Unlock the power of Python for data-driven decision-making as you master Python programming fundamentals and dive into data analysis. Acquire essential skills to clean and manipulate data, create insightful visualizations, and perform statistical analysis, all through hands-on projects with real-world datasets.
Learn to extract information from databases by writing SQL queries, joining tables, aggregating data, and filtering results. You'll learn PostgreSQL in this class, but the concepts apply equally to other databases such as SQL Server and MySQL.
Become proficient in Microsoft Excel and go from beginner to pro with our hands-on Excel Bootcamp. Learn functions, formulas, Pivot Tables, macros, and time-saving techniques to conquer all the key skills needed to excel in Excel.
Learn how to use Tableau, a powerful data visualization tool, to create interactive and aesthetically pleasing charts, graphs, and maps. This course will teach you how to connect to datasets, analyze and structure data, customize visualizations, and publish your work.
Learn how to use Power BI, a powerful data visualization tool, to create interactive and aesthetically pleasing charts, graphs, and maps. Learn how to connect to datasets, analyze and structure data, customize visualizations, and publish reports.
Learn the fundamentals of data analytics, including descriptive and inferential statistics, and how different industries and organizations use data for forecasting and decision-making. Explore statistical analysis and modeling, including statistical algorithms, theorems, and models.
Learn the fundamentals of machine learning, including regression analysis and classification algorithms, in this practical, hands-on course. Gain the skills needed to solve real-world problems using machine learning, with a focus on Python programming and data science libraries.
Automate and enhance the data analysis process with artificial intelligence (AI). In this course, you'll learn how to utilize AI tools to collect, preprocess, analyze, visualize, and interpret data without the need for extensive coding knowledge.
Learn to gather, manipulate, and analyze real-life data in this course, where you'll gain hands-on experience with Python's NumPy and Pandas libraries. Develop your data visualization skills using Matplotlib, Seaborn, Plotly, and Dash Enterprise, and complete real-life projects that can be deployed online.
Learn the essential skills needed to become a Data Analyst or Business Analyst, including data analysis, data visualization, and statistical analysis. Gain practical experience through real-world projects and prepare for a successful career in the field of data analytics.
Learn Python, SQL, automation, and machine learning to become a Data Scientist. Gain Python programming, data analysis, SQL querying, and predictive modeling skills. Perfect for beginners, this program prepares you for entry-level data science and Python engineering roles. Unlock high-paying job opportunities in the field of data science.
Learn Tableau for data visualization and become a certified Tableau Desktop Specialist in this hands-on certification course.
Learn to extract information from databases by writing SQL queries, joining tables, aggregating data, and filtering results. You'll learn PostgreSQL in this class, but the concepts apply equally to other databases such as SQL Server and MySQL.
Learn Power BI for data analysis & visualization and become a certified Power BI Data Analyst Associate in this hands-on certification course.
Boost your resume with a Microsoft Excel Certification. Our program offers three courses, private training, the Specialist Exam fee with free retake, lifetime access to Excel video courses, and free exam proctoring.
Data analytics is the collection, organization, and analysis of data in order to draw conclusions. Its application spans numerous industries, regardless of their size, as it provides insights into customers’ needs and opportunities for improvement. While humans have been collecting and analyzing data for centuries, the invention of computers assisted with modern data analytics for large-scale data collection, storage, and analysis. Today, people generate large amounts of data daily, and the volume is increasing continuously, thanks to technological advancements that have doubled the capacity for storing information every 40 months since the 1980s. These advancements have helped data analytics become a vital tool in recent times.
The first part of the data analytics lifecycle involves collecting data from large databases. The next stage involves data purification, eliminating duplicate, incorrect, or unusable information. The following step is data analysis, which uses different techniques, including programming languages like Python. Finally, the outcomes must be converted into a format that others can understand. This process is called data visualization and usually involves tools such as Tableau and Power BI.
Data analytics training enables individuals to extract insights from large datasets and make informed decisions across various domains. With this skill set, individuals can embark on many projects or careers, ranging from business intelligence and market research to predictive analytics. For example, professionals can analyze customer data to identify trends to help businesses define their marketing strategies or investigate financial data to uncover patterns and reduce risks.
Data analytics training in Los Angeles also opens doors to applications beyond the professional realm. Individuals can use their skills to analyze personal data, like fitness metrics or spending habits, to make informed lifestyle choices. They can also contribute to societal issues by analyzing public data to understand healthcare and environmental trends. Whether it’s optimizing supply chains or understanding consumer behavior, there are many ways to utilize data analytics.
