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By Kaya Ismail

Digital marketing has become a necessity for any business attempting to survive in this day and age. It’s been said that the most important thing is not the amount of money you spend on digital marketing but the way you spend it. What does this mean? Well, certain categories within digital marketing will give you more benefits than others, depending on your company’s size and goals.

Digital Marketing is constantly changing, but there are some foundational categories of digital marketing you need to have a strong grasp of to thrive in the digital ecosystem. To learn more about the top digital marketing disciplines and the software tools for digital marketing. we asked the experts and here’s what they shared.

Top Categories for Digital Marketing

If there is a universal truth in digital marketing, every marketing executive has their own ideas of how marketing should be done and even how many marketing categories there are. Working in such a rapidly evolving industry means that new marketing categories can crop up all the time.

Even in the past ten years, there has been a huge jump in how digital marketing is understood. However, like Kate Adams, SVP of Marketing at Boston, MA-based Validity, said, “while marketing categories aid in creating brand awareness, recognition and trust, what a lot of marketers don’t realize is that the success of their digital campaigns is highly contingent on the health of their data.”

To understand more about each category, let’s take a closer look at some of the most popular digital marketing categories.

SEO

For some marketers, SEO is the pillar of their campaigns. This is because SEO is applicable to the other digital marketing categories. For instance, you’d have to use relevant SEO keywords in your drip campaigns and content marketing to make them effective and engaging, which is why SEO and research are fundamental for digital marketing.

SEO Tools

“When it comes to doing SEO, our go-to tools would be SEMrush and Ahrefs. We find using SEMrush helpful as it allows us to quantify our website’s estimated reach and also determine our site’s domain and/or resource authority,” shares Maya Levi, Marketing Manager at Tel Aviv, Israel-based ReturnGO.

SEM

Search engine marketing (SEM) refers to the practice of leveraging paid advertising that appears on the search engine results pages (or SERPs). In search engine marketing, companies place bids on keywords that Google visitors might use when looking for certain products or services, which gives the company the opportunity for their ads to appear alongside results for those search queries.

SEM Tools

According to Christopher Moore Chief Marketing Officer at Mooresville, NC.-based Quiet Light, “The best tool for pay-per-click advertising is Google Ads Editor as it allows you to create and edit different ad campaigns across different Google accounts making it far easier to manage your various campaigns and edit ads as the campaign goes along to make them more SEO friendly.”

Content Marketing

Content can take many forms, from blog posts to voice instructions delivered through IoT devices. Since it can takes many forms, it is often seen as the lifeblood of marketing campaigns. Due to its flexibility, content marketing has a central and all-encompassing role in every marketing strategy and can be tailored to fit customer needs before, during and after the buying process.

Content Marketing Tools

There are many content tools out there to help improve your content marketing. They can help build, grammar, content suggestions and SEO best practices. Examples include Grammarly, Ink and Jarvis.ai. Most of these are artificial intelligence-based apps that helps marketers overcome writer’s block and create content more consistently across niches, and ensure SEO is baked-in to the process.

Email Marketing

Getting into inboxes and engaging recipients through email marketing has become more challenging than ever before, with inbox volume nearly doubling year-over-year. Email marketing tools can help simplify email marketing campaigns and provide crucial insights to help increase engagement and improve execution.

Email Marketing Tools

A good piece of advice is that you should always start with an ESP (email service provider) that fits your budget and your brand. “Flodesk is a great option for paid with no tiered plans, but if you want to start with free, MailerLite is another popular one with a generous amount of free subscribers before you need to upgrade your plan,” shares Abby Sherman, Director of Strategy at Minneapolis, MN.-based Snap Agency.

Data Analytics

Without data, marketing is nothing but guesswork. On the other hand, the inappropriate use of data can definitely cripple even the best-laid plans. “Marketing and sales teams waste up to 50% of their time dealing with data quality issues,” confesses Adams. “From duplicate records to outdated contact information. If companies aren’t reaching the right audience, their marketing efforts (and money) are going to waste,” she continued.

Data Analytics Tools

“To gather data and insights, we supplement our usage of SEMrush with Google Analytics (GA),” said Levi. “Google Analytics is easy to set up, and it allows us to stay on top of our social networking profiles and website’s performances. Through the data that we get from GA, we can resolve marketing roadblocks that we encounter along the way strategically,” she shared.

All in all, to make it easier for you to meet the demands of digital marketing, it’s best if you choose and invest in the right software that will make the research, execution, and optimization of these efforts much easier for you. Some of these helpful apps include CRM tools, automation software, and collaboration tools that, with the help of personalization and a human touch, will help you create relevant and effective marketing campaigns.

Feature Image Credit: Adobe

By Kaya Ismail

Sourced from CMS WiRE

By

Snap sees augmented reality at the intersection of customer experience, ads, data and commerce. The big question is whether we need smart glasses en masse to make it happen.

Snap is hellbent on the idea that it can make augmented reality profitable and a commerce platform. Perhaps it has a point.

At Snap’s investor day on Tuesday, the company outlined an upbeat outlook with “sustained revenue growth of about 50% for several years assuming favourable economic conditions.” Snap also said it will invest in Discover to drive engagement, Spotlight to expand premium inventory supply and augmented reality as an advertising tool. Snap Map will be a small business ad platform.

Evan Spiegel, CEO of Snap, said:

Our strategy is to take product innovations like augmented reality lenses and evolve them into platforms by building tools for creators and developers and providing distribution for their creations to reach the Snapchat community. We’ve laid a foundation for this to happen more broadly by organizing our platforms into 5 main screens of our application, Camera, Map, Chat, Stories and Spotlight.

Spiegel said that Snap has invested heavily in augmented reality and will be doubling down on the strategy in 2021.

Also: As Snapchat use soars during pandemic, infrastructure costs also climb

“Augmented reality has evolved from something fun and entertaining into a real utility. Our camera can solve math equations, scan wine labels to find ratings, reviews, and prices, tell you the name of the song you’re listening to and so much more,” said Spiegel.

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Snap also has enabled more than 200 beauty brands to upload thousands of SKUs to its camera.

In other words, it’s early days for augmented reality to meet advertising, but chances are good Snap gets there first. After all, Snap has 35 million businesses on its Snap Map. The combination of commerce, location, and augmented reality could be promising.

The big questions revolve around whether it’s truly primetime for augmented reality as a commerce and advertising platform and whether Snap can lead. Augmented reality, along with its cousin virtual reality, has a place in the enterprise for training, remote maintenance, and knowledge transfer. There is a real return on investment.

