and looming credibility crisis in data science.

The Credibility Crisis in Data Science

https://www.datacamp.com/community/blog/credibility-crisis-in-data-science

https://www.datacamp.com/community/news/spacy-cheat-sheet-advanced-nlp-in-python-nryt9kwegmb

Check out the first official spaCy cheat sheet! A handy two-page reference to the most important concepts and features.

https://www.datacamp.com/community/news/the-credibility-crisis-in-data-science-with-skipper-seabold-6bbmgpwy1qm

The Credibility Crisis in Data Science (with Skipper Seabold)

https://www.datacamp.com/community/news/python-for-finance-algorithmic-trading-vrfjxb6qk2

This Python for Finance tutorial introduces you to the basics of algorithmic trading in Python

Python For Finance: Algorithmic Trading

https://www.datacamp.com/community/news/object-oriented-programming-in-python-tutorial-tt4da4d8rac

Object-Oriented programming is a widely used concept to write powerful applications. As a data scientist, you will be required to write applications to process your data, among a range of other things.  In this tutorial, you will discover the basics of object-oriented programming in Python.

Object-Oriented Programming in Python: Tutorial

<h2 id="the-state-of-data-driven-digital-transformation">The state of data-driven digital transformation</h2>
<p>As organizations undergo accelerated digital transformations amid the COVID-19 pandemic, the case for leveraging data, machine learning and analytics has never been more important.</p>
<p>The 2020 McKinsey Global Survey on the <a href="https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/global-survey-the-state-of-ai-in-2020">State of Artificial Intelligence</a> reported that 50% of businesses have adopted artificial intelligence in at least one business function, and 22% of organizations attribute at least 5% of earnings before interest and tax to artificial intelligence initiatives.</p>
<p>However, in order to unlock the value of data and artificial intelligence at scale, organizations must democratize their data and unlock organization-wide data fluency. One of the ways to do so is by providing access to trusted and actionable data across teams and skill levels so that everyone can make data-informed business decisions.</p>
<h2 id="data-infrastructure-at-the-core-of-data-democratization">Data infrastructure at the core of data democratization</h2>
<p>Before achieving data fluency, organizations need to get the foundations right. This starts with understanding how data flows through an organization and then building toward an organized and unified data infrastructure.</p>
<p>In a <a href="https://www.datacamp.com/resources/webinars/data-trends-2021">webinar on 2021 data trends and predictions</a>, Ramnath Vaidyanathan, Vice President of Product Research at DataCamp, described how data typically flows through an organization. This flow requires data engineering tools to take in raw data, ingest and transform them, store them in a centralized location, and produce data insights in the form of dashboards and reports, machine learning outputs, and more.</p>
<p>Scaling access to data within the organization depends heavily on the presence of a solid data infrastructure architecture and the use of the latest data infrastructure tooling, which enables organizations to transform raw data into accessible and valuable data insights.</p>
<p><center><a href="https://www.datacamp.com/resources/webinars/data-trends-2021"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1615906487/dataflowsthruorg_xgcspr.png" /></a><br></p>
<h2 id="the-state-of-data-infrastructure-tooling">The  state of data infrastructure tooling</h2>
<p>The importance of data infrastructure and tooling is evidenced by the rapid growth and evolution of the data infrastructure market. Many solutions are meant to streamline working with a particular element of an organization’s data infrastructure, which has resulted in an increasingly fragmented landscape of tooling that can be challenging to navigate.</p>
<p>The following visual created by Dataiku aptly illustrates how the data infrastructure market has been segmented into numerous granular categories, with an expanding list of competing solutions present within each segment.</p>
<p><center><a href="https://blog.dataiku.com/technoslavia-the-fragmented-world-of-data-infrastructure-in-2020"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1615906493/technoslavia_gxmlfq.png" /></a></p>
<h5 id="source-dataiku-https-blog-dataiku-com-technoslavia-the-fragmented-world-of-data-infrastructure-in-2020-">Source: <a href="https://blog.dataiku.com/technoslavia-the-fragmented-world-of-data-infrastructure-in-2020">Dataiku</a></h5>
<h2 id="the-future-of-data-infrastructure-tooling">The future of data infrastructure tooling</h2>
<p>Late last year, Andreessen Horowitz sought to provide some clarity in this confusing data infrastructure landscape by developing a unified architecture framework. With the growing need to work with large volumes of data and the plethora of available solutions, this framework offers a practical reference when modernizing, mapping, and building an organization’s data stack.</p>
