Data Analytics Classes Bay Area, CA

Data analytics is the process of analyzing raw data to find trends and answer questions. Applicable in any industry, this process includes extensive sets of diverse data—structured, unstructured, and semi-structured—that are continuously generated at high speed and in high volumes.

Big data is typically measured in terabytes or petabytes, with one petabyte equal to 1,000,000 gigabytes. One petabyte is the equivalent of 250,000 films, and the large datasets used in data analytics measure anywhere from hundreds to thousands to millions of petabytes. (That’s a lot of data!)

A successful data analytics professional can help answer business questions related to historical trends, future predictions, and decision-making. Machine learning tools, massive datasets, and more affordable computing power have made these techniques applicable in many industries, helping businesses draw meaningful conclusions from complex and varied data sources.

What Can You Do with Data Analytics Training?

If you speak with a Data Scientist or analyst, they’ll tell you that it’s more than just a potential profession. Data analytics is a hobby, a passion, one that can be applied in all areas of life. Noticing issues in your neighborhood? You could use urban data flows to predict and prevent infrastructural issues like potholes through a complex analysis of factors like traffic volume, pavement age, weather, and traffic accidents. Whether you’re on your local HOA board or presenting to your city council, your insights can help better your community.

Or, you could use predictive analysis to gather information on various sports-related topics, allowing you to make strategic choices in your fantasy leagues based on current team and player strengths and weaknesses.

Data analytics is, at its base, the combination of pattern recognition and a strong understanding of math and statistics. These things exist in everything around you, so data analytics can apply to almost anything you can think of.

What Will I Learn in a Data Analytics Class?

Data analytics courses can teach you about the data analytics process (including collecting, wrangling, mining, and visualizing data), different data roles, and data structures. Basic math will be covered as well and, for technical skills, you can expect to study programming languages like Python, database tools like Excel, and data visualization tools like Tableau. By the end of a data analytics course, you should be competent and confident enough to begin or expand a career in data science and analytics.

Data Analytics Process

Typically, the data analytics process is divided into six sequential parts:

• Define the problem or research question: You’ll ask questions like “What are the problems that are being mentioned by my stakeholders?” and “What are their expectations for the solutions?” while identifying the root problem that needs addressing.
• Collect data: The data has to be collected from various internal or external sources. Common sources of data collection include interviews, surveys, feedback, and questionnaires. The collected data can be stored in a spreadsheet or SQL database.
• Data cleaning: Clean data means data that is free from misspellings, redundancies, and irrelevance. One of the most important steps in this process, different functions provided by SQL and Excel can help you clean the data.
• Data analysis: The cleaned data is used for analyzing and identifying trends. Sample code can be written in SQL to perform calculations, and you can use Excel to create pivot tables and perform calculations.
• Data visualization: Nothing is more compelling than a visualization. Tableau is a simple drag-and-drop tool that helps in creating compelling visualizations. Looker is a data visualization tool that directly connects to the database and creates visualizations. Sharing the insights with the team members and stakeholders will help in making better, more informed decisions.
• Presenting data: This process includes the creation of visual representations (like charts, graphs, and tables) to effectively communicate patterns, trends, and insights gleaned from the data analysis. The goal is to facilitate a clear understanding of complex information, making it accessible to both technical and non-technical audiences.

Data Types and Structures

A data type is the most basic and the most common classification of data, one where information is transmitted between the programmer and the compiler. The programmer informs the compiler about what type of data is to be stored and also how much memory space it requires. A few basic examples are int and string, essentially any variable used in the code.

In contrast, a data structure is a collection of different forms and types of data that has a set of specific operations that can be performed. A collection of data types, data structures are a way of organizing the items in terms of memory, and also a way to access each item through some defined logic. Examples of data structures are stacks, queues, linked lists, and binary trees.

Math Skills

The math requirements for a Data Analyst vary, but they typically include probability and statistics, calculus, and linear algebra. The foundation for optimization algorithms and deep learning techniques, calculus helps data scientists solve optimization problems, like finding the minimum or maximum of a function. Probability is critical for hypothesis testing and distributions like the Gaussian distribution, while statistical methods are used to analyze large datasets and interpret the results. Finally, linear algebra helps data scientists understand algorithms. While you don’t need to have complete mastery of all of these, it is helpful to have some knowledge of these specific math skills to succeed at data analytics.