Mastering skills in data analytics is not just about learning technical tools; it’s about gaining the expertise to navigate the complex landscape of information in a city like LA. From SQL proficiency for efficient data management to critical thinking for analysis, these skills are essential for thriving in Los Angeles’ data-driven industries.
Structured Query Language (SQL) is a programming language for managing relational databases. It is fundamental to data analytics, as it enables analysts to retrieve and manipulate data stored in databases. Learning SQL is essential for Data Analysts and other professionals, as it allows them to perform tasks such as querying databases to extract specific information, joining tables to combine data from multiple sources, and aggregating data to generate summary statistics. Proficiency in SQL is crucial for data analytics professionals, as it forms the backbone of data retrieval processes, assisting with extracting insights from large datasets.
Data visualization involves presenting data in visual formats to facilitate understanding and interpretation. It is a vital skill in data analytics, as it enables analysts to communicate their complex findings in a way that is easy to digest. Analysts can use data visualization to identify patterns, trends, and outliers within datasets, making it easier for people to grasp insights. Moreover, visualizations help convey stories and drive decision-making processes. Mastering data visualization tools is essential for Data Analysts who want to communicate findings to audiences and boost the impact of their analyses.
Statistical analysis is a branch of mathematics that involves collecting, exploring, and interpreting data to uncover patterns. It is a crucial skill in data analytics, allowing analysts to form conclusions from large datasets. Statistical techniques, including regression analysis, hypothesis testing, and correlation analysis, help analysts measure relationships between variables, assess the significance of their findings, and make predictions based on the data. Overall, gaining proficiency in statistical analysis helps Data Analysts identify trends that drive the decision-making processes in many types of organizations.
Machine learning is a subcategory of artificial intelligence that focuses on developing algorithms capable of learning from data and making predictions. Both AI and machine learning are powerful tools in data analytics, as they allow for discovering patterns and relationships within datasets and generating predictive models. Machine learning algorithms, such as decision trees, can be applied to data analytics tasks, including classification, regression, clustering, and recommendation systems. By learning and becoming proficient with machine learning techniques, Data Analysts can unlock the potential of data to optimize processes.
Critical thinking involves honing the ability to analyze, evaluate, and interpret information objectively and logically. Data analytics and critical thinking go hand in hand, as this skill helps professionals assess the validity of findings, identify biases, and make sound judgments based on evidence. Tapping into this type of thinking encourages one to question assumptions, consider alternative perspectives, and scrutinize the reliability of data sources. By cultivating solid critical thinking skills, Data Analysts can navigate complex datasets and ensure their analyses’ accuracy and integrity. Additionally, critical thinking helps analysts approach data-driven problems creatively, opening the door to innovative solutions.
Learning data analytics varies in difficulty depending on prior experience and the specific skills one wants to learn. Gaining familiarity with basic concepts in data analytics, such as understanding data structures, introductory statistical methods, and using standard data analysis software like Excel or SQL, is relatively straightforward and achievable with practice over a few months. However, becoming an expert in data analytics, which entails advanced statistical techniques, programming languages like Python, machine learning algorithms, and data visualization tools, requires significant time and effort that can take several years. Expertise in this subject requires continuous learning to stay updated with developments in the field and gaining experience through real-world projects or work experience. While challenging, becoming proficient in data analytics can be highly rewarding for those passionate about starting a career in this field.
One of the most challenging parts of learning data analytics is mastering the diverse skill set, which includes statistics, programming, data manipulation, machine learning, and data visualization. Each of these areas presents complexities requiring time and effort to grasp thoroughly. Working with datasets can also be challenging due to their inconsistencies and the need for problem-solving skills to clean them. Additionally, staying updated with standard processes in data analytics adds another layer of challenge, forcing one to put time aside regularly to remain competitive. Navigating through these challenges requires perseverance and a commitment to lifelong learning.
The time it takes to learn data analytics will vary between individuals based on factors like prior experience and their chosen learning approach. Generally, gaining confidence in experimenting with basic data analytics concepts might take a few months of consistent study and practice. This level of proficiency enables individuals to perform fundamental tasks like data cleaning, visualization, and basic analysis. However, achieving professional-level proficiency in data analytics, which involves mastering advanced statistical methods, programming languages, and machine learning techniques, typically requires years of continuous learning and experience. Professionals often pursue formal education and specialized training programs and engage in specialized projects to reach this level of expertise. Therefore, while one can start experimenting with data analytics relatively quickly, attaining a professional level of competence is a more extensive and long-term endeavor.