<p><center><a href="https://a16z.com/2020/10/15/the-emerging-architectures-for-modern-data-infrastructure/"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1615906493/andreeson_bjpxzl.png" /></a></p>
<h5 id="source-andreessen-horowitz-https-a16z-com-2020-10-15-the-emerging-architectures-for-modern-data-infrastructure-">Source: <a href="https://a16z.com/2020/10/15/the-emerging-architectures-for-modern-data-infrastructure/">Andreessen Horowitz</a></h5>
<p>As this space and trend evolves, we will see the consolidation and standardization of various tools in the data infrastructure stack. This alongside cloud adoption, data discovery tools, and centralized data governance platforms will allow organizations to provide scalable access to data across teams and functions.</p>
<p>So what does an ideal future data infrastructure deliver? According to <a href="https://www.datacamp.com/resources/webinars/operationalizing-data-within-large-organizations">Maksim Pecherskiy</a>, Data Engineer at the World Bank and former CDO of the city of San Diego, data must be <strong><em>collected, discoverable, reliable, understood, compliant,</em></strong> and <strong><em>actionable</em></strong>. Ultimately, the goal is to make data usable and trustworthy, which in turn drives organization-wide data fluency.</p>
<p><center><a href="https://www.datacamp.com/resources/webinars/data-trends-2021"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1615906507/usedandtrusted_wide_ypurb3.png" /></a></p>
<h2 id="mature-data-infrastructure-includes-cloud-adoption-data-discovery-tools-and-centralized-data-governance-platforms">Mature data infrastructure includes cloud adoption, data discovery tools, and centralized data governance platforms</h2>
<p>As data infrastructure tooling matures around data democratization in 2021 and beyond, so will the adoption and growth of the following technologies:</p>
<h3 id="1-cloud-adoption">1. Cloud Adoption</h3>
<p>The use of cloud technologies is <a href="https://www.oreilly.com/radar/cloud-adoption-in-2020/">becoming the norm</a>, especially in the wake of the accelerated data-driven digital transformation we’ve seen in 2020.</p>
<p>Besides improving data storage and access, cloud solutions also offer numerous user-friendly products for collecting, ingesting, transforming, storing, and creating insights from data.</p>
<p>There are numerous providers in the cloud platform space, and one of the key players is <a href="https://aws.amazon.com/">Amazon Web Services</a> (AWS), which has <a href="https://aws.amazon.com/products/">more than 100 products</a> that enable enterprises to make the most out of their data at high speed, at scale, and at low cost.</p>
<h3 id="2-data-discovery-tools">2. Data Discovery Tools</h3>
<p>Scaling data infrastructure will be crucial as data tables, reports, and models are added to organizational data landscapes. Stakeholders must be readily able to locate reliable, understood, compliant, and actionable data and insights to do their best work.</p>
<p>To tackle this challenge, <a href="https://datacamp.com/community/data-discovery-tools">data discovery tools</a> (also referred to as metadata management tools or data catalogs) can act as versatile search engines for these troves of data within the organization. Metadata essentially refers to a set of data describing the particular data, and is useful for indexing the large variety of data available.</p>
<p>Lyft open-sourced their own data discovery tool <a href="https://www.amundsen.io/">Amundsen</a>, to offer metadata engine solutions that aim to help organizations discover, understand, share, and trust the data they use for their analyses. This capability is paramount for any organization looking to scale its data competencies.</p>
<h3 id="3-centralized-data-governance-platforms">3. Centralized Data Governance Platforms</h3>
<p>As mentioned earlier, creating trust in data is essential for driving data democratization.  This is where data governance plays a major role. With the ever-growing volume and variety of data, centralized oversight of the data quality, consistency, and relevance is crucial.</p>
<p>Centralized data governance platform services offered by companies like <a href="https://www.collibra.com/">Collibra</a> and <a href="https://www.alation.com/solutions/data-governance/">Alation</a> help organizations effectively manage the availability, consistency, and integrity of their data. This then allows all stakeholders to work from a reliable single source of truth.</p>
<p>A maturing data infrastructure is only one of the data trends we foresee for 2021 and beyond. For more, download our white paper on <a href="https://www.datacamp.com/resources/whitepapers/data-trends-and-predictions-2021">Data Trends and Predictions 2021: The Year of Data Fluency</a> or watch our <a href="https://www.datacamp.com/resources/webinars/data-trends-2021">webinar</a>.</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/v1603718763/banner_style_1_cruuf7.png" /></a></p>


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

Unlocking the value of data in an organization starts with having the right data infrastructure and tooling foundations. Here’s a look at the current state and future trends of data infrastructure.

