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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.
Gain practical SQL skills in this hands-on certification program, where you’ll learn to write queries, manage data, and analyze results using Microsoft SQL Server.
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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 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.
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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.
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Data analytics is the process of organizing and analyzing raw data to turn it into actionable insights. In other words, a Data Analyst notices trends in different datasets so they can, in turn, help their clients or companies make the most well-informed decisions. They apply statistical techniques to understand and further convey their findings to others. Data analytics is integral to businesses that want to see sustained growth and become real competitors in the market, so they are employed in almost every industry. Additionally, data analytics includes visualizing the data through charts, graphs, diagrams, tables, and other visual representations. Data visualization software such as Tableau or Microsoft Excel helps users create these with ease. Those who work in data analytics frequently use programming languages like Python and SQL. Additionally, these professionals should have strong mathematics skills, so learners may choose to study statistics, algebra, or calculus.
With proper training, it is possible to enter several fields as a Data Analyst. For instance, a Business Analyst can provide data-driven solutions to improve sales, forecast market changes, and manage stock trends. A marketing professional can help garner a more positive social media response and determine campaign effectiveness. Financial Analysts also manage a company’s stocks, investments, profits, bonds, and other kinds of financial handling. There is a wide variety of data analytics, so there are ample options to suit an individual’s needs and proclivities.
Similarly, those in other fields such as marketing, business, or finance, could benefit greatly from learning basic data analytics and visualization tools. For example, many marketing and business professionals learn data analytics and visualization tools. Some finance professionals may do the same. While it may vary from position to position, having a wide range of skills could lead to more responsibility and career advancements. All of this is to show that learning data analytics is an incredibly transferable and useful skill, even if that is not an individual’s specific field.
Depending on the level of advancement, most data analytics classes will teach a few core concepts, tools, and techniques. Data analytics requires learning data visualization and utilizing software such as Tableau, Microsoft Excel, or Microsoft Access. More importantly, it requires learning strong communication skills so the information from the data can be shared clearly and effectively with an intended audience. Additionally, data analytics classes often teach or use certain coding languages like SQL and Python. As previously stated, Data Analysts need strong skills in mathematics such as statistics, algebra, and calculus.
Learning data analytics comes with challenges, particularly for absolute beginners. However, with the right tools and proper training, data analytics becomes less challenging over time. In addition, there is a considerable difference between gaining foundational skills and becoming a professional in data analytics. Learning basic skills will be much easier to achieve, regardless of your previous experience. Becoming an expert will come with more challenges than learning the basics. However, the level of difficulty will ultimately differ from person to person. Some with a propensity for mathematics or computer skills may pick up on data analytics more quickly than those with minimal experience.
It is hard to pinpoint the exact time it takes to learn data analytics; however, most can learn the basics within three months. The reason it is difficult to determine a learning timeline is mainly because this field encompasses different concepts, tools, and technologies. It may take less time to learn Excel compared to Tableau for some. Additionally, learning multiple coding languages may take its own undetermined amount of time. Plus, it is important to remember that advancing in your training means spending more time and effort on your studies. To fully master data analytics will likely take multiple years.
Data analytics students often learn additional skills to help them through their career paths or afford them new professional opportunities. Once you have gained sufficient knowledge of data analytics skills and technologies, many choose to broaden their skill sets. Data Analysts often use tools like Microsoft Excel, Tableau, Power BI, and Google Analytics, so it will be beneficial to know software and applications that are related to or used alongside them. For instance, Microsoft Office applications like PowerPoint can help users explain data and present their findings in a clearer way. Some may choose to learn Google Charts or other useful visualization tools that can assist in their data analytics process.
Many data analytics experts also learn adjacent skills like project management. Because some data analysis positions require professionals to handle different clients at once, they must become experts at managing their time and meeting deadlines and expectations. Additionally, any effective Data Analyst must have strong communication skills, so some may choose to take a business writing course to learn relevant terminology and jargon. Alternatively, some may practice applying design principles to create effective data visualizations. This may include learning particular software or simply understanding how alignment, balance, negative space, and proximity can help make all the difference with an effective presentation or data visualization.
Detroit has various industries that regularly hire data analytics experts. The automotive manufacturing industry is well-known in the Detroit area and requires data analytics for improving quality control, managing products, identifying issues, and improving the customer’s overall experience. Similarly, healthcare Data Analysts improve patient outcomes and experience as well as help healthcare providers ensure the best care for those in the community through enhanced treatment plans and more insight into regional health trends. Financial Analysts are highly sought after in financial institutions as they help investment agencies, banks, and insurance companies alike gain more insight into market trends, stock prices, company budgets, and other financial concepts. Lastly, the retail industry needs data professionals to help improve customer relations. Data analysis can also help improve marketing campaigns, thus driving more sales and consumer interaction.