Choosing between in-person and online learning for data analytics depends on individual preferences, learning styles, and overall availability. In-person learning involves attending classes physically at a designated location and offers face-to-face interaction between instructors and peers. This format encourages a collaborative learning environment by providing immediate answers to questions and offering hands-on experience through interactive activities. In fact, data from Pew Research shows that roughly 65% of students prefer learning in an in-person format. However, in-person learning may require a significant time commitment due to commuting and set class schedules.
Live online learning is an option that allows students to attend classes remotely in real-time, interacting with instructors and classmates through virtual platforms like Zoom. This format offers flexibility, allowing learners to access courses from anywhere with an internet connection and eliminating any transportation concerns. Live online learning also enables instant instructor feedback and encourages peer discussions through chat or video calls. However, students may encounter technical issues or distractions while attending virtual classes, and scheduling conflicts may arise if sessions are held at fixed times in a live online format.
Asynchronous online learning provides the most flexibility, as students can access course materials anytime. Learners can study asynchronously, fitting their coursework around personal and professional commitments, meaning this format mainly benefits individuals with busy schedules or who prefer self-directed learning. Additionally, self-paced online courses offer a range of resources, including recorded lectures and interactive exercises, to ensure accessibility among learning styles. However, this type of class requires self-discipline and motivation to stay on track, as there is less interaction with instructors than in-person or live online formats. Overall, the choice between in-person, live online, or self-paced learning for data analytics depends on individual preferences, lifestyle, and learning objectives.
Besides data analytics, several complementary skills can enhance your capabilities and career prospects. One skill is programming, particularly proficiency in languages like Python or R, commonly used for data manipulation and machine learning tasks. Understanding programming languages enables Data Analysts to automate processes, develop custom solutions, and work more efficiently with large datasets. Additionally, becoming comfortable with data visualization tools such as Tableau, Power BI, or Matplotlib in Python can boost your ability to communicate insights through visualizations.
Best practices and domain knowledge are other valuable skills to learn alongside data analytics. Understanding the context in which data analysis is applied, whether in finance, marketing, healthcare, or any other industry, enables analysts to ask questions, identify insights, and provide actionable recommendations. Additionally, focusing on critical thinking, problem-solving, and communication skills can complement data analytics expertise by helping professionals navigate challenges in the field. A general combination of technical knowledge and soft skills can empower individuals to excel in data analytics and make a meaningful impact in their organizations.
Los Angeles, a vibrant city known for its cultural richness, relies heavily on data analytics to drive innovation across numerous sectors. From the finance industry to the entertainment and media world, data analytics plays a crucial role in shaping strategies and driving positive outcomes.
The finance industry in Los Angeles includes sectors like banking, investment firms, and financial services. With the city’s status as a major financial center, data analytics plays a fundamental role in decision-making within this industry. Financial institutions in Los Angeles, such as City National Bank, Oaktree Capital Management, and East West Bank, use data analytics to identify trends, assess risk, and develop investment strategies. This means finance professionals can make informed decisions regarding issues like risk mitigation by leveraging data analytics tools such as predictive modeling and machine learning algorithms. Additionally, data analytics is used for fraud detection, which helps institutions in Los Angeles enhance their operational efficiency.
Los Angeles is known as a hub for the entertainment and media industry, making data analytics a vital component in driving success within this sector. Entertainment companies in Los Angeles, including Disney, Netflix, Warner Bros. Discovery, Paramount, and Universal Studios, utilize analytics to examine viewer preferences, predict audience behavior, and optimize content creation and distribution strategies. Leveraging data from streaming platforms, social media, and audience feedback allows professionals to tailor content to specific demographics, enhance viewer engagement, and maximize revenue. Analyzing data is also key for marketing and advertising purposes, as companies need to identify target audiences and allocate resources efficiently in the competitive landscape of Los Angeles.
Los Angeles is also emerging as a technology hub with an ecosystem of startups and tech companies. Analytics, as a whole, helps drive technological advancements and business growth within this dynamic industry. Technology companies in Los Angeles—such as Snap Inc., Riot Games, Hulu, Google’s LA offices, and SpaceX—use analytics to look into user behavior and improve product performance, among countless other tasks. Analyzing the large amount of data generated from user interactions allows tech professionals to make decisions regarding everything from product development to marketing strategies. Additionally, analytics is used in business areas like cybersecurity, predictive maintenance, and business intelligence.