Front-facing commerce and consumer applications by verticals such as retail remain an augmented reality work in progress.

Here are the key questions:

Do we need more wearable devices to make augmented reality fly? Snap started with a plan to offer glasses but now rides along with smartphones. Those screens can be limited. Snap CTO Robert Murphy noted:

As powerful and portable as modern computing is, we are constrained in how we engage with it. Hunched over with our fingers tapping and swiping on small screens. Advances in technology will change this, overlaying digital experiences directly in our field of view and empowering us to engage with computing the same way we do as humans, with our heads up looking out at the world in front of us. Over time, the gap will close between what we are able to see through a screen and what we’re able to imagine ourselves and with others. Our ability as humans to transmit ideas will improve dramatically with information and entertainment directly in our line of sight.

Our goal as a company is to accelerate the path to this future by building on what is possible today. This requires that we reimagine the role of the camera. Historically, cameras were used for documenting moments, capturing a scene exactly as it is for the purpose of viewing it later in time. Now through developments in hardware and software, we can do a lot more than just capture a scene. We can understand, interpret, edit and augment a scene, and not just for later, we’re increasingly able to do all of this in real time. This is the camera that will enable the next-generation of computing. And that’s why we are a camera company.

Does Snap have the scale to make augmented reality a mainstream option? In a word: Yes. Snapchat is used by 265 million people daily and that audience creates 5 billion Snaps. These users have captions and lens. It’s just a matter of time before data and commerce follow.

Murphy said:

Our augmented reality platform is driven by 3 major efforts: one, innovating in technology to unlock new capabilities in the camera; two, exploring creatively to design exciting and informative experiences; and three, supporting a growing community of AR consumers and creators. We’re investing heavily in each of these with incredibly talented technical and creative teams in which scientists, engineers, designers and product and community thinkers are working together to invent the future.

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What augmented reality data overlays can drive monetization? Murphy said the ability to use neural rendering to change faces could have implications for fashion and beauty. Understanding facial expressions could also have a role. Landmarkers can drive brick-and-mortar commerce. Murphy said:

Neural rendering will lead to even more realistic visual transformation, enabling real time, high-quality special effects. Landmarkers and local lenses are the precursor to large-scale robust 3D mapping, which will someday allow anyone, anywhere to engage with AR connected to any physical space. And scan is the starting point to bring our vast growing library of AR experiences, not to your fingertips but immediately into your line of sight.

Are augmented reality glasses necessary? Snap is planning for the day and it may advance its own hardware or leverage other vendors (think Apple AR glasses). Murphy said:

We are extremely optimistic about all the growing momentum in AR for smartphones. It’s a starting point to imagine AR beyond the phone. To fully realize this idea of computing overlay directly on to the world will require a new device. A completely new kind of camera that is capable of rendering digital content rights in front of us, put the power to instantly and continuously understand the world as our own eyes do, and all in a light wearable form factor.

Spectacles is our investment in this future. It’s an opportunity to design and develop a device specifically for augmented reality. We’re doing this incrementally by building and releasing increasingly more capable devices that are connected to the Snap platform. Over time, the same lenses that we’re starting to see on today smartphones, lens that can help you shop new outfits, see your favorite characters come to life or learn new things about the world, will be able to be experienced in full immersive 3D.

Will AR be an advertising platform? Snap certainly sees AR as part of its ever-evolving ad stack. Peter Sellis, senior director of product at Snap, said:

Our team will focus next on the camera via AR advertising. We’re going to do this by first, building the core behavior of AR as a utility; then second, making it easier for brands to create and experiment; and then third, we’ll pair it seamlessly with our powerful advertising platform.

We are investing in building new experiences for specific verticals where we believe AR can clearly augment the customer journey and provide value to businesses. We’re going to start with shopping. We’ve already partnered with several leading brands to leverage our technology for virtual try on experiences. Through our recent beta program with over 30 brands across verticals from beauty to auto,

Snapchatters tried on products over 250 million times. These same Snapchatters were 2.4x more likely to click to purchase an average. Next, we’re making it easier for businesses to create, publish and share lenses with millions of Snapchatters.

Can AR attract the big ad budgets? Jeremi Gorman, the chief business officer at Snap, said:

Over the next few years, we believe our AR capabilities will become the next industry standard for mobile native advertising. We have already partnered with several leading brands to leverage our AR and ML technologies to power virtual storefronts and try on experiences such as Champs, Clearly, Dior, Essie, Kohl’s, Levi’s, Jordan Brand, Sally Hansen and Gucci, just to name a few.

The challenge with AR, which is different from our existing video ads business is that we’re still in the early stages of development of the AR industry in its entirety.

They are not often existing augmented reality budgets that these large agencies are within the brand. However, I’ve been in this industry a long time. And I remember when there weren’t distinct mobile budgets, video budgets, social budgets or e-commerce budgets either, but here we are in a place where those are core disciplines that each brand and each agency, so too will be augmented reality.

Add it up and Snap is seeing AR blend with a direct response to deliver real returns with a strategy to target key verticals. The biggest wild card will be timing.

 

 

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Sourced from ZDNet

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Although 86% of marketers feel they are adequately trained and skilled, nearly all report that they want a new skill in order to advance their careers. The most frequently reported skills are data analytics, performance marketing, social media, and SEO.

Sidecar surveyed 146 marketing professionals in the retail industry. The majority of respondents were based in the U.S., with the remainder in Canada. All reported that they contribute to ecommerce marketing efforts at their company.

  • C-Level executives want skills in data analytics, social media, and performance marketing.
  • SEO directors or vice presidents want data analytics, performance marketing, and leadership skills.
  • Associated and managers want data analytics, SEO, social media, and performance marketing.

Job titles including associate, manager, director, vice president, chief marketing officer (CMO), and chief executive officer (CEO). The analysis groups these titles into associates and managers, directors and vice presidents, and C-level. Responses were fielded between September and October 2020.

Some responses were not discrete skills marketers want, but rather strategic knowledge and big-picture capabilities they hope to acquire. One CEO cited the ability to create the perfect balance between digital marketing spend and great content. A director asked for strategic thinking on how to lead a brand through the changing environment.

The top five functions that have had the greatest focus in hiring during the past 12 months include social media, content marketing, SEO, email marketing, and graphic design.