Learning data analytics can open doors to many exciting, rewarding, and highly regarded career paths. Below are just a few of the many job titles that a data analytics professional may obtain, but there are myriad options. Additionally, the salary ranges listed are generalizations. An individual's salary may vary depending on an individual’s previous experience or level of education as well as the industry and size of the company.
With adequate training, many seek a professional position as a Data Analyst. In this role, an individual will condense, organize, analyze, and visualize different datasets from which they can provide advice and guidance. They often work on teams and work in virtually every industry that relies on data storage and maintenance. A Data Analyst often spends their workday analyzing data and sharing it with interested groups within the organization. This may include maintaining databases, tracking trends, and creating reports. They typically earn a reasonable salary that ranges anywhere from $67,000 to $106,000, depending on the aforementioned factors.
A Machine Learning Engineer spends their time researching, designing, and maintaining the artificial intelligence that allows computer programs to run without direct commands from humans. The goal is to help the computer grow and continue to improve over time with minimal effort. Those in this position combine programming skills with data science and ultimately create user-friendly and highly effective computer programs. They often spend their workweek writing algorithms, running tests, and performing data analysis. Machine Learning Engineers earn an average salary of $165,000 but can realistically expect a yearly wage anywhere between $144,000 and $188,000, making it a highly sought-after and desirable career path.
Data Engineers are tasked with designing, building, maintaining, and optimizing the data infrastructure needed for data collection, storage, access, and analysis. They often specialize in a particular skill such as databases or data pipelines but can also have a general skill set. Data Engineers often work alongside other data professionals and developers and can be found in various industries. Some common tasks for a Data Engineer include writing queries, developing data pipelines, coding, and meeting with others in their department. The average salary for a Data Engineer in the United States is close to $127,000. However, most Data Engineers can expect to take home $107,000 to $157,000 each year depending on various factors.
Data analytics is merely a subset of data science, so many who learn analytics can find employment as a Data Scientist. These data professionals can analyze and visualize data, but must also find new ways to collect, store, manage, and analyze said data. In essence, they are tasked with finding new ways to not only analyze and interpret data but handle data. Contrary to Data Analysts, most Data Scientists have strong object-oriented programming skills and use machine learning frequently. They can often be found researching, testing theories, predicting outcomes based on trends, and creating data visualizations. The average Data Scientist in the United States earns roughly $130,000 per year but the salary ranges anywhere from $90,000 to well above $200,000 with proper training and adequate experience.
Noble Desktop offers a ||CPN395|| course where students will complete projects based on real-world scenarios, providing them with ample experience and practice. The instructor will guide students through the data analytics process, including how to collect, clean, analyze, and visualize data. Students will use predictive and prescriptive analysis for optimum decision-making. Students will earn eight additional mentoring sessions where they can review their portfolios and ask for clarification on tricky subjects. In addition, students can retake the course within a year at no additional cost. Students will also earn a verified digital certificate of completion to show for all their hard work.
There are also bootcamps provided by Michigan State University that cover data analysis concepts in-depth. Their Data Analytics Bootcamp is a project-based course intended to prepare students for the professional field. Students will complete six modules that cover multiple subjects and tools, like Excel and Tableau for data visualization, numerous databases, web development tools, and frameworks, machine learning, and coding languages like Python, SQL, HTML, CSS, and JavaScript. Students will have access to around-the-clock academic support for portfolio development and will receive free resources to help them with their careers. Upon completion of the course, students will earn a certificate from MSU’s College of Engineering.
Datamites Global Training Institute also hosts a Power BI course where students will complete various projects and learn all about data modeling, data visualizations, Python in Power BI, KPIs, Query Editor, and Excel. The instructor will show students how to create data visualizations like graphs, charts, and tables throughout the course. Additionally, students will have access to extra classroom assistance and free supplemental video collections. Plus, students can choose from different payment options and partake in career support like resume building and mock interviews. This course also prepares students for the International Association of Business Analytics Certification in data analytics.
The Tableau Desktop: Part 1 course at New Horizons provides students with experience using Tableau. Students will learn how to collect, organize, and manage data as well as create custom visualizations that display said data. In addition, they will learn how to connect Tableau to different sources and configure basic functions in Tableau. Additionally, they will create and optimize their dashboards with this data visualization software. Students will have access to resources even after the class ends and will feel confident using Tableau for future professional endeavors by the time they leave the course.
Noble Desktop provides corporate training that will help your department feel more united and successfully communicate with one another. Additionally, corporate training also optimizes workflow and helps reduce errors, which ultimately result in lost time. Your group can choose from in-person and online options, depending on what works with your specific learning needs and preferences. Plus, Noble Desktop offers your organization the opportunity to purchase discounted bulk vouchers for attendance in regular classes. To learn more about your scheduling, training, and pricing options, email Noble Desktop at corporate@nobledesktop.com.