http://datacamp-community-prod.s3.amazonaws.com/72e88aa1-b4f2-4658-9d86-15becf8263df

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/the-data-storytelling-skills-data-teams-need

<p>In this episode of DataFramed, we speak with Andy Cotgreave, Technical Evangelist at Tableau about the role of data storytelling when driving change with analytics, and the importance of the analyst role within a data-driven organization.</p>

In this episode of DataFramed, we speak with Andy Cotgreave, Technical Evangelist at Tableau about the role of data storytelling when driving change with analytics, and the importance of the analyst role within a data-driven organization.

The Data Storytelling Skills Data Teams Need

https://www.datacamp.com/community/blog/the-data-storytelling-skills-data-teams-need

https://www.datacamp.com/community/podcast/smart-cities-with-data-science

<p>Amen Ra Mashariki deep dives into the state of data literacy in government agencies, the use cases he worked on to make the City of New York smarter, and more!</p>

Amen Ra Mashariki deep dives into the state of data literacy in government agencies, the use cases he worked on to make the City of New York smarter, and more!

Creating Smart Cities with Data Science

https://www.datacamp.com/community/blog/smart-cities-with-data-science

https://www.datacamp.com/community/news/data-science-at-atandt-labs-research-with-noemi-derzsy-w1vwosxhaq

Noemi Derzsy, a Senior Inventive Scientist at AT&T Labs Research within the Data Science and AI Research organization, discusses her work there and much more!

Data Science at AT&T Labs Research (with Noemi Derzsy)

https://www.datacamp.com/community/news/video-intro-to-python-for-spreadsheet-users-6fmsw8na1xp

In this video tutorial, you'll watch the end-to-end automation of a routine spreadsheet task being completed using Python.

Video: Intro to Python for Spreadsheet Users

https://www.datacamp.com/community/news/getting-started-with-the-tidyverse-tutorial-hf369yzqzar

Start analyzing titanic data with R and the tidyverse: learn how to filter, arrange, summarise, mutate and visualize your data with dplyr and ggplot2!

Getting Started with the Tidyverse: Tutorial

<p>Table of Contents</p>
<ol>
<li>Introduction: the Data Analyst role     </li>
<li>From 9 to 5: A Day in the Life of a Data Analyst in 2022</li>
<li>Most Common Tools for Data Analysts</li>
<li>Where you can Learn these Skills</li>
<li>Other Important Skills for a Successful Career as a Data Analyst</li>
<li>Resume &amp; Interviewing: Data Analyst Job Search Prep</li>
<li>Building a Career</li>
<li>Get Started Today</li>
</ol>
<p><strong>Data analysis can offer you job security, the potential for remote work, high pay, and interesting challenges… intrigued?</strong></p>
<p>Careers in data analysis have become very popular in the last 5-10 years for all of these great reasons. While analysts have existed in organizations for years, new tech tools and a major influx in data collection in organizations is reigniting serious interest in this career path.</p>
<p>From tracking sales and orders, to customer and stakeholder management, data is an integral part of every organization. “The use of analytics by businesses can be found as far <a target="_blank" href="https://www.dataversity.net/brief-history-analytics/#">back as the 19th century;</a>” however, as more of the world gets online, making data meaningful has grown to become a critical part of most modern businesses&#39; survival.</p>
<centre><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1644846055/image3_ygyck1.png"></centre>
<centre> <a target="_blank" href="https://www.datasciencecentral.com/data-science-job-market-shrinking-not-so-fast/">Ref: Data Science Job Market Shrinking?</a> </centre>