What Mature Data Infrastructure Looks Like | DataCamp

What Mature Data Infrastructure Looks Like

<p>The most successful organizations today rely heavily on data to make decisions. Many of these data-fluent organizations generate millions of rows of data every day that are used and analyzed by a wide range of teams. One of the key challenges to equipping teams with the ability to access and work with data is ensuring that data is <a href="https://www.datacamp.com/resources/webinars/operationalizing-data-within-large-organizations">collected, trusted, reliable, actionable, and discoverable</a>.</p>
<p>To navigate these complex data landscapes, many organizations have developed and open-sourced in-house technologies, called data discovery tools.</p>
<p>In a <a href="https://www.datacamp.com/resources/webinars/the-path-to-data-fluency">recent webinar</a>, Ramnath Vaidyanathan, VP of Product Research at DataCamp describes how organizations can go from <strong><em>data reactive</em></strong>, where no one has the skills or access to work with data, to <strong><em>data fluent</em></strong>, where everyone has access and skills to work with data and make data-driven decisions. Organizations can increase their data maturity through investments in the <a href="https://www.datacamp.com/community/blog/iptop">IPTOP framework</a>: infrastructure, people, tools, organization, and processes. As organizations scale access to data through investments in data infrastructure, they will need to provide easy data discoverability with data discovery tools.</p>
<blockquote>
<p>Data is not useful if people cannot access it, if they don’t understand the context of the data collected, and if they cannot find what they need. This is why data discovery tools are really critical.
Ramnath Vaidyanathan, VP of Product Research at DataCamp</p>
</blockquote>
<p><center><a href="https://www.datacamp.com/webinars/the-path-to-data-fluency"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1615904291/Screen_Shot_2021-03-16_at_10.18.07_AM_mp6zpm.png" /></a><br></p>
<h2 id="how-data-fluent-companies-use-data-discovery-tools">How data-fluent companies use data discovery tools</h2>
<p>Many data-driven companies have implemented data discovery tools in their data infrastructure. Let’s examine the motivations and drivers behind notable data discovery tools like <a href="https://eng.uber.com/metadata-insights-databook/">Uber Databook</a> and <a href="https://www.amundsen.io/">Lyft Amundsen</a>.</p>
<p><center><a href="https://www.datacamp.com/webinars/the-path-to-data-fluency"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1615904178/datadiscoveryengines_pi2uj7_oqvtm7.png" /></a><br></p>
<h3 id="data-discovery-challenges-to-consider">Data discovery challenges to consider</h3>
<p>A key data discovery challenge is productivity and compliance. <a href="https://eng.lyft.com/amundsen-lyfts-data-discovery-metadata-engine-62d27254fbb9">Lyft outlined</a> that it faced these challenges as the size of its data grew exponentially and was projected to continue to grow at a similar rate for at least the next 10 years. When developing Amundsen, they noted that about 25% of an analyst’s time was spent on data discovery. They also saw increasingly more stringent compliance demands in the countries they serviced.</p>
<p><a href="https://eng.uber.com/metadata-insights-databook/">Uber expressed similar concerns</a> as it began to expand beyond its primary function as a ride-sharing app to services like Uber Eats, Uber Freight, and Jump Bikes. When developing Databook, Uber had over 15 million trips a day and 18,000 employees across different teams. Another challenge was that its data <a href="https://eng.uber.com/databook/">existed in many different forms</a> including <a href="https://hive.apache.org/">Hive</a>, <a href="https://prestodb.io/">Presto</a>, and <a href="https://www.vertica.com/">Vertica</a>. Analysts needed to be able to efficiently access and understand the various data sources in order to achieve data fluency.</p>
<p><a href="https://engineering.fb.com/2020/10/09/data-infrastructure/nemo/">Facebook built its Nemo data discovery platform</a> to address the need for high-quality, trustworthy data as data size and complexity increased across roles and geographic locations. Due to the privacy concerns associated with the data they analyze, Facebook also faced significant compliance concerns that had to be addressed when offering tables to specific analysts.</p>
<p>John Bodley from Airbnb said <a href="https://www.youtube.com/watch?v=gayXC2FDSiA">in a talk at a 2017 conference</a>, “As Airbnb grows, so do the challenges around the volume, complexity, and obscurity of data.” This means that employees often found data to be “siloed, inaccessible, and lacking context.” As a result, employees were making duplicate tables and did not use data if they didn’t trust it. Airbnb developed <a href="https://medium.com/airbnb-engineering/democratizing-data-at-airbnb-852d76c51770">Dataportal</a> to help make the data discovery process significantly more efficient by removing these barriers to productive data analysis.</p>