This differs from the functions that marketing professionals plan to hire for during the next 12 months. Social media marketing tops the list, followed by email marketing, content marketing, digital strategy, data analytics, and graphic design.

Survey participants were asked what platforms they would like to spend more time on. Some 42% cited Google paid search, while 41% cited Facebook, 40%, Amazon; 40%, Instagram; 37%, Google Shopping; 32%, Pinterest; 20%, TikTok; 15%, Snapchat; 13%, Walmart; and 10% cited Microsoft.

Participants in the survey were asked which tasks they want to devote more time to. Brand building and data analysis were tied for the top response, with about 45% saying they want more time to do each, followed by 43% who cited competitive analysis, while 36% cited customer experience; 34% cited creative; 33% cited multichannel strategy; 32% cited customer shopping trends; 32% cited marketing attribution; 20% cited more time to devote to improving their company’s mobile experience; and 14%, more time to set goals.

  • C-Level executives cited that they want more time for brand building
  • Directors and VP levels want more time for brand building
  • Associate directors and managers want more time for competitive and data analysis.

Marketers at small businesses want more time for data analysis, creative, brand building, multichannel marketing, and customer experience.

When asked to cite the number one goal for the company’s marketing team rather than an individual goal, 38% of marketers cited the acquisition of new customers, while 29% cited driving profitability; 9% cited increasing customer lifetime value; 9% cited retaining existing customers; 6% cited growing brand awareness; 3% cited growing website traffic; 3% cited SEO; 2% cited developing quality content; and 1% cited improving the customer experience.

When asked to cite the top challenges for this year, (multiple choice) 51% of respondents cited limited time, followed by 40% who cited limited budget, while 32% cited competing priorities, 26% cited brand recognition, 24% cited achieving scale, and 23% cited manual processes, among many more such as competition, lack of skills in-house, lack of data-driven decisions, insufficient marketing attribution, and lack of collaboration.

By

Sourced from MediaPost

The COVID-19 pandemic has turned out to be one of the most significant disruptive events witnessed by this generation. From mainstreaming remote working, cutting global travel to a comprehensive digital shift, the outbreak has changed the way businesses are executed.

One of the most notable elements of this transformation is the way organisations have been forced to embrace digital marketing to be able to survive the crisis and transform the way they attract and engage customers and clients.

As people are forced to stay indoors, there has been a shift to a space where businesses and customers interact less physically and more through the online route. There has been a surge in organisations seeking to create new websites or update existing ones, creating elaborate social media campaigns and launching new e-commerce channels. Intelligent content creation and SEO (Search Engine Optimisation) are other elements that are receiving fresh focus. Organisations that embrace this transformation quickly and more comprehensively are the ones that are more likely to survive. Here are some phases of entrepreneurship that are adjusting to the “new normal”:

The age of webinars: As live conferences and face-to-face activities take a back seat, organisations are working out new ways to engage with customers. Webinars have emerged as a popular way to achieve digital thought-leadership and getting quality leads. At the same time, customer engagement is also taking place within these digital discussions. That’s why there is a sea of webinars to spread the message. Even when the crisis ebbs, people are likely to continue to conduct part of their thought-leadership events through webinars, as they serve the same purpose at a fraction of the cost. Webinars have filled the gap of traditional conferences and are likely to become a mainstream marketing strategy.

Increased usage of data analytics: As organisations increase their digital presence, the importance of creating useful databases has increased. With people spending more time on social media, their chances of seeing advertisements on such platforms or coming in touch with content marketing blogs are greater. This is why organisations now need to create valuable databases, analyse them and use this information intelligently to reach out to the target audience. Tracking the pattern of consumer behaviour, online traffic patterns, analysing which content retains the customer and getting a break up of which products are enticing what type of customers are essential elements of data analytics that organisations need to use to boost their online sales.

Content is the king: Businesses must focus on expanding their social media presence by creating intelligent and attractive content. With the shift from outbound to inbound marketing, it becomes essential to engage consumers in subjects they might find interesting. However, content distributed on social media should not be only promotional in nature; it must be knowledge and awareness-based as well. It must engage consumers emotionally through human interest stories rather than blatantly promoting the product.

By Narendra Shyamsukha.

Sourced from THE HINDU

By Isaac Sacolick,

A brief guide to the analytics lifecycle, the expanding array of tools and technologies, and selecting the right data platform

Whether you have responsibilities in software development, devops, systems, clouds, test automation, site reliability, leading scrum teams, infosec, or other information technology areas, you’ll have increasing opportunities and requirements to work with data, analytics, and machine learning.

Your exposure to analytics may come through IT data, such as developing metrics and insights from agile, devops, or website metrics. There’s no better way to learn the basic skills and tools around data, analytics, and machine learning than to apply them to data that you know and that you can mine for insights to drive actions.

Things get a little bit more complex once you branch out of the world of IT data and provide services to data scientist teams, citizen data scientists, and other business analysts performing data visualisations, analytics, and machine learning.

First, data has to be loaded and cleansed. Then, depending on the volume, variety, and velocity of the data, you’re likely to encounter multiple back-end databases and cloud data technologies.

Lastly, over the last several years, what used to be a choice between business intelligence and data visualisation tools has ballooned into a complex matrix of full-lifecycle analytics and machine learning platforms.

The importance of analytics and machine learning increases IT’s responsibilities in several areas. For example IT often provides services around all the data integrations, back-end databases, and analytics platforms.

Furthermore, devops teams often deploy and scale the data infrastructure to enable experimenting on machine learning models and then support production data processing, while network operations teams establish secure connections between SaaS analytics tools, multi-clouds, and data centres.

In addition, IT service management teams respond to data and analytics service requests and incidents; infosec oversees data security governance and implementations and developers integrate analytics and machine learning models into applications.

Given the explosion of analytics, cloud data platforms, and machine learning capabilities, here is a primer to better understand the analytics lifecycle, from data integration and cleaning, to dataops and modelops, to the databases, data platforms, and analytics offerings themselves.

Analytics begins with data integration and data cleaning

Before analysts, citizen data scientists, or data science teams can perform analytics, the required data sources must be accessible to them in their data visualisation and analytics platforms.

To start, there may be business requirements to integrate data from multiple enterprise systems,extract data from SaaS applications, or stream data from IoT sensors and other real-time data sources.

These are all the steps to collect, load, and integrate data for analytics and machine learning. Depending on the complexity of the data and data quality issues, there are opportunities to get involved in dataopsdata catalogingmaster data management, and other data governance initiatives.