In Los Angeles, non-profit sectors are vital components of the community, dedicated to addressing societal challenges and enhancing the quality of life for city residents. Within this landscape, digital storytelling is a powerful tool for advocacy. These non-profit organizations, including LA Food Bank, United Way of Greater Los Angeles, and The Skid Row Housing Trust, utilize digital storytelling to raise awareness about pressing issues and foster empathy among those who can help with funding. They can communicate their missions and calls to action through content creation, like videos and interactive presentations. As Los Angeles continues to prioritize social welfare initiatives, digital storytelling remains vital for advancing equity and progressing positive social change.
In the Los Angeles job market, data analytics roles are in high demand across industries and offer lucrative opportunities for professionals with analytical skills. These skills can be anything from crunching numbers to uncovering trends and patterns. Some of the most common professional roles for individuals with the proper training and skills include working as a Data Analyst, Business Analyst, Data Engineer, and Database Administrator.
Data Analysts in Los Angeles are typically responsible for collecting and processing data to provide organizational insights. They work with large datasets, use statistical methods to identify trends, and present findings through visual reports. Data Analysts often collaborate with teams across an organization to understand business needs and develop strategies for data-driven solutions. Salaries for Data Analysts generally range between $60,000 and $115,000 per year, with a median salary of $86,000 in Los Angeles. This role offers room for growth into more specialized positions such as Senior Data Analyst or Data Scientist.
A Business Analyst is a professional who bridges the gap between business objectives and technical solutions by analyzing data and processes. They work to identify areas for improvement and present strategies to enhance profitability. Business Analysts often use their analysis skills for process modeling, understanding business needs, and translating them into actionable plans. Their responsibilities may include creating business cases, defining project scope, documenting requirements, and facilitating communication between different teams. The pay range for Business Analysts typically ranges between $59,000 and $130,000 per year, with a median salary of $87,000 in Los Angeles.
A Data Engineer is responsible for constructing and maintaining the infrastructure that generates and analyzes large volumes of data. They typically use programming languages like Python and tools like Apache to build data pipelines. Data Engineers collaborate closely with Data Scientists and Analysts to ensure data is accessible for analytical purposes. Their responsibilities often include data modeling, database management, ETL processes, and ensuring data quality and integrity. Los Angeles Data Engineers typically earn between $110,000 and $230,000 per year, and this role can offer opportunities for growth into working as a Data Architect, Technical Lead, or positions like consulting or freelancing.
Database Administrators in Los Angeles oversee the management and maintenance of databases to guarantee their security. Their duties include installing database software, monitoring performance, and troubleshooting any issues that arise in these areas. DBAs also implement backup and recovery protocols to safeguard against data loss and enforce security measures to protect sensitive information. In Los Angeles, DBAs find opportunities in many different industries such as technology, finance, healthcare, and government, where they may work as full-time staff, contractors, or consultants. Database Administrators’ salaries in Los Angeles typically range between $49,000 and $112,000.
Noble Desktop’s Data Science Certificate provides a comprehensive approach to mastering essential data science and Python programming skills. This program stands out for its practical focus and expert-led instruction, making it an ideal choice for individuals looking to start a career in data analytics. Through live online sessions, students learn Python, SQL querying, data analysis, and predictive modeling techniques. One notable advantage of this certificate course is the emphasis on real-world applications, where students work on projects that simulate industry scenarios, helping them to build a portfolio of work to showcase to potential employers. Some additional benefits include one-on-one mentoring sessions for personalized guidance and support, where they can address their job search, resume development, and portfolio building. Overall, the Data Science Certificate from Noble Desktop offers a compelling blend of theoretical knowledge and hands-on experience, equipping individuals with the tools and expertise necessary to pursue rewarding data science and Python engineering careers.
There is also Noble Desktop’s Data Analytics Certificate, which equips students with the skills needed for a successful career as a Data Analyst or Business Analyst. The classes provide hands-on training in data analysis, data visualization, and statistical analysis, all essential skills that are highly sought after in industries like healthcare, finance, and education. The course covers predictive and prescriptive analytics, exploring case studies where students utilize regression models and forecasting techniques to predict future outcomes and optimize decision-making. The curriculum is structured around key topics, including Excel for Data Analytics, Python for Data Science, SQL, and Tableau, each offering in-depth training in specific areas of data analytics. Students also benefit from free retakes, setup assistance, payment plans, and one-on-one mentoring, enhancing their learning experience and supporting their career development. The training ensures that graduates are well-prepared to meet the growing demand for data analytics professionals in today’s workforce.