<p>These challenges are widespread, industry-agnostic, and apply to any organization looking to scale data-driven decision making. The need for easily discoverable data will become paramount as organizations look to become data fluent.</p>
<h2 id="the-four-key-goals-of-data-discovery-tools">The four key goals of data discovery tools</h2>
<p>Now that we understand data discovery challenges, let’s look at how various data discovery tools address them. <a href="https://eng.uber.com/databook/">Uber</a> breaks down the goals of a  successful data discovery platform into four components:</p>
<ol>
<li><strong><em>Extensibility</em></strong>: The ability to easily add metadata, storage, and entities to tables</li>
<li><strong><em>Accessibility</em></strong>: The ability to programmatically access all metadata</li>
<li><strong><em>Scalability</em></strong>: The ability to support a lot of concurrent read requests</li>
<li><strong><em>Power</em></strong>: The ability to support read and write requests across multiple data centers</li>
</ol>
<p>Attaining these data discovery goals increase as organizations mature their data competencies. As the scale of their data increases, each of these components becomes increasingly more important.</p>
<h2 id="lyft-and-airbnb-provide-detailed-metadata">Lyft and Airbnb provide detailed metadata</h2>
<p><a href="https://eng.lyft.com/amundsen-lyfts-data-discovery-metadata-engine-62d27254fbb9">Lyft’s engineering team</a> argues that “metadata is the holy grail of future applications.” They break metadata down into two subcategories. The first is a describing set of data made up of application context <em>(i.e., what humans need to know about the data to operate with it)</em>, behavior <em>(i.e., who owns a particular dataset and its common usage patterns)</em>, and change <em>(i.e., how the dataset is changing over time)</em>. The second component is the data being described which is any data stored in the organization in any format such as data stores, dashboards, datastreams, and more.</p>
<p>When developing <a href="https://medium.com/airbnb-engineering/democratizing-data-at-airbnb-852d76c51770">Dataportal</a>, Airbnb’s data discovery platform, metadata was an integral part of the platform’s added value to the data pipeline. Before Dataportal was introduced, many employees did not trust the data they were using due to a lack of context and metadata. Airbnb argues that “the understanding of the entire data ecosystem, from the production of an event log to its consumption in visualization, provides more value than the sum of its parts.”</p>
<p><center><a href="https://medium.com/airbnb-engineering/democratizing-data-at-airbnb-852d76c51770"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1615904101/airbnbimage_irgwjr.png" /></a><br></p>
<h5 id="source-airbnb-https-medium-com-airbnb-engineering-democratizing-data-at-airbnb-852d76c51770-">Source: <a href="https://medium.com/airbnb-engineering/democratizing-data-at-airbnb-852d76c51770">Airbnb</a></h5>
<h2 id="ensure-high-quality-search-results-with-data-discovery">Ensure high-quality search results with data discovery</h2>
<p>Once this context is provided, data discovery requires effective search. No matter how trustworthy and easy-to-understand each table is, data discovery platforms are only useful if analysts can find the data they need quickly.     </p>
<p><a href="https://www.amundsen.io/">Lyft Amundsen’s</a> landing page allows users to search for tables using natural language queries and provides visibility into the most popular tables used in the organization for quick access. Furthermore, analysts have the option to leave feedback on the table to tune future search results.</p>
<p>Facebook’s data discovery solution, <a href="https://engineering.fb.com/2020/10/09/data-infrastructure/nemo/">Nemo</a>, allows analysts to filter on usage, privacy restrictions, and recency of data while also leveraging Facebook’s competency in natural language processing by allowing users to type questions into the search bar to get relevant tables.  </p>
<p>Uber Databook allows similar search filtering capabilities by allowing analysts to filter on dimensions such as name, owner, column, and nested columns. Uber leverages <a href="https://en.wikipedia.org/wiki/Elasticsearch">Elasticsearch</a> to deliver search results quickly and effectively.</p>
<h2 id="arriving-at-data-democratization-through-data-discovery">Arriving at data democratization through data discovery</h2>
<p>Scaling data fluency and data discoverability go hand in hand, as analysts need to have easy access to data to make data-informed decisions.</p>
<p><center><a href="https://www.datacamp.com/webinars/the-path-to-data-fluency"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1615904005/Screen_Shot_2021-03-16_at_10.13.21_AM_romy3u.png" /></a><br></p>
<p>Organizations with low data maturity should focus on building up data skills and culture and scaling data infrastructure. However, as organizations progress in their data maturity journey and expand data access, enabling extensible and scalable data discovery will be integral to becoming a data-driven organization. For an in-depth discussion on all the levers to grow organizational data maturity, watch our webinar on <a href="https://www.datacamp.com/webinars/the-path-to-data-fluency">the path to data fluency</a>.</p>