We all know the phrase, “garbage in, garbage out.” Analysts must be concerned about the quality of their data, and data scientists must be concerned about biases in their machine learning models.

Also, the timeliness of integrating new data is critical for businesses looking to become more real-time data-driven. For these reasons, the pipelines that load and process data are critically important in analytics and machine learning.

Databases and data platforms for all types of data management challenges

Loading and processing data is a necessary first step, but then things get more complicated when selecting optimal databases. Today’s choices include enterprise data warehouses, data lakes, big data processing platforms, and specialised NoSQL, graph, key-value, document, and columnar databases.

To support large-scale data warehousing and analytics, there are platforms like Snowflake, Redshift, BigQuery, Vertica, and Greenplum. Lastly, there are the big data platforms, including Spark and Hadoop.

Large enterprises are likely to have multiple data repositories and to use cloud data platforms like Cloudera Data Platform or MapR Data Platform, or data orchestration platforms like InfoWorks DataFoundy, to make all of those repositories accessible for analytics.

The major public clouds, including AWS, GCP, and Azure, all have data management platforms and services to sift through.

For example, Azure Synapse Analytics is Microsoft’s SQL data warehouse in the cloud, while Azure Cosmos DB provides interfaces to many NoSQL data stores, including Cassandra (columnar data), MongoDB (key-value and document data), and Gremlin (graph data).

Data lakes are popular loading docks to centralise unstructured data for quick analysis, and one can pick from Azure Data Lake, Amazon S3, or Google Cloud Storage to serve that purpose. For processing big data, the AWS, GCP, and Azure clouds all have Spark and Hadoop offerings as well.

Analytics platforms target machine learning and collaboration

With data loaded, cleansed, and stored, data scientists and analysts can begin performing analytics and machine learning. Organisations have many options depending on the types of analytics, the skills of the analytics team performing the work, and the structure of the underlying data.

Analytics can be performed in self-service data visualisation tools such as Tableauand Microsoft Power BI. Both of these tools target citizen data scientists and expose visualisations, calculations, and basic analytics.

These tools support basic data integration and data restructuring, but more complex data wrangling often happens before the analytics steps. Tableau Data Prep and Azure Data Factory are the companion tools to help integrate and transform data.

Analytics teams that want to automate more than just data integration and prep can look to platforms like Alteryx Analytics Process Automation. This end-to-end, collaborative platform connects developers, analysts, citizen data scientists, and data scientists with workflow automation and self-service data processing, analytics, and machine learning processing capabilities.

Alan Jacobson, chief analytics and data officer at Alteryx, explains, “The emergence of analytic process automation (APA) as a category underscores a new expectation for every worker in an organisation to be a data worker. IT developers are no exception, and the extensibility of the Alteryx APA Platform is especially useful for these knowledge workers.”

There are several tools and platforms targeting data scientists that aim to make them more productive with technologies like Python and R while simplifying many of the operational and infrastructure steps. For example, Databricks is a data science operational platform that enables deploying algorithms to Apache Spark and TensorFlow, while self-managing the computing clusters on the AWS or Azure cloud.

Now some platforms like SAS Viya combine data preparation, analytics, forecasting, machine learning, text analytics, and machine learning model management into a single modelops platform. SAS is operationalising analytics and targets data scientists, business analysts, developers, and executives with an end-to-end collaborative platform.

David Duling, director of decision management research and development at SAS, says, “We see modelops as the practice of creating a repeatable, auditable pipeline of operations for deploying all analytics, including AI and ML models, into operational systems.

“As part of modelops, we can use modern devops practices for code management, testing, and monitoring. This helps improve the frequency and reliability of model deployment, which in turn enhances the agility of business processes built on these models.”

Dataiku is another platform that strives to bring data prep, analytics, and machine learning to growing data science teams and their collaborators. Dataiku has a visual programming model to enable collaboration and code notebooks for more advanced SQL and Python developers.

Other analytics and machine learning platforms from leading enterprise software vendors aim to bring analytics capabilities to data centre and cloud data sources. For example, Oracle Analytics Cloud and SAP Analytics Cloud both aim to centralise intelligence and automate insights to enable end-to-end decisions.

Choosing a data analytics platform

Selecting data integration, warehousing, and analytics tools used to be more straightforward before the rise of big data, machine learning, and data governance.

Today, there’s a blending of terminology, platform capabilities, operational requirements, governance needs, and targeted user personas that make selecting platforms more complex, especially since many vendors support multiple usage paradigms.

Businesses differ in analytics requirements and needs but should seek new platforms from the vantage point of what is already in place. For example:

  • Companies that have had success with citizen data science programs and that already have data visualisation tools in place may want to extend this program with analytics process automation or data prep technologies
  • Enterprises that want a toolchain that enables data scientists working in different parts of the business may consider end-to-end analytics platforms with modelops capabilities
  • Organisations with multiple, disparate back-end data platforms may benefit from cloud data platforms to catalog and centrally manage them
  • Companies standardising all or most data capabilities on a single public cloud vendor ought to investigate the data integration, data management, and data analytics platforms offered

With analytics and machine learning becoming an important core competency, technologists should consider deepening their understanding of the available platforms and their capabilities. The power and value of analytics platforms will only increase, as will their influence throughout the enterprise.

Feature Image Credit: Dreamstime

By Isaac Sacolick,

Sourced from ARN from IDG

This is probably the most common question I got asked beside “How did you land your job in Data Science/ Data Analytics?” I will write another blog on my job hunting journey, so this will focus on how to get the industry exposure without that gig yet.

I gave a talk on this topic before at DIPD @ UCLAthe student organization dedicated to increasing diversity and inclusion in the fields of Product and Data that I co-founded. However, I aim to expand this topic and make it accessible to a broader audience.

And there it goes, I hope this post will potentially inspire more and more data enthusiasts to start their own blogs.

This may be a tough time for many of us, but it’s also a prime time to turbocharge and level up your skill sets in data science and analytics. If your employment got impacted at this time, treat the unfortunate as a great opportunity to take a break, reflect and kickstart your personal project — things that are luxurious when time does not allow.

“When one door closes, another opens” — Alexander Graham Bell

Hardship does not determine who you are, it’s your attitude and perseverance that define your values. Let’s get right into it!

Where to start?

Photo by Carl Heyerdahl via Unsplash

Start small and scale up

Before we start any project, first narrowing down your interests. This is your personal project so you will have full autonomy over it. Find something that makes you tick and gets you motivated to devote your time!

