Data Analytics Courses in NYC or Live Online

About Data Analytics

What is data analytics?

In this increasingly tech-enabled world, everything from washing machines to doorbells connects to Wi-Fi. As a result, computer programs and applications are generating data at an astonishing rate. A recent Forbes study suggests that human beings produce 2.5 quintillion bytes of data a day (which amounts to 2,500,000,000,000,000,000 bytes of data). There has never been a more exciting time to learn about data! According to Forbes, more than 50% of companies are analyzing big data (a fancy term for massive datasets) to help them gain a competitive advantage in the marketplace. This has also led companies to increasingly prioritize hiring data analytics experts who can help them make sense of the massive datasets they are working with.

Data analytics is the process of manipulating and analyzing data to understand and uncover insights that can influence business decisions. Companies use these insights to target and adjust their efforts to reach their target audience, understand what parts of their marketing campaigns are succeeding, and determine whether certain investment and expansion options will likely generate long-term success. Customer and social analysis are among most companies’ primary goals for data analytics. Financial analysis is also an important component of this process.

Why learn data analytics?

Data analytics is a very versatile skill if you are hoping to succeed in almost any field. Professionals in virtually every industry, from commercial software development and the financial services industry to the entertainment industry and professional sports, are discovering that analyzing and interpreting data is essential to getting a leg up on the competition. Whether it is as simple as using a tool like Google Analytics to track user behavior online or as complex as building machine learning algorithms to spot trends in unimaginably huge datasets, learning data analytics skills is an invaluable tool for ensuring the success of your current project.

Learning the basics of data analytics can also be useful for aspiring entrepreneurs and business owners looking for ways to minimize the risks they are taking early on in their careers. The early days of running a business are the most fraught for most professionals. Learning how to analyze data can help ease the difficulties by letting business owners and investors make more informed decisions about their projects’ direction. For example, learning the basics of data analytics for digital marketing can give you insight into how to reach a new consumer base or bring new consumers to your storefront most successfully. These tools won’t guarantee success, but they will ensure that you take all the steps necessary to maximize your chances of success.

Data analytics isn’t only utilized in commercial contexts. Not only are professional sports organizations beginning to use data analytics to gain a competitive advantage (at virtually every level of play), but political organizations and nonprofit advocacy organizations are starting to leverage data analytics to improve their rhetorical persuasiveness. Political campaigns are relying on voter data information that can get as specific as understanding which streets to target with advertising materials, and even advocacy organizations are seeing the value in data analytics practices like SEO (being the first charitable organization on Google is a great way to put your specific message into the public consciousness). Regardless of the field you plan on working in, learning data analytics skills will likely make you a more competitive applicant for your dream job.

Data analytics vs. data science

Some students may be confused by the distinction between data analytics and data science when looking for training options. While the two fields use many of the same tools and data scientists and data analysts tend to work closely with each other, the terms and theories involved in these fields are not interchangeable.

Like data analytics, data science is also a hot topic but involves different skills and tools. Data science is the study of data, with a focus on using algorithms, mathematics, and techniques to extract useful data from non-traditional data sources. While data analytics relies on readily available data, data science focuses on extracting this data into a usable form that can be processed and analyzed with data analytics software.

Data scientists use tools like the coding language R and the Hadoop framework to “scrape” or extract raw data from a website. In data science programs, students often learn how to scrape public websites with lots of raw data such as X (formerly known as Twitter) or Google. Examples may include writing an algorithm to produce all results in a web directory or finding all records containing a specific word on a public site.

Another way to look at the difference between data science and data analytics is to view data science as preparing data before analysis and data analytics as reviewing data after the fact. Data scientists are experts in data extraction, cleaning, and preparation, while data analysts are experts in uncovering trends and actionable insights. Despite being similar in name, data science and data analytics involve entirely different data processes, tools, and functions.

Data analytics fields

FinTech

One of the most significant places where data analytics is changing the shape of the industry is in the financial sector. The financial services industry generates an unfathomable amount of data daily, with the NYSE alone seeing millions of trades daily. This is far more data than human beings can reasonably process, and when financial consultants and investment bankers are working with millions of dollars of their clients’ money, they must have more than gut instincts to go off when making fiscally sound decisions. Data analytics, particularly financial technology (FinTech), has emerged as one of the ways in which professionals in the financial sector can be more confident in their investment advice.

Financial technology helps investment analysts and financial analysts identify subtle, long-term trends in asset and futures markets that they can take advantage of to see steady returns on their and their clients’ investments. Smart investing isn’t about trying to hit it big on one investment. Rather, it is about making many small, informed decisions that generate value over time. FinTech helps professional and amateur investors gain a more nuanced understanding of the financial marketplace to make better, more consistent investment decisions and see solid returns on their investments.

To learn more about FinTech course options, consider exploring the course offerings available through Noble Desktop.