https://www.datacamp.com/community/blog/data-discovery-tools

Understand how top-performing companies solve data discovery challenges to drive data fluency at scale.

How Data Discovery Tools Enable Data Democratization | DataCamp

How Data Discovery Tools Enable Data Democratization

<h2>Conceptos básicos de Python</h2><p>Una introducción a los conceptos básicos de Python. Aprende como utilizar Python de forma interactiva y a tráves de una consola. Crea tus primeras variables y familiarícese con los tipos de datos básicos en Python.</p><h2>Listas de Python</h2><p>Aprende a almacenar, acceder y manipular datos en una lista: el primer paso hacia un trabajo eficiente con gran cantidad de datos.</p><h2>Funciones y paquetes</h2><p>Para aprovechar el código que los desarrolladores de Python han escrito, aprenderás sobre funciones, métodos y paquetes. ¡Esto te ayudará a reducir la cantidad de código que necesitas para resolver problemas!</p><h2>NumPy</h2><p>NumPy es un poderoso paquete de Python para hacer ciencia de datos eficientemente. Aprende como trabajar con arrays de NumPy, una alternativa más rápida y poderosa a las listas, y toma tus primeros pasos en la exploración de datos.</p>

https://www.datacamp.com/community/open-courses/introduccion-a-python

Aprenderás acerca de poderosas maneras de almacenar y manipular datos, así como también geniales herramientas de ciencia de datos para comenzar tus propios análisis. 

Introducción a Python

<h2>Introduction</h2><p>Introduction to data.world</p><h2>Working with datasets</h2><p>Working with data.world datasets</p><h2>Working with multiple datasets</h2><p>Working with multiple datasets</p><h2>Querying with data.world</h2><p>Querying with data.world</p><h2>Wrap up & next steps</h2><p>Setup your own data.world account and apply what you've learned and more!</p>

https://www.datacamp.com/community/open-courses/intro-to-dataworld-in-python

data.world allows data people to discover and share cool data. In this course, you'll learn to use the data.world Python SDK to pull data and metadata into your environment.