There will be a lot of challenges along the way that may discourage or sidetrack you from accomplishing the project, the thing that keeps you going should be the analysis topic that strongly aligns with your interest. It does not have to be something out of the world. Ask yourself what is important to you and why should we care about it.

When I first started, I knew that I wholeheartedly care about mental health and the ways to gain more mindfulness. So I dug deeper into analyzing the top 6 guided meditation apps to understand which one will be most suitable for my preferences.

Getting inspirations

Photo by Road Trip with Raj via Unsplash

Read, read, and read!

One of the most important key factors that I learned through my research assistant position at CRESST UCLA is to balance the workload between analysis and literature review. What this means is that we need to find what has been done in the past and figure out which additions or unique aspects you can contribute on top of the findings. My reading sources vary from Medium, Analytic Vidhya, statistics books to any relevant sources that I can find on the internet.

Take my Subtle Couple Traits analysis for example. There has been some work done in the space of music taste analysis via Spotify API, but no one has really delved into movies yet. So I took this chance and discovered the intersection of our couple’s cult favorites for music and movies.

Finding the right toolbox

Photo by Giang Nguyen via MinfulR on Medium

Now you get to this step where you need to figure out which data to collect and find the right tools for the job. This part has always resonated intrinsically with my industry experience as a data analyst. It’s the most challenging and time-consuming part indeed.

My best tip for this stage of analysis is to ask a lot of practical questions and come up with some hypotheses that you need to answer or justify through data. We have to also be mindful of the feasibility of the project, otherwise, you can be more flexible in terms of tweaking your approach towards a more doable one.

Note that you can use the programming language that you are most comfortable with 🙂 I believe that either Python or R has its own advantages and great supporting data packages.

An example from my past project can crystalize this strategy. I was curious about the non-pharmaceutical factors that correlate to the suppression of COVID-19 so I listed out all of the variables I can think of such as weather, PPEs, ICU beds, quarantines, etc. then I began massive research on the open-source data sets.

“All models are wrong, but some are useful” — George Box

Since I did not have a background in public health, building predictive models for this type of pandemic data was a huge challenge. I first started with some models I’m familiar with such as random forest or Bayesian ridge regression. However, I discovered that pandemic typically follows the trend of a logistic curve in which the cases grow exponentially over a period of time until it hits the inflection point and levels out. This refers to the compartmental models in epidemiology. It took me almost 2 weeks to learn and apply this model to my analysis but the result was extremely mesmerizing. And I eventually wrote a blog about it.

The process

If you are working in the Data Science/Analytics field, this is not new to you — “80% of a data scientist’s time consists of preparing (simply finding, cleansing, and organizing data), leaving only 20% to build models and perform analysis.”

Photo by Impulse Creative

The process of cleaning data may be cumbersome, but when you get it right, your analysis will be more valuable and significant. Here’s the typical process I take for my analysis workflow:

1) Collecting Data

2) Cleaning Data

Many more…

3) Project-based techniques

  • (NLP) Sentimental analysis, POS, topic modeling, BERT, etc.
  • (Predictions) Classification/Regression model
  • (Recommendation System) Collaborative Filtering, etc.

Many more…

4) Write up insights and recommendations

Connecting the dots

This is the most important part of the analysis. How do we connect the analysis insights into a real-life context and making actionable recommendations? Regardless of your project’s focus, whether it’s about machine learning, deep learning or analytics, what problem is your analysis/model trying to solve?

Photo by Quickmeme

Imagining that we build a highly complex model to predict how many Medium readers will clap for your blog. Okay, so how’s this important?

Link it to potential impacts! If your post receives more endorsement from claps, it may get curated and featured more often on Medium platform. And if more paying Medium readers find your blog, you can probably earn more money through the Medium Partner Program. Now that’s an impact!

However, it’s not always about profit-driven impact, it could be social, health, or even environmental impact. This is just one example of how you can make the connections between technical concepts with real-world implementation.

Roadblocks

You may hit a wall at some points during the journey. My best piece of advice is to proactively seek help!

Besides from reaching out to friends, colleagues, or mentors to ask for advice, I often found it helpful to search or post questions on online Q&A platforms like Stack Overflow, StackExchange, Github, Quora, Medium, you name it! While seeking for solutions, be patient and creative. If the online solutions have not yet solved your problems, try to think of another way to customize the solution for the characteristics of your data or the version of the code.

The art of writing is rewriting.

When I first published my first data blog to Medium, I found myself re-visiting my post and fixing some sentences or wording here and there. Don’t be discouraged if you notice some typos or grammar mistakes after releasing it, you can always go back and edit!

Since it is our personal project, there’s no obligation on whether you must finish it. Hence, prioritization and disciplines play a crucial role throughout the journey. Set a clear goal for your project and lay out a timeline to achieve it. At the same time, don’t spread yourself too thin since it may cause you to lose interest.

Understand your timeline and capacity! I often push my personal project in a sprint of 2 to 4 weeks to finish during break or the weekends. In order to organize your sprint and track your progress, you can refer to some Agile framework that can be found through collaboration software like Trello or Asana. As long as you make progress even the smallest one, your success shall flourish some day. So keep going and don’t give up!

Closing Remarks

The first step is always the hardest. If you don’t think that the project is ready yet, give yourself some time to fine-tune and share it!

Nothing will be perfect at first. But by shipping it to the audiences, you would know what to improve for later projects — I adopted this principle wholeheartedly from product management perspectives.

I used to be not good at communicating my thoughts structurally and clearly (which I’m still trying to improve), but by pushing myself out of the comfort zone, I have gone extra miles from where I was. I hope this will, to some degree, inspire you to start your first data blog. Believe in yourself, be brave and reach out to me or anyone in your network if you need help along the way!

“Faith is taking the first step even when you don’t see the whole staircase.” — Martin Luther King

Photo by Glen McCallum via Unsplash

By Giang Nguyen

Sourced from towards data science

By

Developing a holistic data strategy

Enterprises of all sizes, all over the world, have now recognized that data is an integral part of their business that cannot be ignored. While each enterprise may be at a different stage of their personal data journey – be it reducing operational costs or pursuing more sophisticated end goals, such as enhancing the customer experience – there is simply no turning back from this path.

In fact, businesses are at the stage where data has the power to define and drive their organisations overall strategy. The findings from a recent study by Infosys revealed that more than eighty-five percent of organisations globally have an enterprise-wide data analytics strategy already in place.