<p>This new environment, combined with the rise of Massive Open Online Courses (MOOCs) and other open courses, has created a new market for data analytics careers. While graduate programs and certificates are rapidly appearing, professionals still have the chance to make their way into this field from diverse disciplines, using open courseware and self-paced material available online.</p>
<p>Makeup of education for Data Analysts in the US in 2021:</p>
<p><centre><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1644846055/image2_vaxjpb.png"></centre>
Source: <a target="_blank" href="https://www.zippia.com/data-analyst-jobs/demographics/">zippia</a></p>
<p>Specialists from all backgrounds have found lucrative opportunities in this field by focusing on the core skills needed to solve business problems, and support non-technical managers.<br>While this role is still rapidly evolving, many things have remained the same: the need for critical thinking, the ability to collaborate with and present to diverse audiences, subject matter expertise, and an understanding of the fundamental need for each step of the data analytics process. We will cover all of these areas in detail in the rest of this article.</p>
<p>##From 9 to 5: A Day in the Life of a Data Analyst in 2022##
<strong>Choose your own adventure!</strong> Most modern data analyst roles fall into one of two categories: you’re either the “data person” in a non-technical department, or an analyst in a technical data-focused department, working alongside other analysts, engineers, and scientists. Your days will look very different depending on the type of data analyst you are.</p>
<p><strong>When you are “the data person” in a non-technical department</strong>, you will collaborate with your manager and colleagues every day. From gathering, cleaning, and analyzing data, you will support their efforts to make data-driven decisions. These roles usually heavily focus on <a href="https://www.analyticsvidhya.com/blog/2021/06/must-known-data-visualization-techniques-for-data-science/">reporting and visualizations</a>, so you’ll likely <a target="_blank" href="https://www.datapine.com/blog/data-dashboards-definition-examples-templates/">manage a dashboard</a> or present regularly to a manager or non-technical colleague. The goal of these roles is to optimize existing data flows to augment subject matter expertise.</p>
<p>While this role has been represented for decades in business and other organizations, increasingly, management has been prioritizing these hires. Where an experienced manager&#39;s intuition used to reign, demonstrable proof is now the standard for important decisions. The more you can understand your organization’s motivations, the more success you will have in this role.</p>
<p>Critical thinking, technical skills which focus on managing visualizations for large datasets, and knowledge of <a target="_blank" href="http://makemeanalyst.com/basic-statistics-for-data-analysis/">basic statistics</a> are all key skills for this type of role. This is also one of the most collaborative types of data analytics roles, so diplomacy, <a href="https://hackernoon.com/the-ultimate-guide-to-strategic-thinking-for-data-scientists-g4j3uti">strategic thinking</a>, and project management skills are also beneficial.</p>
<p>Your days will be filled with these conversations and collaborations, with your priority being ownership and maintenance of the relevant core datasets which inform your department’s actions. This type of data analyst role is great for learning more about how an organization functions and deepening your specific subject matter expertise. Since it leans heavily towards a more traditional business or managerial function, these roles will follow organizational trends: expect to leave when your colleagues do (5pm), and relationships and management knowledge will be more critical to your career growth than increased technical skill.</p>
<p><strong>When you’re on a core data team</strong>, life will look very different. These teams tend to manage the data for an entire organization, and report directly to executive leadership, who likely have a forward-thinking, tech-savvy perspective.</p>
<p>Often, these teams act as consultants for the rest of the organization: gathering requirements, developing data pipelines, and serving dashboarding tools for analysts on these non-technical teams to manage. Work will likely be a mix of accommodating legacy systems, along with experiments with cutting edge technology. Your work will focus on understanding data storage, pipeline, and analysis best practices so there can be some consistency throughout the entire organization. SQL skills will be indispensable in this type of role, since you’ll be treated as an expert on specific datasets and the overall system across the organization. Being able to quickly verify a non-technical stakeholder’s ideas or concerns will make you a standout on this team.</p>
<p>If you’re in a large organization, expect to find yourself learning a lot about databases, and the challenge of uniting legacy tools with modern systems and expectations. On these teams, your key stakeholders will be: solution architects, who manage the organization’s overall data storage; systems analysts, and software engineers; along with SCRUM or other types of project managers. Since this role errs on the technical side, your career growth will largely depend on advanced skill in a specific technology (most likely SQL), and excellent people and project management skills.</p>
<p>While these two descriptions cover the experiences in most data analyst roles, there are still a few niche data analysts whose lives are even more different: <a target="_blank" href="https://hevodata.com/learn/marketing-data-analyst/#:~:text=Marketing%20Data%20Analysts%20are%20the,impact%20of%20their%20Marketing%20Strategies.">marketing data analysts</a>, <a target="_blank" href="https://jobdescriptionandresumeexamples.com/financial-data-analyst-job-description-key-duties-and-responsibilities/">financial data analysts</a>, and <a target="_blank" href="https://www.cleverism.com/job-profiles/product-analyst/">product data analysts</a>.</p>
<h2 id="most-common-tools-for-data-analysts">Most Common Tools for Data Analysts</h2>
<p>In most organizations, <a target="_blank" href="https://docs.microsoft.com/en-us/learn/certifications/mos-excel-expert-2019/">advanced Excel</a> skills will never go out of style. Regardless of how high tech or innovative your company is, a spreadsheet will never stop being a part of your workflow. It’s an easy way to share your data with non-technical peers, and do some meaningful analysis on the fly. Microsoft offers certifications for advanced Excel skills, and there are online courses to prepare for the required exam. Online courses and certifications are a great way to learn the most important and common functions of Excel.</p>
<p>Next, <a target="_blank" href="https://searchdatamanagement.techtarget.com/definition/SQL#:~:text=SQL%20(Structured%20Query%20Language">structured query language (SQL)</a>%20is,on%20the%20data%20in%20them.&amp;text=SQL%20became%20the%20de%20facto,late%201970s%20and%20early%201980s.) is the most important code you will write as a data analyst. SQL has stood the test of time, and been the core tool for data retrieval for many years. Some methods to focus on when you’re just starting to learn are SELECT, JOIN, and HAVING.</p>
<p>Ongoing reporting and visualization is key in most data-related jobs. <a target="_blank" href="https://www.tableau.com/">Tableau</a>, <a href="https://looker.com/">Looker</a>, and <a href="https://powerbi.microsoft.com/en-us/">Power BI</a> are some common tools which can be used for the most common KPIs across organizations. They all have powerful technical capabilities, but are user-friendly enough for non-technical managers. They’re listed from greatest to least in terms of price; you’ll often find Looker and Power BI in departments or organizations where budgets are smaller.</p>