Intro to data.world in Python

<p>Python is the most popular programming language today and is widely used across verticals from software and web development to game development, IoT and more. More importantly, it’s become the de facto programming language for data science and machine learning. This blog post will outline why and when to use Python, how to install it, basic Python syntax and functionality, an introduction to simple data analysis, and more.</p>
<h2 id="when-and-why-to-use-python">When and why to use Python</h2>
<p>Python is a highly versatile language with many use cases across verticals. Just like many programming languages, Python is open source and free to use, and contains a large library of tools and packages (more on packages later!) that simplify working with a variety of tasks. Here’s are some verticals where Python is useful:</p>
<h3 id="desktop-and-mobile-software-development">Desktop and Mobile Software Development</h3>
<p>Whether building simple software applications like calculators, or document editors and social networks. Python offers a plethora of tools to prototype, develop, and run desktop or mobile applications.</p>
<h3 id="web-development">Web Development</h3>
<p>Did you know that web applications like Uber, Netflix, and Spotify <a href="https://yourstory.com/mystory/top-twelve-python-applications-real-world-know">were built with Python</a>? There are a variety of web frameworks on Python that simplify website development.</p>
<h3 id="game-development">Game Development</h3>
<p>This may come as a surprise, but games such as The Sims 4 and Civilization IV were built with Python. Many Python libraries exist that streamline prototyping and development of games.</p>
<h3 id="internet-of-things-iot-">Internet of Things (IoT)</h3>
<p>The rise of embedded systems and the internet of things (IoT) has catalyzed innovation and business process improvement across domains. Python offers a set of packages that make it easy to design, create, and deploy an IoT device on <a href="https://www.arduino.cc/">Arduino</a>, <a href="https://www.raspberrypi.org/">Raspberry Pi</a>, and other <a href="https://www.home-assistant.io/integrations/">IoT devices</a> such as Alexa and Google Home.</p>
<h3 id="data-science-and-machine-learning">Data Science and Machine Learning</h3>
<p>Python’s rise in popularity can be attributed to its rich set of packages and tools for data science and machine learning. Python can be used across the data science workflow, from exploratory data analysis, data pre-processing, model building and deployment, and finally to results interpretation and communication. Since Python is used across the data science workflow, it is used by a variety of data roles from data analysts, data scientists, data engineers, machine learning engineers, and more.</p>
<h2 id="how-to-install-python">How to install Python</h2>
<p>Python can be installed in a variety of avenues, depending on if you have a Mac or Windows, or if you want to install it with Anaconda. Since Windows and Mac are installed similarly, the following instructions can apply to both operating systems. If you want a deeper look into installing Python for data science, <a href="https://www.datacamp.com/community/tutorials/python-developer-set-up">check out this article</a>.</p>
<h3 id="mac-and-windows">Mac and Windows</h3>
<p>Go to the Python website, which will send you to the downloads page as seen in the screenshot below. Choose Windows or Mac and click on the download button to access the installer package.</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1617984299/python1_j4xkeo.png" /></center><br></p>
<h3 id="anaconda">Anaconda</h3>
<p>One of the more convenient ways to install Python for data science is by installing an <a href="https://www.anaconda.com/products/individual">Anaconda</a> distribution, which also installs all relevant data science packages. Follow the link above to install it on either Mac, Windows, or Linux. For a deep dive on installing Anaconda, check out these blog posts for <a href="https://www.datacamp.com/community/tutorials/installing-anaconda-mac-os-x">MacOs</a> and <a href="https://www.datacamp.com/community/tutorials/installing-anaconda-windows">Windows</a>.</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1617985265/DLanaconda_x9jrol.png" /></center><br></p>
<h2 id="how-to-get-started-with-python-for-data-science">How to get started with Python for data science</h2>
<p>In this section, we will cover how to open Python on Mac and Windows, how to launch the terminal, the basics of Python like print statements and operators, variables and assigning values, data types, packages and how to install them, as well as the popular data science library <code>pandas</code> and how to install it.</p>
<h3 id="1-launching-python">1. Launching Python</h3>
<h4 id="launching-terminal">Launching Terminal</h4>
<p>To launch Python, you will first need to launch your terminal. For both Mac and Windows, you can simply search it on your computer. On Mac, the easiest way is to do a Spotlight Search, which is the magnifying glass icon on the upper right corner of your desktop.</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1617984299/python2_uasjil.png" /></center><br></p>
<p>Similarly, for Windows, you will search for the command prompt through the start menu, as described in a DataCamp article <a href="https://www.datacamp.com/community/tutorials/running-a-python-script">How to Run Python Scripts</a>. You can also launch the new Windows Terminal after installing and searching for it as mentioned on the Windows blog post <a href="https://devblogs.microsoft.com/commandline/introducing-windows-terminal/">Introducing Windows Terminal</a>. After you launch it, here&#39;s what you will see:</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1617984299/python3_pv1jvj.png" /></center><br></p>
