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 combines several disciplines to extract knowledge and insights from data. It's an interdisciplinary approach that uses scientific methods, processes, algorithms, and systems to make data usable and meaningful. Data Scientists use their skills to ensure the data is high quality and ready for analysis, which involves collecting, cleaning, and organizing data from various sources. Statistical methods, machine learning algorithms, and data visualization tools are used to analyze the data. Core areas of data science are statistics and probability, machine learning, data visualization, and programming languages.
The term "data science" can be traced back to the 1960s. In 1962, mathematician John W. Tukey described a field he called "data analysis" that foreshadowed modern data science, emphasizing the use of computing for data exploration. The 1980s witnessed the emergence of the term "data science, " which started gaining momentum in the 1990s. The explosion of data volume, variety, and velocity marked the dawn of the big data era in the twenty-first century. As data science becomes integrated across various industries, its impact on decision-making and problem-solving is expected to grow exponentially.
Data science training equips you with a powerful skill set that goes beyond the professional realm. Data science can be a powerful tool to manage your personal finances. Utilizing your knowledge and some easy-to-use tools, you can gain valuable insights into your spending habits, optimize your budget, and even predict future financial needs. For a more advanced approach, you can use linear regression or other machine learning techniques to forecast future expenses based on your historical data. This can help you plan for upcoming bills and unexpected expenses.
Data science can be a powerful tool to complement your health and fitness journey. Sync your fitness tracker or smartwatch with data analysis tools. You can leverage Python libraries like Pandas to extract and analyze data on steps taken, distance covered, calories burned, heart rate, and sleep patterns. If you're training for a specific event, data science can help you evaluate your performance. Analyze metrics like pace, distance, or strength gains to see how your training program impacts your fitness level. Additionally, you can track your calorie intake using an app and combine this data with your activity data to understand your daily calorie expenditure.
If you are into sports, you can dive into the world of sports analytics. You can scrape data on player performance, game statistics, or historical trends. Analyze this data to uncover hidden patterns, predict game outcomes, or evaluate player performance metrics.
In a data science class, you'll acquire a strong foundation in technical skills that equip you to tackle data challenges.
R is a powerful language specifically designed for statistical computing and data visualization. You'll learn R if statistics is emphasized heavily in your data science course, particularly if it's focused on the social or biological sciences. R's visualization libraries, like ggplot2, are known for their elegance and flexibility. If data visualization is a core component of the class, R could be the chosen tool for learning visualization techniques.
Python is a general-purpose language that's beginner-friendly and easy to learn, even for those with no prior programming experience. This makes it accessible for students new to data science. It is widely considered the dominant programming language in data science. It has a rich ecosystem of libraries specifically designed for data science tasks. Libraries like NumPy for numerical computing, Pandas for data analysis and manipulation, and Matplotlib for data visualization are fundamental tools for Data Scientists, and you'll likely learn them in a data science class.
SQL is considered one of the most essential skills for Data Scientists. It is the standard language for interacting with relational databases, which are the most common type of database used to store structured data. Data science projects often rely on retrieving and manipulating data from these databases, making SQL a fundamental skill. While the depth of SQL coverage might vary depending on the class, you'll gain a solid understanding of the fundamentals and its role in the data science workflow.
Google Colab is a free, browser-based Jupyter Notebook environment specifically designed for data science tasks. This makes it a popular platform for educators due to its accessibility and ease of use. Students don't need to worry about installing software or configuring environments, allowing them to focus on learning data science concepts. Many data science classes prioritize teaching core data science concepts and techniques rather than specific platforms. Google Colab serves as a suitable tool to practice these concepts within the familiar Jupyter Notebook interface used extensively in data science.
Hadoop is a foundational technology in the world of big data. It provides a framework for the distributed storage and processing of large datasets across clusters of computers. If your data science class has a module on big data, Hadoop might be introduced as a prominent example of a big data processing framework. This would provide context for the challenges and solutions involved in handling massive datasets.
Data science can be challenging to learn, but the difficulty depends on your background, goals, and the specific areas you delve into. Grasping the fundamental concepts of data science, including data analysis, visualization, and basic programming, can be achievable with dedication, even for beginners. Focusing on advanced cutting-edge research areas in data science or building complex data science systems requires a strong foundation and continuous learning. Ensure you have a solid understanding of math, statistics, and programming before diving into advanced topics.
Even for the enthusiastic learner, data science presents some hurdles. Data science is a blend of statistics, probability, mathematics, computer science, and sometimes domain-specific knowledge. If you're new to these areas, grasping the foundational concepts can feel overwhelming. While the specific languages might differ, data science relies heavily on programming to manipulate, analyze, and model data. Python is number one in the field, but R and SQL are also commonly used, so if you're new to coding, this can be a significant hurdle.
The time it takes to learn data science depends on several factors, making it difficult to give a one-size-fits-all answer. Factors that affect learning time are your prior knowledge, learning pace, and course quality. Grasping fundamental concepts like data analysis, visualization, and basic programming can be achievable with dedication, even for beginners. More advanced specialization in data science or building complex data science systems requires a strong foundation and continuous learning. This might involve university degrees, research programs, or significant industry experience.