<p>When data gets too big and messy, <a target="_blank" href="https://www.python.org/">Python</a> &amp; <a target="_blank" href="https://jupyter.org/">Jupyter Notebooks</a> are another tool for data analysts. We’re nearly entering data science territory with these tools - most data analyst jobs will not center this approach because it takes a high level of skill to be able to make python more valuable than the tools listed above. However, by using jupyter notebooks or google colab, you’re more able to collaborate with others, do more complex analysis, and demonstrate an entire narrative of analysis and visualization in one place. You’re likely to see these tools required for marketing data analyst roles, which tend to focus on A/B Testing.</p>
<p>While many claim there is still debate over which language is the best for data analytics, <a target="_blank" href="https://www.upgrad.com/blog/data-science-programming-languages/">Python has consistently outstripped R as the most popular language</a> both overall, and in the private sector.</p>
<p><centre><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1644846055/image1_bvenrw.png"></centre>
<a target="_blank" href="https://towardsdatascience.com/which-programming-language-should-data-scientists-learn-first-aac4d3fd3038">Ref: Which Programming Language Should Data Scientists Learn First?</a></p>
<p>However, if R seems more appealing, or you have experience with it from work in academia, you still can’t go wrong. If you’re fluent in one language, being able to program in the other is a small challenge. <strong>What’s most important to consider when choosing a programming language for data analysis is that you feel comfortable</strong> and able to dedicate yourself to it entirely. For these tools to make sense in a workplace or your career, you need strong proficiency and know-how to navigate results.</p>
<h2 id="where-you-can-learn-these-skills">Where you can Learn these Skills</h2>
<p>Data Camp is an excellent place to start.</p>
<p>Data Camp has both skill and career tracks which can guide you through all the tools and best practices necessary to break into a career in data analytics: data analyst with R, data analyst with Python, and data analyst with SQL.</p>
<p>Otherwise, here are a few other places you can start your learning journey:</p>
<ul>
<li><a target="_blank" href="https://app.datacamp.com/learn/courses/introduction-to-sql?utm_source=adwords_ppc&amp;utm_medium=cpc&amp;utm_campaignid=1565261447&amp;utm_adgroupid=63048150921&amp;utm_device=c&amp;utm_keyword=free%20sql%20courses&amp;utm_matchtype=e&amp;utm_network=g&amp;utm_adpostion=&amp;utm_creative=295185739362&amp;utm_targetid=aud-299261629574:kwd-300479289579&amp;utm_loc_interest_ms=9026841&amp;utm_loc_physical_ms=9043090&amp;gclid=CjwKCAiAz--OBhBIEiwAG1rIOq9900hmaq8oQSZO9RhJAyUUmf0pL0cYp8lorXx1lPWX9AR6bSUw8xoCnGQQAvD_BwE">SQL</a></li>
<li><a target="_blank" href="https://www.learnpython.org/">Python</a></li>
<li><a target="_blank" href="https://www.coursera.org/learn/r-programming">R</a></li>
<li><a target="_blank" href="https://www.edx.org/learn/excel">Advanced Excel</a></li>
<li><a target="_blank" href="https://www.udacity.com/course/data-visualization-in-tableau--ud1006">Tableau</a></li>
</ul>
<h2 id="other-important-skills-for-a-successful-career-as-a-data-analyst">Other Important Skills for a Successful Career as a Data Analyst</h2>
<p>Despite all the tech that comes with this career, data analytics takes much more than specialized computer skills to be successful. Being able to successfully integrate within an institution through organizational knowledge, relationship building, and business best practices will help you avoid stagnation. A few key areas include:</p>
<ul>
<li><a target="_blank" href="https://www.atlassian.com/agile/product-management">Product management</a></li>
<li><a target="_blank" href="https://www.pmi.org/about/learn-about-pmi/what-is-project-management">Project management</a></li>
<li>Collaborating with stakeholders</li>
<li>Becoming a subject matter expert (SME)</li>
<li>Business administration/organizational awareness for public, NGO, and academic work</li>
</ul>
<h2 id="resume-interviewing-data-analyst-job-search-prep">Resume &amp; Interviewing: Data Analyst Job Search Prep</h2>
<p>Here are some of the most common themes in interviews for data analyst roles:</p>
<ul>
<li>SQL skills<ul><li> Prepare to be tested on the fundamentals in many technical interviews</li>
<li>Understand JOINs, HAVING, etc.</li></ul>
</li>
<li>Tableau<ul><li> Prepare to be asked about past dashboards you’ve created - having a <a target="_blank" href="https://public.tableau.com/en-us/s/">Tableau Public</a> profile with a few examples will be sure to impress your interviewer</li>
</ul></li>
<li>Questions about your experience:<ul><li> Describe a project where you had to collaborate with non-technical stakeholders</li>
<li> Describe a situation where there was low data quality, but a stakeholder still wanted analysis completed/a decisive outcome which you couldn’t give</li>
<li>Describe a data workflow you’ve worked with in the past (tools, process, stakeholders, visualizations, etc.)</li>
</ul></li>
</ul>
<p>Like all interviews, the <a target="_blank" href="https://www.indeed.com/career-advice/interviewing/how-to-use-the-star-interview-response-technique">STAR method</a> is a solid approach to all your responses. As it is a technical role, you will have to continuously educate yourself on new tools and processes. Admitting you don’t know something, but are willing to find an answer, demonstrates an understanding of what’s required for the day-to-day work of a data analyst.</p>
<p>Many major companies hire and pay well for experienced Excel and SQL developers. These are some core technologies that will never go out of style. There are data analysts who’ve become experts in these two tools alone and built highly successful careers for themselves.</p>
<p>Regardless of your approach, be sure to list all technologies you have knowledge of or experience with, so your interviewer knows to ask you about anything relevant. Just because they are not using a specific technology now, does not mean they won’t in the near future, and vice versa. Be transparent about your skills and show them what you can do.</p>
<h2 id="building-a-career">Building a Career</h2>
<p>Choosing to build a career right now as a data analyst is a great choice. Our world is in the middle of a great AI “arms race” between major economies, and the need for data-savvy professionals is only going to grow.</p>
<p>If working in the private sector doesn’t interest you, there are plenty of other opportunities to apply your skills to something more meaningful. Here are a few examples:</p>
<ul>
<li><a target="_blank" href="https://online.hbs.edu/courses/big-data-social-good/">Data for social good</a></li>
<li><a target="_blank" href="https://datarade.ai/data-categories/esg-data">ESG/sustainability reporting</a></li>
<li><a target="_blank" href="https://www.scmp.com/yp/learn/college-uni-life/university-programmes/article/3071524/how-data-analytics-helped-obama-win">Digital media strategy used in Obama’s presidential campaign</a></li>
</ul>
<h2 id="get-started-today">Get Started Today</h2>
<p>If the idea of solving problems, collaborating with diverse people, and making a difference in any organization you’re a part of motivates you, consider becoming a data analyst.</p>
<p>From critical thinking, to database knowledge and strong people skills, you’d be investing in a high value career with endless potential. Learning these skills will take commitment and focus. New technologies are developed constantly, but focusing on the core skills mentioned in this article will ensure that you have a strong foundation and long career.</p>
<p>Our best advice is to use the resources that are available online today. There are excellent programs such as Data Camp, which are developed by experts and experienced professionals who have faced these challenges firsthand. Trust them and the learning process, and you’re sure to find your version of success. Best of luck, we look forward to seeing you in the data analyst community!</p>