<h4 id="how-to-open-python-on-mac-windows">How to open Python on Mac + Windows</h4>
<p>After Python installation is complete, you can launch your terminal by simply typing the word ‘python’. This will start up Python and show you the current version you have installed. The three arrows point to the right means you are ready to code in Python!</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1617984299/python4_gajrzb.png" /></center><br></p>
<h3 id="2-basic-python-syntax">2. Basic Python Syntax</h3>
<p>In the following syntax commands below, you will type the command and then hit <code>enter</code> or <code>return</code> on your keyboard to see the results.</p>
<h4 id="print-statements">Print statements</h4>
<p>Next, you can start your first command in Python. We will start off with the <code>print()</code> statement, which will be typing the word print, followed by parenthesis. The output will be typed within those same parentheses and enclosed by quotes.</p>
<pre><code class="lang-python">print(&quot;Hello, World&quot;)
</code></pre>
<pre><code>Hello, World
</code></pre><h4 id="operators">Operators</h4>
<p>Then, you can perform operations, like +, -, and *.  As you can see, the + adds the two numbers together, the - subtracts the second number from the first number, the * multiples both numbers together.</p>
<pre><code class="lang-python">5 + 5
</code></pre>
<pre><code>10
</code></pre><pre><code class="lang-python">5 - 5
</code></pre>
<pre><code>0
</code></pre><pre><code class="lang-python">5 * 5
</code></pre>
<pre><code>25
</code></pre><h4 id="booleans">Booleans</h4>
<p>The next command is to compare two values. In the case below, we will look at comparing the same two numbers, five and five. The command is asking if 5 is greater than 5, which of course, it is not, so the returned output is <code>False</code>. For booleans, we expect the output to be either true or false.</p>
<pre><code class="lang-python">5 &gt; 5
</code></pre>
<pre><code>False
</code></pre><h4 id="creating-variables-and-assigning-them">Creating variables and assigning them</h4>
<p>In this section, we see that you can assign a number to the name <code>variable_1</code>, and then return it by calling that same <code>variable_1</code> to get the value that you had assigned it:</p>
<pre><code class="lang-python">variable_1 = 100
</code></pre>
<pre><code class="lang-python">variable_1
</code></pre>
<pre><code class="lang-python">100
</code></pre>
<h4 id="lists">Lists</h4>
<pre><code class="lang-python">my_list = [&#39;potato&#39;, 3, 4.02]
</code></pre>
<pre><code class="lang-python">my_list[0]
</code></pre>
<pre><code>‘potato’
</code></pre><p>A list is a data structure in which values can be stored and accessed. The neat thing about lists is that they do not all need to be the same data type. Below, we can see that we have a str, int, and a float, respectively. In order to access the items in the list so that they appear in the output, you can type your list name followed by brackets with the place of the item you want. For example, we append [0] to our listname to return the 1st element in our list as seen below.</p>
<p>For a further exploration into Python lists, see the article <a href="https://www.datacamp.com/community/tutorials/python-list-index">Python List Index()</a>.</p>
<h4 id="data-types">Data types</h4>
<p>Python values can have a variety of forms, including ones like <code>int</code>, <code>float</code>, and <code>str</code>. For example, <code>int</code> represents integers, <code>float</code> represents decimal numbers, and <code>str</code> represents “strings”, or more generally text.</p>
<p>Using the <code>type()</code> function, we will be able to check the type of our values. In this case, we’re looking at three values, including 10, 10.06, and the word ‘tutorial’. The first is an integer, while the second is a float, and the last is a string. Below, we can see the command and its respected data type output.</p>
<pre><code class="lang-python">type(10)
</code></pre>
<pre><code>&lt;class &#39;int&#39;&gt;
</code></pre><pre><code class="lang-python">type(10.06)
</code></pre>
<pre><code>&lt;class &#39;float&#39;&gt;
</code></pre><pre><code class="lang-python">type(&#39;tutorial&#39;)
</code></pre>
<pre><code>&lt;class &#39;str&#39;&gt;
</code></pre><p>For a more in-depth look at data types and how to convert them, you can read the following blog, <a href="https://www.datacamp.com/community/tutorials/python-data-type-conversion">Python Data Type Conversion Tutorial</a>.</p>
<h4 id="packages-and-installation">Packages and installation</h4>
<p><a href="https://docs.python.org/3/installing/index.html">Packages</a> are powerful tools that have preserved functionalities. For example, <code>pandas</code> is a package that lets you work with tabular data in Python—more on that below. Think of them as apps you download on Python that provide a specific functionality. You will want to exit out of Python in your terminal by typing Ctrl + D, and then typing the following command below (for Mac users). Here we’re installing a sample package called SomePackage just to illustrate the installation.</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1617984300/python10_uwyf92.png" /></center><br></p>
<h4 id="installing-pandas-and-jupyter-notebook">Installing pandas and Jupyter Notebook</h4>
<p><a href="https://www.datacamp.com/community/tutorials/pandas">pandas</a> is perhaps the most used data science library in Python. It allows you to work with tabular data in Python and perform a variety of operations. In some sense, you can consider it as the Excel or spreadsheets of Python. The <a href="https://pandas.pydata.org/">developers of pandas</a> describe it as “a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.”</p>