This high percentage is not surprising. However, the story does not end with just having a strategy. There are numerous other angles that enterprises must consider and act on before we can deem a data journey as successful.

Developing a data strategy

First, enterprises need a calculated strategy which covers multiple facets. Second, the real life implementation of the strategy must be seamlessly carried out – and this is where the challenge lies for all enterprises.

Consider having to create a comprehensive and effective strategy for your company. Data strategy is no longer about simply identifying key metrics and KPIs, developing management roles or creating operational reports, or working on technology upgrades. Rather, its reach extends to pretty much all corners of the business.

In short, data strategy is so tightly integrated with business today, that it is in the driver’s seat, which is a momentous shift from more traditional approaches of the past.

What are the characteristics of a good, strong data strategy?

Creating a good, strong data strategy begins with ensuring complete alignment with the organisation strategy. The data strategy must be closely aligned to the organisational goal, be it around driving growth or increasing profitability or managing risk or transforming business models.

Not only that, but the data strategy must be nimble and flexible, allowing periodic reviews and updates to keep pace with wider changes in the business and market. The data strategy should be able to drive innovation, creating a faster, better and more scalable approach.

A strong data strategy must be built in a bi-directional manner so that it can enable tracking of current performance using business intelligence to provide helpful pointers for the future. This approach is only possible if organisations choose to adopt a multi-pronged data strategy that encompasses people, technology, governance, security and compliance. Importantly, organisations must also choose to adopt an appropriate operating model.

Taking a holistic approach to data

A holistic approach includes developing a defined vision, having a clear structure around the team and factoring in the current skill set of the team. This is in addition to considering what the enterprise can reasonably anticipate in the future and identifying mechanisms to successfully drive the change across the organisation.

The technology component involves having a distinct vision, assessing the existing solution landscape, all the while being cognizant of the latest technological trends and arriving at a path that fits well with overall organisational goals and the technology vision.

Governance, security, and compliance are other critical aspects of a good data strategy. Integrity, hygiene and ownership of data, plus relevant analytics on the data to determine the Return On Investment on data strategy, are all essential steps which cannot be forgotten. We cannot overstate the importance of security.

Adherence to compliance has assumed significance with various regulations in play all over the world, such as GDPR in Europe and new data privacy laws in California and Brazil for example.

In essence, the data strategy must define a value framework and have a reliable mechanism to track the returns to justify the investments made. About fifty percent of respondents to our survey agreed that having a clear strategy chalked out in advance is essential to ensuring an execution that is effective in practice and goes off without any hiccups.

Identifying the best strategy is essentially pointless if the execution falters

Many obstacles have the power to prevent the flawless execution of a data strategy. Copious challenges in the technology arena can arise in various forms, for example: having the knowledge to choose the right analytics tools, lack of availability of people with the right skill set, upskilling, reskilling and training the workforce with the necessary skills for the world of tomorrow and so on. Most of the challenges articulated by respondents to the Infosys survey arose in the execution phase of a data strategy.

While these challenges may appear daunting in the first instance, they can be addressed with careful planning and preparation. Being prepared and equipped for multiple geographies, multiple locations, multiple vendors, talent acquisition and good quality training are just some of the numerous possible ways companies can begin working towards smooth execution of their digital strategy.

Feature Image Credit: Image credit: Pixabay

By

Gaurav Bhandari, AVP and Head of Data & Analytics Consulting at Infosys.

Sourced from techradar.pro

By Jessica Burton

Today’s cities are living entities. They develop, grow and become more complex over time. Yet, many of their most pressing issues, such as the need for utility improvements and monitoring crime, remain the same. Like never before, city officials have the capabilities to implement analytics technology. But surveillance will be at the heart of smart cities.

These technologies will help with a myriad of everyday city demands, in addition to more intricate challenges pertaining to security, healthcare, mobility, energy and economic development.

We need accurate insights into cities like never before.

With more than half of the world’s population residing in cities, this need for smarter and more accurate insights into their everyday workings is monumental. City management officials could learn much from leaders like Cisco, Amazon and Google. These companies have made it their business to not just collect data, but  utilize it to improve livelihoods and communities.  As we look to their successes, it becomes increasingly evident that the answer to creating smarter cities lies largely in surveillance technology that captures data analytics.

With the rise in surveillance technology and predictive analytics, we can make smart cities smarter and effectively, increase their efficiency. The reality is, however, that connectivity is never a guarantee. Therefore, necessary data must be present, regardless of connectedness, to ensure real-time decisions can be made. Satisfactory amounts of local storage must exist to position the most perceptive data nearest to the point of compute. This speaks to the increasing importance of the edge, as well as embedded storage.

Growth in real-time data is causing a shift in digital storage needs.

The growth of real-time data though edge analytics is causing a shift in the type of digital storage cities need. Fast, uncompromised access to data is becoming ever more critical. With a recent study, Data Age 2025: The Digitization of the World from Edge to Core, estimating that 175 zettabytes of data will be generated by 2025, there has never been a greater volume of insights at our fingertips and cities must step up to develop ways to use this data for good. In many ways, cities are already doing this – from intelligent street lights optimizing routes based on traffic patterns to reduce emergency response time by 20 to 30 percent, to advanced surveillance cameras with analytics deployed to enhance security operations, leading to a reduction in crime by 30 to 40 percent. However, we can do so much more.

To be a true smart city today, cities will need an “edge tier” approach to store, filter and manage data closer to the sensors. To gain deeper insights, the data is then stored and analyzed for longer periods of time in the edge domain as well as in the cloud or backend. Edge analytics that capture and collect data on network video recorders (NVRs) make it possible to act in real-time. With this technology, cities can find missing persons, notify residents of nearby emergencies and send out traffic congestion warnings.

Data insights will provide many wide-ranging benefits to cities.

The opportunities data analysis and data-driven urban improvement present are both hugely exciting and impossible to ignore. Behavioral analytics, thermal cameras and AI engines in edge devices like NVRs are just a sampling of the technologies that have given us the ability to remain constantly connected on a vast network. By horizontally interrelating individual systems, we can now develop insights into various mechanisms. This includes patterns in electricity, water, sanitation, transportation, environmental monitoring and weather intelligence.

West Hollywood’s Innovation Division is an excellent example to look to.

