Dive deeper into data science through machine learning and visualization projects. Learn how coding and analytics combine to reveal new opportunities in data.
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.
Master Python for data analysis, machine learning, and automation. Build predictive models, create dynamic dashboards, and unleash the power of data visualization. Launch your career in data science and Python engineering, equipped with Python, NumPy, Pandas, and Matplotlib.
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.
Master the industry-standard tools and programming languages used by Data Analysts, including Excel, SQL, and Tableau, in this comprehensive classroom training program. Gain hands-on experience working on real-world projects and learn to organize, analyze, summarize, and visualize data to present actionable insights.
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 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 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.
Data Science is a vast field that includes managing and interpreting large amounts of data and providing insight into the best next steps a company can take. Those who specialize in data science should have strong skills in mathematics, coding, machine learning, artificial intelligence, and other data or tech-based skills. This is an increasingly necessary field as most companies need professionals to handle their sensitive and important data.
The idea of data science has been around for decades, beginning roughly in the mid-1970s. Since its emergence, data science has undergone different evolutions and updates and shows no signs of slowing down. Because so many data science positions are available and they pay well, it is becoming one of the most sought-after career paths today.
The majority of people who learn data science do so for professional reasons. With an adequate data science education, students can go on to find ample employment. In addition, most of these jobs pay handsomely and are needed across almost every industry. The modern world is becoming more reliant on data and data interpretations, so data science is an incredibly worthwhile field.
Data science students can not only find numerous jobs after their training, but there is also a lot of variety in the job availability. Because many skills are taught in a data science training course, students can apply these skills to other fields such as business, finance, or healthcare data science. These transferable skills create a more well-rounded portfolio and resume, which is advantageous in the job search process.
Those who have studied data science may choose to specialize in one area or related skills. Plus, data science is ever-evolving. Even experts and data science professionals can benefit from training sessions as there are new practices, industry standards, and new concepts to learn. By adding to their already strong skills with a data science course, professionals can stay up-to-date with the data science field and feel more confident in the workplace.
Some students may find it challenging to learn data science, especially if they have no prior experience. In addition, this field changes quite frequently, so there is always something new to learn. The learning curve can be relatively steep, but with the right training, data science is possible to learn. However, there is a major distinction between familiarity and professional-level skills, so it will be more challenging to become an expert in data science.
Data science can be challenging to some, but difficulty is subjective. Some students may find maintaining data quality relatively challenging. Others may find it hard to manage their time well across different projects. All in all, with practice, time, and dedication, learning data science becomes less challenging.
Data science is an extensive field, so it will likely take considerable time to learn. Students may gain basic skills in a few months, whereas it may take six months to a year for proficiency. Students must remember that it will take longer to become a professional as opposed to becoming comfortable with data science concepts and tools.
Students can find asynchronous, live online, and in-person courses that each come with advantages and disadvantages. Some may choose asynchronous methods, which are essentially self-paced. Students will have guidelines and rough schedules but are on their own to learn the skills and apply them to projects. This option may be better suited to those with previous experience or who are already professionals and simply want an extra bit of training.
Alternatively, there are ample live online options. These provide more interaction and give students access to additional benefits. Students can learn alongside their peers and ask their instructor questions that can be answered in real-time. While some live online classes require students to purchase their own materials, they remove the need for transportation and a commute. This is ideal for those with busier schedules and those who live in more remote areas.
In-person classes are generally regarded as the most effective learning method for several reasons. First, they provide ample interaction between the student, their peers, and the instructor. Students can ask questions, receive feedback, and get direct assistance with any issues or troubleshooting tasks. This course option requires students to have time each week to make a commute, which is challenging depending on factors like transportation, location, and schedule, but this is an interactive and hands-on learning method, which may be necessary for a field like data science that has many in-depth and complex concepts to learn.
Aspiring Data Scientists can gain more insight into the field through free online resources. There are social media pages, websites, and video collections where students can learn more about data science, from troubleshooting and theory to new tools and updates in their specific industry. Noble Desktop, for example, has a free seminar page with dozens of pre-recorded lectures on different topics and tools. While these are incredibly useful, it is important to remember that these resources are supplemental. Professional skill development can only be achieved through an official training course.
Many data science students learn additional skills to help with their specialization or as a way to explore different job roles and fields. For example, many learn UX design and related applications or tools. Data Science and UX design both require strong communication and problem-solving skills. In addition, they both require some level of data analysis and being able to understand how trends and patterns affect a user.
In addition, many choose to learn data science within a particular industry. Some may choose to learn more about business tactics or financial concepts. In addition, many work in marketing so they may learn different marketing concepts and tools like those under Adobe Creative Cloud. In addition, they often have strong mathematical skills.
Many top San Francisco industries rely on data science specialists for smooth business operations. Companies within the technology industry hire Data Scientists regularly to extract data, use machine learning, and provide advice on the best business practices. In addition, manufacturing companies use data science to ensure a streamlined production process. Through data analysis, manufacturers can improve materials, cost, and shipping. The tourism industry hires data science professionals to follow market trends and gain more insight into a marketing campaign's effectiveness. Lastly, the healthcare industry uses data science to manage patient data, infer the best treatment options, and reduce costs within the institution.