https://www.datacamp.com/community/blog/blog-data-analyst-skills-for-career-success

Explore data analysis as a career and how to up-skill for the role. 

Learn about the data analyst job roles and the skills required to ace them.

Data Analyst Skills for Career Success

<p>Data processing, analysis, and visualizations were done using R and its corresponding packages. Notebook and pertinent files used can be found <a target="_blank" href="https://app.datacamp.com/workspace/w/03312344-a7b0-47cf-b97d-06125fabcbd8/edit">here</a>.</p>
<p>According to the Merriam-Webster dictionary, data is factual information used as a basis for reasoning, discussion, or calculation. By this definition, data is basically any piece of collected information that can be used and further processed and analyzed to gain insights. It is commonly associated with computers as data is normally produced and stored in a computer, yet data has been around much longer than we might imagine.</p>
<h2 id="history-of-data">History of Data</h2>
<p>The earliest example of humans storing and analyzing data dates back to 18000 BCE, when it was discovered that prehistoric humans were using tally sticks, as a means of carrying out rudimentary calculations. These Paleolithic tribespeople marked notches on sticks and bones to keep track of their activities, such as trading and monitoring supplies. By 2400 BCE, the abacus, a device used in performing calculations, was invented in Babylon.</p>
<p>Throughout history, constant development of data collection, processing, and analysis was seen through numerous writings in stone tablets, clay, papyrus, wood, and paper scrolls. Eventually, as more forms of data were discovered, the need for processing, collecting, storing, and analyzing it also evolved.</p>
<p>As human society became more advanced, so did its requirement for processing data. In the 1800s, the census started taking place in the United States. The number of data points in the census grew exponentially to the point that the U.S. Census Bureau estimated that it would take years or even decades to collect and analyze all the data in the census. This was largely problematic since compiling and analyzing the current census data would only be completed when the next census was about to start or had already started. Fortunately, a young engineer and inventor named Herman Hollerith developed the Hollerith Tabulating Machine–an electromechanical tabulating machine which reduced the time needed to collect and analyze census data from years, to just mere months. Because of this, Hollerith is regarded as the father of modern automatic computation and would later become known for founding IBM.</p>
<p>Fast forward to the 1900s, and the invention of the computer. With the advent of a more powerful computing machine comes a more sophisticated requirement for data storage. Fritz Pfleumer, a German-Austrian engineer, invented a method of storing information magnetically on tape. Some principles of his invention are still being used today for digital data storage. In this era, the term “business intelligence” gained popularity as the need for emerging software, and systems for analyzing commercial and operational performance, quickly rose.</p>
<p>The revolution of data truly changed when Tim Berners-Lee created the World Wide Web, also known as the Internet, in 1989. This led to automated information sharing between people all over the world. This means that today more data is being shared, created, and stored, leading to new ways for collecting, using, and analyzing data.</p>
<h2 id="transition-to-big-data-">Transition to “Big Data”</h2>
<p>Due to the incredible amount of internet growth in the 1990s and the steady development of personal computers and computing devices in general, the number of devices online (and consequently the amount of data created) grew quickly.</p>
<p>While the idea of Big Data had been around even before the 1990s, it was only in 2005 when Roger Mougalas officially gave it a label. He described it as “a large set of data that is almost impossible to manage and process using traditional business intelligence tools.”</p>
<p>Big Data is a term used to describe the large volume of data–both structured and unstructured–that overwhelms organizations on a day-to-day basis. It encompasses the volume of information, the velocity or speed at which it is created and collected, and the variety or scope of the data points being covered.</p>
<p>Given the size and complexity of Big Data, the process of collecting, organizing, and analyzing it to discover patterns and other useful information has become part of helping a lot of organizations to make their business decisions. In turn this has given rise to data science–an interdisciplinary field which uses scientific methods, processes, algorithms, and systems from a large volume of data, to uncover patterns and enable business leaders to draw informed insights.</p>
<h2 id="what-is-data-science-">What is Data Science?</h2>
<p>According to IBM, data science is a multidisciplinary approach to extracting actionable insights from the large and ever-increasing volumes of data collected and created by today’s organizations. The field typically requires computer science and pure science skills as data scientists apply scientific methods in their approach, and use predictive analytics and artificial intelligence to extract insights from the data.</p>
<p>Nowadays, “data science” is often used by businesses and organizations as a catch-all term for the process of handling large amounts of data, whether it is preparing, cleaning, analyzing, or visualizing the data to reveal patterns. These are just some of the data science careers one can pursue:</p>
<ol>
<li><b>Data Scientist:</b><br>