Take for instance, West Hollywood’s Innovation Division, which recently received the American Planning Association (APA) Technology Division’s Smart Cities Award for the “WeHo Smart City” Strategic Plan. Its three-part plan consisted of strategies including:

  • Data-driven decision-making rolling out to departments citywide
  • Collaboration and experimentation designed to enable City Hall staff to work better together.
  • Automation of processes to improve public safety and manage the built environment through smart city sensors and smart building programs.

With data collected from predictive analytics based on Deep Learning activities in the back-end, in some cases for over a year, we can pre-identify trends to manage incidents in one sector that directly impact another.

Access to real-time data and surveillance tech is key.

Cities need data in the moment and on the go. This places  a larger demand on the edge to produce the predictive and reliable information required, often in real-time. In fact, reports (Seagate) predict that due to the infusion of data into our city workflows and personal streams of life, nearly 30 percent of the “Global Datasphere” — meaning the amount of data created, captured or replicated across the globe – will be in real-time by 2025.

That’s a lot of real-time data. So, how can a city implement surveillance technology to better secure a city and enable smarter analyses? The first step is identifying video storage solutions positioned at the center of a smart city’s surveillance application. These solutions enable recordings, data retention, predictive analytics and real-time alerts. The next step is to position data at the edge and provide ample time for cities to make sense of patterns. More than ever before, cities will need to come together to integrate their technologies and ultimately make their networks smarter. This is a challenge that will require broad cooperation across its systems. Surveillance storage technology is the foundation to this strategy, ensuring timely data access and availability from edge to cloud.

By Jessica Burton

Global Product Marketing Manager at Seagate Technology. Jessica Burton has over 10 years of experience in IT storage and is the Global Product Marketing Manager at Seagate Technology. Her previous experience includes expertise in enterprise storage at Hewlett Packard Enterprise.

Sourced from readwrite

By Chris Donkin

LIVE FROM DIGITAL TRANSFORMATION WORLD, NICE: Mobile operators generally hold an inflated view of their ability to sell internal services using data analytics, but underestimate the technology’s cost-cutting potential, Axiata Analytics Centre head Pedro Uria-Recio (pictured) told Mobile World Live.

In an interview, Uria-Recio said: “Typically telecom operators overestimate their ability to increase their own revenue through analytics, because it is not only about analytics. That information will tell you who is likely to buy something, but then you have to reach the customer through the right channel and make the right offer, so it goes far beyond.”

However, he added operators were generally behind the curve on taking advantage of data to improve network performance and cut overheads.

“The second case is reducing cost. Here, I think telecom operators underestimate their ability to optimise the network through [technologies such as] artificial intelligence,” he noted. “There are things you can do to optimise the antennas and location of the network assets.”

When it comes to external use cases and partnering with other industries, he added “there is huge potential, but telecom operators have not yet cracked it”.

Opportunities cited include credit scoring, fleet management and advertising, though he warned many of the most lucrative sectors are “difficult to get into” and require collaboration with vertical companies.

To aid its own efforts, Axiata Group has a team of around 170 people working in data science and analytics. The division works on both optimising its own processes and selling services to third parties.

By Chris Donkin

Chris joined the Mobile World Live team in November 2016 having previously worked at a number of UK media outlets including Trinity Mirror, The Press Association and UK telecoms publication Mobile News. After spending 10 years in journalism, he moved to telecoms PR as a content specialist producing white papers and marketing collateral for some of the biggest names in the mobile sector. Now working as Content Editor across all channels of the MWL portfolio, Chris produces news and in-depth features on key issues from across the industry. @ChrisDonkin1

Sourced from Mobile World Live

By Charlie Custer

There’s never been a better time to learn data analytics and enter the workforce as a data scientist. The job landscape is promising, opportunities span multiple industries and the nature of the job often allows for remote work flexibility and even self-employment.

Plus, many data analytics experts boast a high median salary, even at entry-level positions.

With technology reaching new heights and a majority of the population having access to an internet connection, there’s no denying that Big Data and data analytics have become hot topics in recent years – and a growing need. According to IBM, the number of jobs for data professionals in the U.S will increase to 2,720,000 by 2020.

Demand for knowledgeable data analytics professionals currently outweighs the supply, meaning that companies are willing to pay a premium to fill their open job positions.

But the skillset and job opportunities within data science go beyond the tech and digital spaces. Let’s take a look at what you need to know as a data scientist – and what you’ll learn when you take our courses.

What Skills Are Required for a Job in Data Analytics?

As you delve into the 10 jobs we have here and start applying for positions in the data analytics field, you’ll notice many of them require the same foundational skills. Make sure you’ve mastered these before you start sending your cover letter and portfolio to potential employers.

And, if you find a skill that you still need to learn, remember that you can take an affordable, self-paced data science course that will help you learn everything you need to know for a successful career in data science.

Python

Python is currently one of the most commonly used programming languages.

Having a solid understanding of how to use Python for data analytics will probably be required for many roles. Even if it’s not a required skill, knowing and understanding Python will give you an upper hand when showing future employers the value that you can bring to their companies.

If you’re ready to advance your programming language proficiency, learn how to manipulate and analyze data, understand the concept of web scraping and data collection, and start building web applications, consider enrolling in our Python for Data Science: Fundamentals Course.

SQL (Structured Query Language)

Working with data sources is a necessary aspect of data analytics.

Early in your career, you’ll need at least a basic understanding of SQL. SQL (pronounced sequel) is often a major component of these positions. When you go to interview, listen for hiring managers’ mentions of this programming language when asking about your work with databases.

The experience you’ll get in our SQL courses will give you a good foundation. Like Python, SQL is a relatively easy language to start learning. Even if you are just getting started, a little SQL experience goes a long way.

Knowing the basics of SQL will give you the confidence to navigate large databases, and to obtain and work with the data you need for your projects. You can always seek out opportunities to continue learning once you get your first job.

Data Visualization Skills

Knowing how to visualize data and communicate results is a huge competitive edge for job seekers.

On the job market, these skillsets have high demand (and high pay)! Regardless of the career path you’re looking into, being able to visualize and communicate insights related to your company’s services and bottom line is a valuable skillset that will turn the heads of employers.

In this way, data scientists are a bit like data translators for other people in the organization that aren’t sure what conclusions to draw from their datasets.

At Dataquest, students are equipped with specific knowledge and skills for data visualization in Python and R using data science and visualization libraries.