Machine learning

Another important emerging field in data science and analytics is machine learning, a process by which a computer algorithm is programmed to ‘read’ large amounts of data and ‘learn’ how to identify patterns and draw inferences from this dataset. This is an exciting new technology that companies are trying to integrate into their regular business practices since it promises to revolutionize how entire industries function. At the moment, the technology is primarily used in content recommendation algorithms for websites like Amazon, YouTube, and Netflix. However, there is optimism that the technological leaps made in AI technology may significantly impact almost all consumer-facing industries as algorithms are refined and processing power improves.

Data analysts are most likely to use machine learning programs as tools for analyzing datasets and building and interpreting the results produced by those algorithms. While data scientists will be responsible for building machine learning algorithms, analysts will be responsible for practically implementing those algorithms and their outputs into a business’s long-term planning structures. They will need to learn how algorithms can be trained on a company’s existing databases, and they will need to know how to feed the algorithm this data. Since machine learning programs are incapable of nuanced thought, data analysts must be thoughtful and deliberate in how they use the machines since they don’t want to replicate bias or turn a blind eye to important trends because their initial training data was poor.

Noble Desktop provides interested students with options for machine learning training in many of their data science and data analytics classes.

Automation

Since humans generate so much data every day, it is vital that some of the processes of organizing, storing, and querying this data are automated. Without the aid of dedicated programs and machines, humans would be unable to catalog all of the data being produced in a way that is useful to anyone. Some of the most important data analytics tools are programs that automatically record and organize data so that companies can easily see how users engage with their products and services. For example, Google Analytics automatically records and organizes web traffic data and creates useful data visualizations that companies can use to track web traffic and see how potential customers are finding their webpage.

Automation is also important for helping data analysts query complex datasets. Learning how to use programming languages like Python will let data analysts create programs that automatically run complex queries that they can use to make sense of datasets and make inferences based on the data with which they are working. This includes everything from using data scraping programs that automatically collect important user data to data organizing tools that take that data and automatically sort it and apply metadata information to it to make it easier to find in a database. This automation process may seem simple, but knowing how to automate the data collection and organization process properly can be the difference between having a useful dataset and having a poorly constructed dataset.

Digital marketing

Data analytics skills are often employed by digital marketers looking to make more informed decisions about what strategies and marketing tactics to employ. They will need to understand consumer behaviors. One of the best ways to do this is to track and analyze the data that customers leave behind when interacting with your webpages and digital applications. Without knowing how your customers are behaving, it is next to impossible to judge where you should focus your efforts when building a digital marketing strategy.

Digital marketers will use data analytics tools like Semrush and Google Analytics to quickly view and understand data points like web traffic and search engine optimization information for your major websites. Social media marketers will use data analytics skills to track engagement on various social media platforms to see if that engagement correlates with business performance. These metrics are important for understanding what aspects of a digital marketing campaign are landing and what elements can be scaled back. Businesses aren’t interested in throwing away money on marketing efforts, so knowing how to read and analyze data can help you make more informed decisions concerning the efficacy of these campaigns.

Noble Desktop offers a wide range of digital marketing classes for students looking to learn how to apply their data analytics skills to this field.

Data analytics tools

Python

Python is the most commonly used computer programming language in the field of data science and data analytics (it is also, according to TIOBE, the most popular programming language in the world). Python is primarily used for building and querying databases, making it an invaluable programming language for anyone interested in data science and data analytics to learn. Using Python, programmers can build complex databases that store multiple different kinds of data and write programs that automatically query and organize that data. Importantly, Python can be used to write scripts that use this data to make forecasts and predictions, as well as scripts that help to visualize large datasets. All these different functionalities come together to make Python one of the most versatile programming languages you can learn when working with data. Working in data analytics, you may not need to learn advanced Python programming (since most of the tools you work with will have been built by data scientists), but if you have a background or interest in coding, learning Python is an ideal step to improving your data analytics skills.

Python is regularly used for the following tasks:

• Building databases
• Analyzing datasets using Python libraries
• Creating linear predictions based on datasets
• Querying databases
• Organizing data
• Writing computer programs that rely upon datasets
• Writing machine learning algorithms

Microsoft Excel

Despite being a popular tool that most individuals have on their personal and business computers, very few people harness the complete power of Microsoft Excel. Microsoft Excel is a data analytics powerhouse that offers support at every stage of the data analytics process from cleaning and analyzing data to performing analysis and displaying results in charts and graphs. Its most advanced users write macros (complex instructions that automate computer tasks) using Visual Basic for Applications (VBA), a coding language used to program Excel.

Microsoft Excel can perform the following tasks:

• Reformat data
• Use formulas to calculate new values from existing data
• Forecast financials using equations and functions
• Complete math functions and statistical analysis functions like ANOVA and regression
• Display results and analysis using pie, line, and bar charts
• Learn more about Microsoft Excel and our courses.