<p>Data scientists need to be able to apply mathematics, statistics, and the scientific method; use multiple tools and techniques for cleaning and preparing data; perform predictive analytics and artificial intelligence; and explain how these results can be used to provide data-driven solutions to business problems. They require much more technical skills than data analysts. For starters, you may take data science-oriented courses like these on DataCamp: <a target="_blank" href="https://www.datacamp.com/courses/intro-to-python-for-data-science">Introduction to Python for Data Science</a> and <a target="_blank" href="https://www.datacamp.com/courses/free-introduction-to-r">Introduction to R</a>.</p>
</li>
<li><b>Data Analyst:</b><br>
<p>Data analysts collect, process, and perform statistical data analyses to arrive at meaningful conclusions for organizations. They transform and manipulate large data sets into usable forms, such as reports or presentations. They also aid in the decision-making process by looking into important patterns, and gleaning insights from the data which they can then effectively communicate to organizational leaders to aid in business decisions. These courses on DataCamp may help you kick-start your data analyst career: <a target="_blank" href="https://www.datacamp.com/courses/introduction-to-sql">Introduction to SQL</a> and <a target="_blank" href="https://www.datacamp.com/courses/exploratory-data-analysis-in-sql">Exploratory Data Analysis in SQL</a>.</p>
</li>
<li><b>Data Engineer:</b><br>
<p>Data engineers are responsible for preparation, processing, and managing gathered and stored data for analytical or operational uses. Like the traditional engineers, data engineers build and maintain data “pipelines” which connect data from one system to another, making information accessible for data scientists. Because of this, data engineers are required to understand several programming languages used in data science such as Python, R, and SQL. Topics related to data engineering like these are available on DataCamp: <a target="_blank" href="https://www.datacamp.com/courses/introduction-to-data-engineering">Introduction to Data Engineering</a> and <a target="_blank" href="https://www.datacamp.com/courses/data-engineering-for-everyone">Data Engineering for Everyone</a>.</p>
</li>
<li><b>Data Architect:</b><br>
<p>Data architects mainly design and create blueprints for data management systems that data engineers then build. Analogous to the traditional architects, data architects are the “visionaries” as they are responsible for visualizing and designing an organization’s data management framework. In addition, data architects improve the performance of existing systems, making sure they are accessible to database administrators and analysts. You may take courses related to data architecture like these that are available on DataCamp: <a target="_blank" href="https://www.datacamp.com/courses/aws-cloud-concepts">AWS Cloud Concepts</a> and <a target="_blank" href="https://www.datacamp.com/courses/database-design">Database Design</a></p>
</li>
<li><b>Business Intelligence (BI) Developer:</b><br>
<p>BI developers are specialized engineers who use software tools to transform data into useful insights that help business decisions. They are responsible for simplifying technical information for everyone else in the company to easily understand. In short, they create and run reports containing data they find using business intelligence tools, and transform the information into more layman terms. Courses on using tools related to business intelligence such as Microsoft Power BI and Tableau are available on DataCamp such as these: <a target="_blank" href="https://www.datacamp.com/courses/introduction-to-power-bi">Introduction to Power BI</a> and <a target="_blank" href="https://www.datacamp.com/courses/analyzing-data-in-tableau">Analyzing Data in Tableau</a></p>
</li>
<li><b>Statistician:</b><br>
<p>Given that statistics is one of the main foundations of data science, many statisticians can easily transition into the data science field. Statisticians are mainly responsible for the collection and handling of data. They decide what data they need and how to collect it. Additionally, they design experiments, analyze and interpret the data, and report conclusions. You may check-out these introductory courses to statistics on DataCamp: <a target="_blank" href="https://www.datacamp.com/courses/introduction-to-statistical-modeling-in-r">Introduction to Statistical Modeling in R</a> and <a target="_blank" href="https://www.datacamp.com/courses/introduction-to-statistics-in-spreadsheets">Introduction to Statistics in Spreadsheets</a>.</p>
</li>
<li><b>Machine Learning Engineer:</b><br>
<p>Machine learning engineers are another group of specialized engineers who are focused on researching, building, and designing artificial intelligence (AI) and machine learning (ML) systems to automate predictive models. Basically, they develop algorithms that use input data and leverage statistical models to predict an output, while continuously updating outputs as new data becomes available. DataCamp has numerous courses related to machine learning which you may find useful such as these: <a target="_blank" href="https://www.datacamp.com/courses/machine-learning-for-everyone">Machine Learning for Everyone</a> and <a target="_blank" href="https://www.datacamp.com/courses/machine-learning-with-caret-in-r">Machine Learning with caret in R.</a></p>
</li>
</ol>
<p>A quick job search online can give you an idea as to which of the data science career paths listed above is the most in demand. The graph below shows the job openings per career path according to <a target="_blank" href="https://www.indeed.com/jobs?q=data%20scientist&amp;vjk=ecfd94ad120e8423">Indeed USA</a> as of December 8, 2021.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1641468289/image1_hntxle.png" /></center>