The Data Analytics Technologies Bootcamp from NYIM Training provides an immersive learning experience in data analysis and visualization tools. Available either in-person in New York City or live online, the course provides training in industry-standard software like Excel, SQL, and Tableau. During the bootcamp, students engage in real-world projects, which helps them to gain skills to organize, analyze, summarize, and present data. Each unit is designed to take learners from beginner to advanced levels, covering key topics such as Excel for Business Fundamentals, Intermediate, and Advanced Excel, as well as multiple levels of SQL Server and Tableau. Participants can benefit from small class sizes, personalized setup assistance, and a highly interactive learning environment. The bootcamp does not have strict prerequisites, but attendees should be comfortable working with computers and eager to tackle advanced topics.
Noble Desktop offers corporate data analytics training designed to meet your team’s specific needs. With over 20 years of experience in professional education, Noble Desktop excels in creating tailored curricula that equip your employees with essential data analytics skills. Its corporate training sessions can be conducted onsite at your company’s location or delivered live online via platforms like Zoom, providing flexibility to accommodate your team’s schedules. Companies can also purchase discounted bulk vouchers for attendance in regular classes, allowing employees the freedom to choose from a range of scheduled courses. Contact Noble Desktop today to learn more about the corporate data analytics programs.
Those seeking a chance to sharpen their skills in tech, design, business, or data can look no further than the opportunities in Los Angeles. The city is more than just Hollywood; it’s a collection of professional services and companies that stretch from the innovative companies in Silicon Beach to the more corporate areas of downtown L.A. With data analytics training, you can find work across many of L.A.'s major businesses and industries. In this data-driven world, it pays off to understand the process of examining data for insights that inform business decisions. For those interested, Noble Desktop’s partner school, VDCI, offers classes taught by industry experts who are passionate, willing, and ready to impart their knowledge to students and professionals alike. They offer insight based on their real-world experience, which makes all the difference when learning a new skill from scratch or while looking to advance in your field.
Los Angeles-based learners can take advantage of the training at 3904 Groton Street, San Diego, California, located directly in the Point Loma neighborhood. Driving from Los Angeles, you’ll take I-5 South all the way. Those who prefer public transportation can hop on the Amtrak and commuter rail services connecting to San Diego from L.A., making it a quick way to access the campus. The facility is designed for interaction, offering students access to top-rated instructors and modern workstations.
With over 20 years of experience, Dr. Deanne Larson has built a reputation as a pioneer in the Los Angeles data science and analytics world. As the founder of Larson & Associates LLC, she has partnered with Fortune 500 companies and FinTech firms to help shape AI strategies and implement solutions through machine learning. She’s worked with global enterprises, particularly in designing their data systems. Her accomplishments go beyond the norm, however, as she’s also been part of projects that serve 225 million customers worldwide and others that require developing cutting-edge data governance frameworks for cloud and blockchain technologies. She’s a seasoned instructor as well, having taught at City University of Seattle, where she mentored doctoral candidates and designed data analytics programs. She holds several doctoral degrees that, paired with her extensive experience, position her to teach subjects like AI, Python, data, machine learning, and project management.
Michael Wilson is a Licensed Architect in the State of California and an Autodesk Certified Platinum Revit Instructor, one of only 20 professionals worldwide to hold this title. With over 25 years of experience in architecture, Michael has contributed to award-winning commercial and municipal projects, including facilities for pharmaceutical companies and landmarks such as the El Cajon Animal Shelter and the Mission Hills Library. Michael teaches and develops Revit courses that emphasize real-world application through project-based learning. Over the past decade, he has trained thousands of students in the Los Angeles AEC community, helping them master workflows. Michael is also an Autodesk Certified AutoCAD and Revit Professional, specializing in Revit certification and interior design software.
John O’Malley is a Registered Architect with more than a decade of experience leading Los Angeles teams through all stages of design and construction. His professional background spans different sectors, where he has earned recognition for his technical precision and leadership. A licensed architect in California, John also holds LEED AP BD+C certification and is an Autodesk Certified Instructor (ACI Silver). Most recently, he teaches AutoCAD courses that prepare students to apply drafting and design principles in real-world settings. John is passionate about mentoring the next generation of design professionals, contributing to the ACE Mentor Program.