Those who study data science can find employment across many industries. Below are a few key job titles and their respective salaries.
A Data Scientist works in a broad field that includes data management, forecasting, resource allocation, and enhancing overall decision-making. They are usually experts in coding languages like Python, SQL, and R and will have strong skills in statistics, calculus, algebra, and probability. They earn a salary ranging from $90,000 to well above $200,000, depending on their experience, level of education, and the company for which they work. In general, the average salary for a relatively new Data Scientist is $120,000.
Additionally, Machine Learning Engineers have strong data science skills. They work on computer programs and ensure they operate properly without much human interaction. They work with artificial intelligence, write algorithms, write and review code, and collaborate with their teammates. In addition, Machine Learning Engineers earn a lucrative salary mainly determined by things like experience, education, and the size of the company. Their salary generally ranges from $144,000 to $188,000. The U.S. average is closer to $166,000.
Python Developers typically work on the back-end of websites, build servers, complete data analysis, and create automation scripts. They will also have a strong understanding of data science and product development. In addition, they may work on a team or independently and will often spend their days writing code and APIs as well as incorporating user-interface elements on a website or an application. In addition, they earn a salary of anywhere from $117,000 to $125,000. However, they can earn even more depending on their skill sets and level of expertise.
A Data Analyst reviews large quantities of data from which they gain insights that can aid in improving marketing campaigns or company profits. They generally work with other data professionals such as Data Scientists, and can find employment across all industries. They spend their days gathering, organizing, and analyzing data. Once they clearly understand how to handle data, a Data Analyst will visualize it and share their findings with clients or others within the organization. Their salary lands around $77,000 but can range anywhere from $69,000 to $151,000, which ultimately depends on different factors.
The ||CPN411|| from Noble Desktop teaches beginner-level students machine learning, automation, Python, SQL, predictive modeling, and data analysis. Through hands-on projects and ample instruction, students will learn how to manage databases, analyze data, and navigate data science libraries such as Seaborn, Plotly, and Matplotlib. In addition, they will create machine learning models and data visualizations. Offered in a part-time or full-time format, students can access additional benefits and resources such as setup assistance, payment plans, and six additional mentoring sessions. Noble Desktop also offers a free retake of this course up to a year after the original enrollment date. Plus, students will receive their verified digital certificate of completion that serves as proof of their accomplishments.
Noble Desktop also provides a Python ||CPN633||. In this course, students will learn linear and logistic regression analysis, practice classification algorithms, and use data science libraries such as Pandas and NumPy. The projects throughout this bootcamp offer students insight into the world of data science as they practice completing real-world machine learning skills. Students should have experience with Python, NumPy, and Pandas before enrolling. Additionally, students can take advantage of setup assistance, bonus training, and a free retake of the course. Once the class has ended, students will also receive their verified certificate.
Additionally, SynergisticIT has an Introduction to ||CPN416|| course where students learn theory behind data science and analytics and other technologies and tools. Students will learn problem-solving tips and tricks in addition to completing projects that provide insight into the data science field. Students will build resource plans and track their progress throughout these projects. This course is ideal for anyone who works in data, mathematics, logistics, or software development. Students will also earn a certificate once the course ends.
General Assembly offers a Data Science Short Course that teaches students Python, data analysis, data modeling, machine learning, data visualization, and how to manage large quantities of data. Those who apply should feel comfortable with programming basics and basic statistics. They should also know basic Python syntax as well. Students can choose from different payment plans and may qualify for discounts. Notably, General Assembly also awards each student with a certificate of completion.
There are data science courses offered at Data Mites as well. For instance, their ||CPN412|| in San Francisco covers data visualization tools like Tableau and coding languages like Python and R. Additionally, students will have access to Data Mites data science cloud lab where they can get more practice. Once they have successfully completed the course, students will receive a certificate of completion to show for their efforts.
Students can also learn machine learning from Full Stack Academy in their 26-week AI & ||CPN633||. Throughout six units, students will learn basic statistics, programming languages like Python, data wrangling, machine learning, and generative AI. This course prepares students for data-driven fields such as business analytics or AI engineering. No experience is technically required, but Full Stack Academy suggests students should have some coding experience, strong mathematical skills, and related employment will benefit most of all.
Has your organization considered searching for data science training options? Your team can choose from many options that can suit your collective needs. Most education centers offer in-person and live online courses, like Noble Desktop. The corporate training at Noble Desktop will help your team feel more equipped to handle day-to-day tasks in the workplace.
In addition to diverse and thorough corporate training sessions, Noble Desktop allows your organization to purchase discounted bulk vouchers for attendance in regular classes. This ensures your team members learn as much as they possibly can. To see your employees thrive and to promote more collaboration and communication, consider contacting Noble Desktop at corporate@nobledesktop.com to learn more about training options, pricing, and scheduling.