<p>Data architect appears to be the most in demand data science career path, due to their importance in creating data management systems which the other data science professionals then use. Next on the list is machine learning engineer, given the importance of utilizing artificial intelligence to predict outcomes in a lot of tech companies.</p>
<p>The least in demand are statisticians, mainly because a lot of the traditional statisticians are now becoming data scientists. This trend is expected to continue given how easy it is for statisticians to move from pure statistics to data science as they already possess the fundamental knowledge needed to become a fully-fledged data scientist.</p>
<h2 id="data-science-is-not-slowing-down">Data science is not slowing down</h2>
<p>The field of data science rose to immense popularity in the 2010s, especially when “Data Scientist” was touted by Harvard Business Review as the “sexiest job of the 21st century.” It became so popular that the title “Data Scientist” became a ubiquitous and trendy expression. While the term “sexiest job” does sound ridiculous at first, it mostly refers to how data scientists are not only in demand, but are also expensive to hire and difficult to retain.</p>
<p>While there is no question that data science is extremely popular nowadays, the better question is, will it remain this popular in the future? Based on employment projections, that does seem to be the case. Data from the 2020-2030 Employment Projections of the <a target="_blank" href="https://www.bls.gov/emp/tables/emp-by-detailed-occupation.htm">U.S. Bureau of Labor Statistics</a> shows that data science careers, which include statistics, data science, and other mathematical and science based occupations such as data engineering, will exhibit a very high growth rate in terms of percent change from 2020 to 2030. Statisticians ranked 14th overall, while data scientists and other mathematical science occupations ranked 31st overall out of 790 jobs included in the data.</p>
<p>While the shares of statisticians and data scientists from the total labor force are small compared to other occupations, those numbers are expected to increase in following years as the data science career path becomes more and more popular. The figure below shows how statisticians, data scientists, and other mathematical science occupations stack up against other jobs with high projected growth.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1641468289/image3_bwn60b.png" /></center>


<h2 id="a-really-expensive-field">A really expensive field</h2>
<p>One major reason why data science is popular is because it is one of the highest paying jobs in the world. The figure below shows the range of salaries for 10 different occupations–including data scientists and data analysts–in New York City. The data was obtained from <a target="_blank" href="https://teleport.org/cities/new-york/salaries/">Teleport</a>, a website which aggregates data on living conditions (e.g., salaries) in different cities.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1641468289/image2_qhdjq6.png" /></center>

<p>According to Teleport, data scientists had the 4th highest annual median salary in New York City at \$114,105, just behind C Level Executives and healthcare workers. In fact, in some other cities like Manila, data scientists rank as high as 2nd highest, just behind C Level Executives. This speaks volume as to how lucrative this job is. Data analyst jobs, whilst not as lucrative as data science positions, are still not to be slighted. At an annual median salary of $61,818, data analysts still earn around the average household income in New York City.</p>
<h1 id="ever-growing-in-the-job-market">Ever growing in the job market</h1>
<p>Another major reason why data science is popular is because of how organizations these days integrate the principles of data science in their day-to-day operations. The graph below shows the top 10 industries that involve data science in their work. The sample data of 8,000 data science oriented companies are extracted from <a target="_blank" href="https://www.diffbot.com/products/extract/testdrive/?token=210ced42a6e6d0cc4ac7f15885a94233.">Diffbot</a>, the world’s largest knowledge graph.</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1641468289/image4_spyov2.png" /></center>

<p>It comes to no surprise that Software Companies–including tech companies like Google, Apple, and Uber–came out on top. After all, the exceptional growth of big data was caused by the creation of the internet, which is closely tied to software and technology. Data science really is about processing a lot of information using various tools and technology.</p>
<p>Next on the list are financial services companies, which has been brought about by the rise of fintech companies. A portmanteau of “finance” and “technology,” fintech companies integrate technology and innovation in their services and offerings to improve their delivery to customers and disrupt traditional financial services. Since it involves handling a vast amount of data such as customer information, financial services companies saw the potential of data science tools to help streamline and optimize their processes and improve their services.</p>
<h1 id="in-conclusion-is-it-worth-pursuing-a-career-in-data-science-">In conclusion, is it worth pursuing a career in data science?</h1>
<p>With everything that has been explained above, the answer is a definite yes! As to which specific data science career path you should pursue, this depends primarily on your individual strengths and general interests. What is important is that any of the careers mentioned above are sure to be worthwhile.</p>
<p>A career path which is very lucrative and appears to show no signs of slowing down in terms of popularity, data science will continue to shape and influence how businesses and organizations operate for many years to come.</p>


https://www.datacamp.com/community/blog/blog-data-science-careers-to-pursue

Examining the incredibly popular field that is data science

Explore all the roles under the umbrella Data science.  And discover introductory courses for the same.

An Analysis of the Top 7 Trending Data Science Careers to Pursue 

<h2>Importing data and star review modification</h2><p>Weighting star reviews based on users who review more restaurants</p><h2>Generating authentic star reviews</h2><p>Modifying restaurant reviews to only the native Indian users</p>

https://www.datacamp.com/community/open-courses/r-yelp-and-the-search-for-good-indian-food

Are all Yelp restaurant reviews created equal? You will learn the techniques to “cut through the noise” and deliver useful metrics for finding a great place to eat on Friday night!

R, Yelp and the Search for Good Indian Food

<h2>Analyzing the 2013 American Community Survey</h2><p>In this first chapter you will use data from the <a href="https://www.kaggle.com/census/2013-american-community-survey" target="_blank" >2013 American Community Survey</a> to figure out whether it makes sense to pursue a PhD or not. The end result of this chapter will be <a href="https://www.kaggle.com/martijn/d/census/2013-american-community-survey/data-exploration-with-kaggle" target="_blank">your own Kaggle script</a> that you can add to your Kaggle account.</p><h2>Pigeons, Chopsticks, Spanish Silver and Marijuana</h2><p>Analyze pigeon racing data, discover the optimal size for your chopsticks, plot Spanish silver production in the 18th century and map the average price of marijuana in the different states of the US.</p>

https://www.datacamp.com/community/open-courses/data-exploration-with-kaggle-scripts

In this course, you'll begin learning the art and science of data exploration. You'll also become familiar with some of the most widely used visualization packages in data science.