Programming

Programming is essential for Data Analysts to manipulate, clean, and analyze data efficiently. Through programming languages like Python or R, analysts can clean and preprocess data, handle missing values, and format it for analysis. Proficiency in these languages can be highly beneficial in performing advanced analysis and building custom data models.

Data Visualization

Data visualization is a key part of data analytics and business intelligence that uses visual elements to represent data. It translates complex, high-volume, or numerical data into a visual representation that is easier to process. Data visualization tools improve and automate the visual communication process for accuracy and detail. You’ll use tools like Tableau to create engaging and effective data visualizations that help communicate your findings to various stakeholders.

How Hard is It to Learn Data Analytics?

Given the field's complexity and the diverse skill set required to excel, data analytics is considered somewhat challenging to learn, blending elements of statistics, mathematics, computer science, and specific industry knowledge. In data analytics, both the initial learning curve and the requirement for continuous learning are substantial, largely because of the breadth of the field and the continual advancement of technology. While data analytics comes with its challenges, it is ultimately a field that anyone can master. With the right amount of passion, dedication, and time, anyone can become an expert in data analytics.

What Are the Most Challenging Parts of Learning Data Analytics?

The answer to this question may vary, depending on your current skill set. If you don’t have previous experience with programming or a background in mathematics, you may find mastery of these two subjects particularly challenging. Additionally, understanding the data often requires documenting data assets by keeping track of information such as every definition of columns in a data warehouse's tables. Some parts of data analytics, like understanding data, may seem repetitive or time-consuming, but usually there are tools to help streamline these processes.

How Long Does It Take to Learn Data Analytics?

Data analytics is a broad field; the time it takes to learn depends on several factors. You can master the basics of data analytics in less than a year, but your level of expertise will include various skills and practice in a particular role. You shouldn’t expect to become an advanced data pro in a matter of months.

The time required also depends on your existing knowledge and how much time you can dedicate each week. With full-time dedication (30-40 hours per week), you can become proficient in around three months, while part-time learners (15-20 hours per week) might take six months or more.

One factor to consider is the type of training you choose. Self-guided data analytics education can take much longer than a targeted bootcamp or certificate, so choosing the right course that fits your schedule is paramount to your success and efficiency.

Should I Learn Data Analytics in Person or Online?

There are several factors to consider when answering this question. First, you’ll want to consider what learning environment is best suited for you. Do you thrive in a collaborative environment where you can interact with your peers and receive in-the-moment feedback from your instructor? Or, do you prefer a more solitary learning environment, perhaps one where you can learn at your own speed?

You’ll also want to consider how much time you can dedicate to your education and what level of flexibility your schedule requires. Are you looking to immerse yourself in an intensive program? Do you work full-time and need your classes to be scheduled for evening and weekend hours?

In-person courses offer face-to-face interaction with both your fellow students and your expert instructor. This collaborative environment is available for both full-time and part-time classes and provides structure and accountability, two things that can help you succeed. Online classes may lack this in-person interaction, but they make up for it with increased flexibility. Live online classes still offer instant feedback from your instructor and interactions with other students, while on-demand virtual courses allow for the most flexibility since you can decide where and when you learn.

The good news is that, no matter which option you choose, you can obtain a quality education with a top-notch curriculum and expert instructors.

Can I Learn Data Analytics Free Online?

Technically, it is possible to learn data analytics for free online. YouTube videos, blogs, and free short workshops can all be a great way to learn the basics without purchasing any educational content. (Don’t forget to watch the most recent uploads so you’re learning the latest trends and techniques.)

But, for the more advanced skills, an in-depth training course is going to be your best bet. These courses have a curriculum developed and taught by experts with the chance to receive feedback or troubleshoot any issues with your instructor or a support team. Formal training can also provide the structure and accountability that some students need to stay focused, creating an environment where you’re more likely to succeed. So, if you’re looking to master more than just the basics, a formal workshop or class is a better choice than free online resources.