SQL

SQL (whether it’s pronounced ess-que-el or sequel is up for debate) is a coding language used to store, organize, and retrieve data from relational databases. With SQL, you can analyze data for an ecommerce store by joining multiple datasets or tables to gain new insights. For example, you can query and merge two datasets, one containing customers and another containing orders, to determine all the customers who bought a specific product and their particular location.

It is important to note that SQL is not a database itself but rather a command language used to communicate with a relational database, most commonly a Microsoft SQL Server.

In recent years, programmers have created adaptations of SQL to use with modern object-oriented or non-relational databases. With SQL, you can perform the following tasks:

• Create a database
• Sort and/or filter data within a database
• Query a database to find specific information about a product or user
• Join two different datasets to view information that meets specific criteria
• Convert output data or export it to external applications
• Discover our industry-leading SQL Bootcamp.

Tableau

Tableau is a powerful tool that allows you to visualize your data analysis. The benefit of Tableau is that non-technical professionals and decision-makers can immediately notice trends and data insights without poring over detailed reports or datasets. Tableau is useful in any industry with access to data and is a popular tool in both the public and the private sectors. Noble Desktop offers targeted Tableau training courses for students who want hands-on experience using the tool.

With Tableau, you can do the following:

• Connect raw datasets from almost any program, including Microsoft Excel, SQL, Microsoft Access, and SPSS
• Use Tableau Prep® to clean and analyze data for further analysis
• Analyze trends and stories within your data
• Create responsive insights that can be customized by colleagues
• Manipulate interactive charts, graphs, and other representations of data

Google Analytics

Google Analytics is just one of the many proprietary tools offered by online web service providers for tracking consumer data and helping optimize webpages. Google Analytics is, in particular, useful for tracking engagement with your webpages, determining where user traffic is coming from, and determining which of your webpages is most successful at reaching consumers. Google Analytics lets users easily visualize and compare their web traffic data to help data analysts present their findings to interested stakeholders. Google Analytics also pairs well with other digital marketing tools like Semrush or Google Ads, which can help specialized data analysts create a fuller picture of the state of any given online marketing campaign. To learn more, consider enrolling in one of Noble Desktop’s Google Analytics training programs.

With Google Analytics, you can do the following:

• Build analytics reports on your webpage’s performance
• Analyze web traffic
• Discover what search terms are leading users to your website
• Gather data relevant for SEO purposes
• Leverage Google’s web services for your business needs.

Data analytics jobs

According to BLS statistics, data science and analytics job openings are expected to grow at a staggering 36% year over year during the next decade. This is more than 700% faster than the national average, making data analytics proficiencies some of the most in-demand job skills in the country.

Data analytics jobs fall into different categories depending on your knowledge of programming and statistics. Most data analytics jobs fall within the business analyst role, in which data professionals work with Excel and Tableau. Some may have additional skills in a programming language or other specialized software. More broadly speaking, data analytics skills are vital for almost any professional marketing position since it will be essential for advertisers to understand how to gauge and read user data as a tool for building and modifying advertising campaigns. As data analytics becomes more prominent in the business world, more professionals will be expected to understand the basics of reading and interpreting data, even if they don’t specialize. In the U.S., business analysts earn an average salary of approximately $78,000.

Most data analytics professionals must incorporate data from various tools to form one coherent story. A campaign targeted to customers, including website traffic, social media, and other forms of marketing, can form three separate data sources. Data analysts often import data into one program to determine which strategies are most effective and identify new approaches that can lead to even more customer engagement and sales.

Business analysts typically use tools such as Microsoft Excel to analyze data and Tableau or another visual tool to communicate actionable insights to decision-makers. Statisticians take data analytics further by using R, SPSS, or other statistical analysis tools to determine risk or make predictions. Other data analysts work within large databases such as SQL or Microsoft Access.

On the financial side of data analysis, investment analysts and financial analysts are often employed to help companies and individuals make sense of financial data that would otherwise be nearly impossible to parse. These professionals use data analytics tools like Excel and Python databases to organize and interpret financial data and programs like Tableau to communicate their findings to clients. Financial analysts tend to work for companies to manage and analyze their large financial holdings, while investment analysts focus more on predicting the movement of stock markets and asset exchanges. Investment analysts are also more likely to work for private investors. In the U.S., the average yearly salary for a financial analyst is $72,000, while investment analysts earn about $78,000.

Data analytics tools are also commonly used by digital marketers and other online advertisers to help build more successful advertising campaigns based on observable metrics. These professionals will use tools like Google Analytics and AWS to collect, organize, and analyze consumer data. Then, they will use their digital marketing training to make inferences based on that data and build advertising campaigns equipped with this knowledge. Some more specialized job titles for digital marketers include social media strategists, who focus on using social media platforms to advertise products, and web optimization specialists, who will use data analytics tools to build a digital presence that captures a larger share of the market. While specialization and job title will impact your salary, on average, a digital marketer makes an annual salary of about $62,000 a year.