Data Exploration With Kaggle Scripts

<p>In our first <a target="_blank" href="https://www.datacamp.com/community/tutorials/Introduction-to-bokeh-tutorial">Data Visualization with Bokeh</a> tutorial, you learned how to build visualizations from scratch using glyphs.  Now, you can extend this concept and build plots from different data structures such as arrays and dataframes. In this tutorial, you will learn how to plot data with NumPy arrays, dataframes in Pandas, and ColumnDataSource using Bokeh. The ColumnDataSource is the essence of Bokeh, making it possible to share data over multiple plots and widgets.</p>
<h2 id="bokeh-plots-from-numpy-arrays">Bokeh Plots from NumPy Arrays</h2>
<h3 id="creating-line-plots-from-numpy-arrays">Creating line plots from NumPy arrays</h3>
<pre><code class="lang-python"># Import packages
import numpy as np
import random
from bokeh.io import output_file, show
from bokeh.plotting import figure

# Create an array
x_array = np.array([10,20,30,40,50,60])
y_array = np.array([50,60,70,80,90,100])

# Create a line plot
plot = figure()
plot.line(x_array, y_array)

# Output plot
show(plot)
</code></pre><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1648655558/image6_wxmfuh.png"></center>

<h3 id="creating-scatter-plots-from-numpy-arrays">Creating scatter plots from NumPy arrays</h3>
<pre><code class="lang-python"># Import packages
import numpy as np
import random
from bokeh.io import output_file, show
from bokeh.plotting import figure

# Create arrays
x_arr = np.array([10,11,12,13,14,15,16])
y_arr = np.array([17,18,19,20,21,22,23])

# Create plot
plot = figure(title = &quot;Scatter plot&quot;, x_axis_label = &quot;Label name of x axis&quot;, y_axis_label =&quot;Label name of y axis&quot;)
plot.circle(x_arr,y_arr, size = 30, alpha = 0.5)

# show plot
show(plot)
</code></pre><p>This yields the following graph:  </p>

<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1648655559/image7_osekpy.png"></center>

<h2 id="creating-plots-using-pandas-dataframes">Creating Plots Using Pandas Dataframes</h2>
<h3 id="plotting-lines-with-pandas-dataframes">Plotting lines with Pandas dataframes</h3>
<p>The data in this example is obtained from <a target="_blank" href="https://www.kaggle.com/camnugent/sandp500/data">Kaggle</a>. The dataset contains all the stocks contained in the S&amp;P 500 index for five years up to December 2018. The file contains seven columns:</p>
<ul>
<li>Date - day of the week date in format yy-mm-dd</li>
<li>Open - stock’s opening price</li>
<li>High - stock’s highest price in the day</li>
<li>Low - stock’s lowest price of the day</li>
<li>Close - stock’s closing price for the day</li>
<li>Volume - volume of stocks traded in the day</li>
<li>Name - stock ticker name</li>
</ul>
<p>We want to see the trend for Google’s stock over the five-year period. First, we load the data using Pandas and then check the first five rows of the Pandas dataframe.</p>
<pre><code class="lang-python"># Import packages
import pandas as pd
# Load data
df = pd.read_csv(&#39;all_stocks_5yr.csv&#39;)
df.head()
</code></pre><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1648655559/image8_jykblu.png"></center>

<pre><code class="lang-python"># Focus on Google&#39;s stock
df_goog = df[df[&#39;Name&#39;] == &#39;GOOG&#39;]
# Convert date column to a time series
df_goog[&#39;date&#39;] = pd.to_datetime(df_goog[&#39;date&#39;])

plot = figure(title= &quot;Pandas Line Plot&quot;, x_axis_type = &#39;datetime&#39;, x_axis_label = &#39;date&#39;, y_axis_label= &#39;Prices&#39;)
plot.line(x = df_goog[&#39;date&#39;], y = df_goog[&#39;high&#39;])

# Output plot
show(plot)
</code></pre><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1648655558/image3_kxcgou.png"></center>

</br><p>In the chart, it was necessary to convert the date column to datetime to render dates along the x-axis. Additionally, the line function (glyph in Bokeh) is used to generate a time series plot for Google&#39;s high prices.</p>
<h2 id="creating-a-scatter-plot-from-pandas-dataframe">Creating a scatter plot from Pandas dataframe</h2>
<p>In this section, we use the open-source S&amp;P 500 stock data available on <a target="_blank" href="https://www.kaggle.com/camnugent/sandp500/data">Kaggle</a>. In this example, we want to see the correlation between Google&#39;s stock prices and Apple&#39;s stock prices. You can learn more about correlation <a target="_blank" href="https://www.datacamp.com/community/tutorials/tutorial-datails-on-correlation">here</a>.</p>
<pre><code class="lang-python"># Import required packages
# Import packages
import pandas as pd
from bokeh.io import output_file, show
from bokeh.plotting import figure

# Load data
df = pd.read_csv(&#39;all_stocks_5yr.csv&#39;)

# Select Google and Apple stock
df_goog = df[df[&#39;Name&#39;] == &#39;GOOG&#39;]
df_apple = df[df[&#39;Name&#39;] == &#39;AAPL&#39;]