10 Jobs that Require a Knowledge of Data Analytics

Before you take the time to learn a new skillset, you’ll likely be curious about the earning potential of related positions. Knowing how your new skills will be rewarded gives you the proper motivation and context for learning.

Lots of employers are hiring for these positions, both remote and onsite, worldwide. Here are a few positions worth looking into – and their median incomes, according to popular job search websites.

1. IT Systems Analyst

Systems analysts use and design systems to solve problems in information technology.

The required level of technical expertise varies in these positions, and that creates opportunities for specialization by industry and personal interests. Some systems analysts use existing third-party tools to test software within a company, while others develop new. proprietary tools from their understanding of data analytics and the business itself.

A typical systems analyst in the US makes around $68,807.

2. Healthcare Data Analyst

Healthcare data analysts have the opportunity to improve the quality of life for many people by helping doctors and scientists find answers to the questions and problems they encounter on a daily basis.

The amount of data coming from the healthcare industry is growing rapidly, be it with the increased popularity of wearables like Apple Watch or through enhanced medical testing in clinics, hospitals and labs. Plus, with a rise in regulations and restrictions on how that data can be stored, retrieved, and processed, demand for proficient data analysts is on the rise as well.

The median salary for a Healthcare Data Analyst is $61,438.

3. Operations Analyst

Operations analysts are usually found internally at large companies, but may also work as consultants.

Operations analysts focus on the internal processes of a business. This can include internal reporting systems, product manufacturing and distribution, and the general streamlining of business operations.

It’s more important for professionals in these roles to have general business savvy, and they often have technical knowledge of the systems they’re working with. Operations analysts are found in every type of business, from large grocery chains, to postal service providers, to the military and can make upwards of $75,000 annually. Due to the versatile nature of this data analytics job and the many industries you may find employment in, the salary can vary widely.

4. Data Scientist

Much like analysts in other roles, data scientists collect and analyze data and communicate actionable insights. Data scientists are often a technical step above of data analysts, though. They are the ones who are able to understand data from a more informed perspective to help make predictions. These positions require a strong knowledge of data analytics including software tools, programming languages like Python or R, and data visualization skills to better communicate findings.

These positions are challenging – and rewarding, with an average salary of $91,494. The demand for data analytics experts with technical backgrounds is at an all-time high.

Dataquest has multiple learning paths that are tailored to provide you with everything you need to hone your technical skills, including the Data Scientist Path that will help you become a certified data scientist.

5. Data Engineer

Data engineers often focus on larger datasets and are tasked with optimizing the infrastructure surrounding different data analytics processes.

For example, a data engineer might focus on the process of capturing data to make an acquisition pipeline more efficient. They may also need to upgrade a database infrastructure for faster queries. These high-level data analytics professionals are also well-paid, with median salaries being comparable to data scientists at $90,963.

6. Quantitative Analyst

A quantitative analyst is another highly sought-after professional, especially in financial firms. Quantitative analysts use data analytics to seek out potential financial investment opportunities or risk management problems.

The median salary for quantitative analysts is $82,879. They may also venture out on their own, creating trading models to predict the prices of stocks, commodities, exchange rates, etc. Some analysts in this industry even go on to open their own firms.

7. Data Analytics Consultant

Like many of these positions, the primary role of an analytics consultant is to deliver insights to a company to help their business. While an analytics consultant may specialize in any particular industry or area of research, the difference between a consultant and an in-house data scientist or data analyst is that a consultant may work for different companies in a shorter period of time.

They may also be working for more than one company at a time, focusing on particular projects with clear start and end dates.

These positions are best for those who like change, and those who have a narrowed interest and expertise in a field of study. Analytics consultants are also in a great position to work remotely, another enticing factor about this role to consider.

Compensation varies widely by industry, but $78,264 is a representative salary for the role.

8. Digital Marketing Manager

Digital marketing also requires a strong knowledge of data analytics. Depending on your other complementary skills and interests, you could find yourself in a specific analytics role within a company or agency, or simply applying your data science expertise as a part of a larger skillset.

Marketers often use tools like Google Analytics, custom reporting tools and other third party sites to analyze traffic from websites and social media advertisements. While these examples require a basic understanding of data analytics, a skilled data scientist has the ability to create a long-term career in marketing.

A lot of money could be wasted on campaigns that do not drive traffic, so marketing professionals will continue to need analysts to make smart decisions about how to leverage existing resources.

While digital marketing positions have a wide range, the higher end salary for an advanced digital marketing manager is $97,000.

9. Project Manager

Project managers use analytics tools to keep track of a team’s progress, track their efficiency, and increase productivity by changing processes.

Project managers need at least a working understanding of data analytics, and often more.

These positions are found internally at large corporations, and frequently in management consulting. Another example of a career trajectory for project managers could be moving into product and supply chain management, which companies rely on to maintain profit margins and smooth operations.

A typical salary for a project manager is around $73,247.

10. Transportation Logistics Specialist

A transportation logistics specialist optimizes transportation of physical goods, and could be found in large shipping companies, like Amazon, UPS, naval transport companies, airlines and city planning offices.

A data analytics background is especially helpful in this job because transportation logistics specialists need to reliably identify the most efficient paths for products and services to be delivered. They must look at large amounts of data to help identify and eliminate bottlenecks in transit, be it on land, sea or in the air.

With seasoned professionals in this industry making around $79,000 per year, a transportation logistics specialist is an appealing career path for individuals who are detail-oriented, technical and forward thinkers.

A data analytics background also helps transportation logistics specialists, among others, to focus on the most important issues, seeing potential problems and solutions in context and communicating those effectively.

Data Analytics Opportunities Around The Globe

These are just a few of the many high-paying jobs which require knowledge of data analytics. Specific figures from this article are for the median salaries in the United States, all cities included.

Salaries in each city may vary and reflect local demand and general cost-of-living expenses. Boston, Portland, and Denver, for example, have become hotspots for data analytics positions.

While the numbers included in this article represent a typical salary in the United States, opportunities for data analytics professionals can be found all around the globe. Many of them can even be done remotely, allowing you the highly-desired opportunity to work from anywhere in the world on a competitive US salary.

Whether your goal is to get a full-time job in a new industry, advance in your existing career, or work for yourself in the data analytics field, Dataquest can prepare you for the opportunity. With the portfolio-building missions and projects in Dataquest’s Data Analyst path, a community of mentors, and a strong alumni network, you’ll have all you need to become a certified data analyst and be set up to get the job of your dreams.

By Charlie Custer

Sourced from DATAQUEST