<p>Jupyter notebook is a web application that interprets code, saves text, and displays visualizations. Both Jupyter and pandas are available to launch if you downloaded Anaconda, or you can install them by running the following commands in terminal:</p>
<pre><code class="lang-python">pip install pandas
</code></pre>
<pre><code class="lang-python">pip install notebooks
</code></pre>
<h3 id="3-basic-data-science-with-python">3. Basic data science with Python</h3>
<p>In this section, we’ll cover how to import a popular Python package while assigning it an alias, how to read in a CSV file, how to show your data in something called a DataFrame, and how to visualize a column by using a certain plotting function. We have already learned how to install pandas above, and because Jupyter Notebook is easy to use and works across several computer types, we will use that web application to display the following data science functions with pandas.</p>
<p>You can launch a Jupyter notebook by simply typing <code>jupyter notebook</code> in your terminal.</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1617990202/jupyter_hello_world2_su37ik.png" /></center><br></p>
<h4 id="importing-packages-with-an-alias">Importing packages with an alias</h4>
<p>Before we begin working with pandas, we need to import it in our environment. Similar to launching an app after installing it, importing a package is simple. We can import pandas and assign an alias to it that is shorter than the original name so that it is easier to work with this package. We will assign pandas as the letters <code>pd</code> instead for shorthand notation—this change is purely optional.</p>
<pre><code class="lang-python">import pandas as pd
</code></pre>
<h4 id="importing-your-data-by-reading-in-a-csv-file">Importing your data by reading in a CSV file</h4>
<p>After you have imported pandas as <code>pd</code>, you can use <code>pd</code> to read in your data, which can be a CSV file. This function is to enclose the CSV file path in quotes, within the parentheses of <code>pd.read_csv()</code>. You can then store your CSV to a dataframe with the = operation. You will now be able to print out your dataframe, but you will only want to see the first rows instead of all of it. The data, in this case, is from past year’s stock performance with the fields date, open, high, low, close, volume, and name, all of which describe characteristics of daily popular stocks. However, it is important to note that you can use any CSV file here to illustrate importing data.</p>
<h4 id="dataframe-head-function">Dataframe head function</h4>
<p>Showing the first few rows of your data is important to check to see if you have imported the correct CSV, as well as seeing if data looks good in general. The way to do this method is by calling your dataframe and attaching the <code>.head()</code> method to the end of it. This function will return the first 5 rows of your data by default. If you want to show more, you can put that value in the parentheses of <code>.head()</code>.</p>
<pre><code class="lang-python">import pandas as pd
</code></pre>
<pre><code class="lang-python">stocks_df = pd.read.csv(&#39;/documents/data/stock_data.csv&#39;)
</code></pre>
<pre><code class="lang-python">stocks_df.head()
</code></pre>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1617986452/Screen_Shot_2021-04-09_at_12.40.35_PM_jtlhgz.png" /></center><br></p>
<h4 id="visualize-column-with-the-plot-function">Visualize column with the plot function</h4>
<p>The <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html">.plot()</a> method  is useful for displaying your dataframe’s columns. In this example, I am showing the x-axis as the “date” column, and the “close” column amount as the y-axis, visualized by a line chart.</p>
<pre><code class="lang-python">stocks_df.plot(x=&quot;date&quot;, y=&quot;close&quot;)
</code></pre>
<pre><code>&lt;AxesSubplot:xlabel=&#39;date&#39;&gt;
</code></pre><p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1617986452/Screen_Shot_2021-04-09_at_12.40.20_PM_zkb0am.png" /></center><br></p>
<h3 id="4-suggestions-for-practical-applications-">4. Suggestions for practical applications:</h3>
<p>This overview of Python for data science has only just scratched the surface. There are plenty of practical applications that you can employ with Python in data science across several industries, including customer churn, sales forecasting, image classification, product classification, sentiment analysis, credit card fraud detection, and recommendation systems. DataCamp’s <a href="https://www.datacamp.com/community/tutorials/recommender-systems-python">Beginner Tutorial: Recommender Systems in Python</a> demonstrates how to successfully recommend movies to people based on a variety of factors with the use of machine learning. Another popular data science use case in the financial industry is credit card fraud detection, which is explained in <a href="https://www.datacamp.com/community/news/credit-card-fraud-detection-using-machine-learning-algorithm-8dyh3fefvrb">Credit Card Fraud Detection Using Machine Learning Algorithm</a>. As you can see, not only are there plenty of specific data science use cases, but there are also industry specific use cases.</p>
<p>For a more in-depth introduction to Python, check out DataCamp’s <a href="https://www.datacamp.com/courses/intro-to-python-for-data-science">Introduction to Python</a> course, or find out more about <a href="https://www.datacamp.com/community/blog/how-to-learn-python">learning Python on our blog</a>.</p>


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