# Select only &#39;date&#39; and &#39;high&#39; columns for Google and Apple
df_goog = df_goog[[&#39;date&#39;,&#39;high&#39;]]
df_apple = df_apple[[&#39;date&#39;,&#39;high&#39;]]
# Rename &#39;high&#39; column to &#39;high_goog&#39; and &#39;high_apple&#39;
df_goog.rename(columns={&#39;high&#39;: &#39;high_goog&#39;}, inplace=True)
df_apple.rename(columns={&#39;high&#39;: &#39;high_apple&#39;}, inplace=True)

df_merged = pd.merge(df_goog, df_apple, how=&#39;inner&#39;, on=&#39;date&#39;)
# See the first five rows
df_merged.head(5)
</code></pre><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1648655557/image2_avd26p.png"></center>

<br><p>Merging ensures that the daily prices correspond to each other based on date. This mitigates the problem of correlating prices that are from different dates, as this will yield false statistical inference.</p>
<pre><code class="lang-python"># Create scatter plot
plot = figure(title= &quot;Pandas Scatter Plot&quot;, x_axis_label = &quot;Apple high prices&quot;, y_axis_label= &quot;Google high prices&quot;)
plot.circle(x = df_merged[&#39;high_apple&#39;], y = df_merged[&#39;high_goog&#39;],color = &#39;red&#39;, size = 10, alpha = 0.8)

# Output the plot
show(plot)
<p></code></pre><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1648655558/image5_oeqmjz.png"></center></p>


<p>The circles represent the link between Apple and Google&#39;s high prices. There is a general positive correlation between Google and Apple’s stock prices.</p>
<p><h2 id="creating-bokeh-plots-from-columndatasource">Creating Bokeh Plots from Columndatasource</h2></p>
<p>The purpose of ColumnDataSource is to allow you to create different plots using the same data. In short, it allows you to build a foundation of data for calling in multiple plots and analyses. This will save you time, as you won&#39;t have to load data multiple times in Jupyter Notebook.</p>
<p>The ColumnDataSource is akin to a python dictionary, where the value of the data in a column has a key string name for that very column.</p>
<h3 id="creating-line-plots-using-columndatasource">Creating line plots using ColumnDataSource</h3>
<br><pre><code class="lang-python"># Import packages
from bokeh.io import output_file, show
from bokeh.plotting import figure, ColumnDataSource

# Create the ColumnDataSource object
df_goog[&#39;date&#39;] = pd.to_datetime(df_goog[&#39;date&#39;])
data = ColumnDataSource(df_goog)

# Create time series plot
plot = figure(x_axis_type=&#39;datetime&#39;,
              title=&#39;GOOG high prices ts plot&#39;,
             x_axis_label = &quot;Date&quot;,
              y_axis_label = &quot;Prices in $&quot;)
plot.line(x=&#39;date&#39;, y = &#39;high_goog&#39;, source = data, color = &#39;green&#39;)
show(plot)
<br><p></code></pre><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1648655557/image1_zcjhnn.png"></center></p>


<p>The plot shows us how Google’s high prices evolved over a 5-year period with the line indicating each price point of the stock.</p>
<p>To create this plot, we passed the entire Google stock dataframe into the ColumnDataSource function and saved it as a variable named data.</p>
<p>Then, we used the source argument to pass this data into the plot function. This makes it possible to use the column names directly when creating the plot instead of repeatedly calling the dataframe.</p>
<p>Finally, we used the line function to generate the green line to represent the data points.</p>
<h3 id="creating-scatter-plots-using-columndatasource">Creating scatter plots using ColumnDataSource</h3>
<p>In this code, we passed the entire Google stock dataframe into the ColumnDataSource (as in the line plot example). The dataframe is saved into the ColumnDataSource as a variable named data. We first create the line plot by directly calling the column names. Finally, we used the diamond function to generate the red diamonds to represent the individual data points.</p>
<br><pre><code class="lang-python"># Import packages
from bokeh.io import output_file, show
from bokeh.plotting import figure, ColumnDataSource

# Create the ColumnDataSource object
df_goog[&#39;date&#39;] = pd.to_datetime(df_goog[&#39;date&#39;])
data = ColumnDataSource(df_goog)

# Create time series plot
plot = figure(x_axis_type=&#39;datetime&#39;,
              title=&#39;GOOG high prices ts plot&#39;,
             x_axis_label = &quot;Date&quot;,
              y_axis_label = &quot;Prices in $&quot;)
plot.line(x=&#39;date&#39;, y = &#39;high_goog&#39;, source = data, color = &#39;green&#39;)
plot.diamond(x=&#39;date&#39;, y = &#39;high_goog&#39;, source = data, color = &#39;red&#39;)
show(plot)
</code></pre><center><p><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1648655558/image4_xpfdxn.png"></center></p>

<p>The plot shows the evolution of Google&#39;s stock over a 5-year period with the red diamonds indicating each price point of the stock.</p>
<h2 id="conclusion">Conclusion</h2>
<p>This tutorial introduced the most common data structures that you are likely to encounter in Python and how you can use them to bring your data to life. While Bokeh is not your only option for Python visualization, it distinguishes itself from Matplotlib or Seaborn by its ease of use and interactive capabilities.  In our next tutorial, you will gain hands-on experience in using layouts for creating effective and interactive visualizations. If you’d like to do a deep dive into all the data visualization possibilities offered by Bokeh, check out DataCamp’s  <a target="_blank" href="https://www.datacamp.com/courses/interactive-data-visualization-with-bokeh">Interactive Data Visualization with Bokeh</a> course.</p>


https://www.datacamp.com/community/tutorials/tutorial-data-visualization-with-bokeh

Learn basic visualizations of various data structures using Bokeh.

Data Visualization with Bokeh Tutorial: Plotting Data Structures 

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Data cleaning is a very basic building block of data science. Learn the importance of data cleaning and how to use python and carry out the process.

Data cleaning in python and the methods involved to get good data. 

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