What Should I Learn Alongside Data Analytics?

There are several other skills that you can start working on while studying data analytics. While you’ll most likely have the experience needed in mathematics and statistics, it can’t hurt to brush up on those two subjects. Additionally, programming languages like Python, R, Java, C, or Perl will be very useful.

Hadoop is the most popular Big Data framework. Most professional arenas will expect (or hope) that you have experience in Hadoop, so it can’t hurt to tackle it alongside data analytics. Likewise, SQL is the most common way of getting information from a database and updating it, so if you can master SQL as well, you’ll be a step ahead of your career competition.

Industries That Use Data Analytics

The Bay Area’s various thriving industries and ever-growing economy make it the perfect place to study and work in data analytics. Below are some of the top industries in the region, each of which utilizes Data Analysts in a variety of ways. Check out these options to get one step closer to your dream career.

International Banking and Finance

Serving as an international center for trade, San Francisco has long been the gateway between America and the Pacific. It is the largest U.S. exporting region to Asia, with more than 71 consulates and foreign trade offices located in the city. It is also the tenth-largest exporting region in the U.S. and the financial services epicenter of the West Coast, housing 30 of the largest non-American banking institutions in the country like Barclays Bank of the United Kingdom, Sumitomo Bank of Japan, Banco di Roma of Italy, Banque Nationale de Paris of France, Hong Kong and Shanghai Bank of China, Industrial and Commercial Bank of China, HSBC, Lippo Bank of Indonesia, and The Royal Bank of Scotland.

Social and Digital Media

Social and digital media is another thriving industry, consisting of more than 300 digital media companies like YouTube, Electronic Arts, Zynga, X (formerly known as Twitter), Dolby Laboratories, Pixar, Sony, Sega of America, Konami Digital Entertainment America, and PDI DreamWorks SKG. YouTube alone contributed $35 billion to the U.S. gross domestic product and supported over 390,000 full-time equivalent jobs in 2022, so it isn’t hard to see the success and growth of this industry in San Francisco and beyond.

Technology

Technology is perhaps the most well-known industry in San Francisco, which is, in part, thanks to its location just 40 miles north of Silicon Valley cities like Cupertino, Palo Alto, and Mountain View. The Bay Area is also home to several tech giants like Salesforce, Instacart, Apple, and Lyft and is also a prime location for start-ups. The region employs more than 400,000 tech professionals, and you could be one of them.

Importing and Exporting

Just across the bay from San Francisco, Oakland employs nearly 200,000 professionals related to the movement of cargo through Oakland marine terminals. Chief exports at the port include fruits and vegetables, waste paper, red meat and poultry, resins, chemicals, animal feed, raw cotton, wood and lumber, crude fertilizers/minerals, industrial machinery, and cereal. Principal imports include auto parts, computer equipment, wearing apparel, toys, games and items made of plastic, processed fruits and vegetables, fasteners and household metal products, red meat, pottery, glassware and ceramics, iron and steel, beverages, and lumber products.

Data Analytics Job Titles and Salaries

There are several exciting career options available for both those who are just getting started in the field of data analytics and those looking for advancement. Each position will have a unique specialty or focus, so you’ll want to have a clear understanding of how you’d like to apply your data skills professionally.

Financial Analyst

Financial Analysts guide individual stakeholders, businesses, and companies on how best to invest money and resources to gain maximum profit. They analyze multiple factors like business environments, market trends, financial status of companies, expected outcomes of operations, and past financial data to make the most informed and calculated decisions. In the Bay Area, you can make between $100,000 and $120,000 as a Financial Analyst.

Database Developer

Database Developers study database processes with the goal of updating them, improving their efficiency, and getting rid of inefficient coding. They keep track of how existing databases are performing and use code and web architecture to create data systems, analyze and maintain existing databases, and implement new user features. You can expect to make around $117,000 as a Database Developer, Administrator, or Architect in the U.S., with ranges slightly higher in the Bay Area.

Weather Analyst

Do you also have an interest in weather systems? Weather Analysts study atmospheric occurrences and analyze data pertaining to meteorological events to provide forecasts and reports. It’s important they have a sound understanding of math and statistics and are also familiar with meteorology. Meteorologists and Atmospheric Scientists make $107,044 to $172,030 per year in the Bay Area, so you can expect salaries around that range.

Marketing Analyst

Are you a marketing or digital media whiz? You could become a Marketing Analyst and spend your time researching and optimizing campaigns, and you may be required to use tools like Moz or Ahrefs, depending on your employer. A Marketing Analyst must also have a basic understanding of data analysis and using Google Analytics. You can earn around $110,000 as a Marketing Analyst in San Francisco, with the Oakland area seeing salaries closer to $88,000.

Data Analytics Classes Near Me

Data Institute Professional Certificates

The University of San Francisco offers several professional certificates in various data science specialties. You could work towards a certificate in applied machine learning, data science for marketing, AI and data ethics, or Python for data analysis (to name a few). Each certificate program offers a range of introductory, intermediate, and advanced courses for you to choose from, depending on your chosen area of focus. Held online, these programs feature hands-on practice with real-world examples. You’ll receive an official certificate of completion at the end that can be added to your resume or LinkedIn profile.

Online Data Analytics Boot Camp

This fully virtual program from UC Berkeley teaches you the knowledge and skills to conduct analytics on a wide array of real-world problems. You’ll learn through a robust curriculum that covers tools and technologies like Excel, Python, JavaScript, HTML5/CSS, API interactions, SQL, Tableau, fundamental statistics, machine learning, Git/GitHub, and more. You’ll then apply your knowledge to projects that align with real-world datasets from various industry fields like finance services, human resources, healthcare, and government. These projects allow you to build a professional portfolio that showcases your skills to stand out in today’s job market.

Data Science Flex

Hosted by Thinkful, this part-time program comes with a tuition refund guarantee if you can't land a job within six months after your graduation. You'll learn how to clean data using Python, query data using SQL, train and evaluate supervised and unsupervised machine learning models, and then choose a track to specialize in. You can dive deeply into Natural Language Processing (NLP), Big Data and Spark, and Deep Learning with TensorFlow and Keras.

Held entirely online over six months, this course can be completed at your own pace to accommodate your busy schedule. Prospective students are required to complete three weeks of prep work and pass an assessment test before being accepted into the program.

Data Science Certificate

Offered by Noble Desktop, this in-depth course can take anywhere from four to 20 weeks to complete. You’ll learn all the skills needed to become a professional Data Scientist, like how to manipulate databases and perform data analysis, master the fundamentals of Python programming, and use its main data science libraries to analyze data. You’ll also try your hand at creating machine learning models, making dashboards and visualizations, and deploying your projects online with GitHub.

Held live online, this course includes access to 1:1 mentoring, a free retake within one year, and a verified digital certificate upon completion. Small class sizes and live project-based training with industry experts help ensure you gain all the knowledge and experience needed to land your dream Data Scientist job.

Data Analytics Certificate

This project-based course from Noble Desktop teaches you to gather, wrangle, analyze, and visualize data using various tools and technologies to help businesses with decision-making and strategy. You’ll work on multiple real-world projects to explore both predictive and prescriptive analytics and gain practical experience. Plus, you have access to 1:1 mentoring, a free retake, and setup assistance when you enroll in this course. Payment plans are available to make this certification more accessible.

Data Analytics Corporate Training

If you’re looking to offer in-person or online training to your office or team, Noble Desktop can help. They offer a variety of corporate training programs, including data analytics. You can order vouchers in bulk at a discount, choose which courses are available to employees, and even let your employees choose their own schedule. Plus, you can choose to have your training hosted at a location of your choosing, live online, or at Noble Desktop’s Manhattan office.

With a top-notch customizable curriculum and instructors who are experts in their industries, Noble Desktop’s corporate training is designed to cater to your business or organization’s needs. For more information or to schedule a free consultation, you can reach out to Noble Desktop at corporate@nobledesktop.com.