Both in-person and online learning have their pros and cons for data science, and the best choice depends on your learning style, goals, and lifestyle factors. In-person courses offer a set schedule and classroom environment, which can help you stay focused and motivated, especially if you thrive on routine and structure. Some in-person courses might offer dedicated lab sessions or practical exercises with in-person guidance, facilitating a more hands-on learning experience. Alternatively, in-person programs often come with higher tuition fees compared to some online options, in addition to additional costs for commuting or accommodation.
Live online learning offers a compelling alternative to traditional in-person data science courses, combining the flexibility of online learning with some of the benefits of a structured classroom environment. Similar to in-person learning, you can attend classes from anywhere with an internet connection. This allows you to learn at your own pace and schedule the coursework around your work or personal commitments. Unlike pre-recorded lectures, live online courses allow you to interact directly with instructors during class sessions. You can ask questions, clarify doubts, and get immediate feedback, similar to a physical classroom setting.
Asynchronous learning is where students can access course materials and complete assignments on their own schedule without having to attend live lectures or participate in real-time sessions. This is great for students who need to fit learning around their work, family, or other commitments. To solidify understanding, lectures, readings, or other materials can be revisited as often as needed. Asynchronous learning requires students to have strong self-motivation and time-management skills to stay on track and complete assignments. There is also less opportunity for real-time interaction with instructors and classmates compared to synchronous learning.
While some advanced data science programs or specialized certifications might require a fee, there's a wealth of valuable free content available online to kickstart your data science journey. Many websites and blogs publish articles, tutorials, and resources specifically for data science learners. These can be a great way to stay updated on trends, learn new techniques, and get insights from practicing data scientists. Several data science educators and enthusiasts have YouTube channels, like Noble Desktop’s YouTube channel, with high-quality tutorials, project walkthroughs, and data science concepts explained engagingly. These can be a great way to learn at your own pace and explore specific topics.
Alongside data science, there are several valuable skills and areas of knowledge that can complement your expertise and make you a well-rounded Data Scientist. Data science projects often involve working with large datasets and require significant computing power. Understanding cloud platforms like Google Cloud Platform (GCP), Amazon Web Services (AWS), or Microsoft Azure allows you to leverage scalable and cost-effective solutions for data storage, processing, and analysis. Data science projects often involve collaboration and code sharing. Learning a version control system like Git helps you track changes, collaborate effectively with others, and revert to previous versions if needed.
While data science skills are transferable, specializing in a particular domain (healthcare, finance, marketing) can significantly enhance your value. Understanding the specific challenges and data landscape of a domain allows you to tailor your analysis and provide more relevant insights. Also, being able to build a narrative around your data analysis is a powerful skill. Learning how to present your findings, not just as charts and graphs, but as a story, resonates with your audience and drives decision-making.
The healthcare industry encompasses a wide range of services and professionals involved in improving a person's health and well-being. The healthcare industry in Atlanta is a thriving sector that's been dubbed the "center for global health." It's a hub for medical research, education, and innovation, attracting leading healthcare organizations, world-renowned medical schools, and a strong talent pool.
Data science is transforming the healthcare industry by enabling data-driven decision-making, improving patient care, and fostering new discoveries. Data science models can analyze patient data (medical history, genetics, lifestyle habits) to identify individuals at high risk for certain diseases. This allows for early intervention, preventive measures, and personalized screening programs. Data science can also predict which patients are at high risk of being readmitted to the hospital after discharge. This allows healthcare providers to implement targeted interventions and care plans to reduce readmission rates, leading to better patient outcomes and lower costs.
Financial technology, also known as FinTech, refers to the use of technology to improve and automate the delivery and use of financial services. It encompasses a wide range of innovations that are transforming how consumers and businesses manage their money. Atlanta's FinTech industry is expected to continue its growth trajectory. With a strong foundation, a supportive environment, and a constant influx of talent and innovation, Atlanta is well-positioned to maintain its status as a leading FinTech hub in the years to come.
The FinTech industry heavily relies on data science to innovate and improve financial services. Data science helps analyze customer data (transaction history, financial goals, risk tolerance) to personalize financial products and services. This could involve recommending suitable investment options, tailoring loan offers, or creating custom budgeting plans. Financial institutions also use data science models to assess and manage various market risks such as credit risk, liquidity risk, and operational risk. These models help them make informed investment decisions and ensure financial stability.
The retail industry refers to the process of selling goods to consumers, from manufacturing or purchasing them from wholesalers to delivering them to the final customer. The Atlanta retail industry is well-positioned for continued growth. With its favorable demographics, diverse offerings, and adaptability to changing consumer trends, Atlanta remains a vibrant retail hub in the Southeast.
Data science is transforming the retail industry by enabling data-driven decisions that enhance customer experience, optimize operations, and boost profits. It helps analyze customer purchase history, browsing behavior, and demographics to personalize product recommendations, marketing campaigns, and promotions. This leads to a more engaging shopping experience and increases the likelihood of conversion. Data science algorithms can analyze market conditions, competitor pricing, and customer demand to set dynamic prices for products. This ensures retailers remain competitive and maximize profit margins.
The manufacturing industry is the backbone of many economies, transforming raw materials into finished goods. The manufacturing industry in Atlanta has a long history, dating back to the 1800s. Today, Atlanta is a major center for manufacturing, with a diverse range of industries represented. For example, it is a hub for transportation equipment manufacturing, particularly automobiles. The presence of major manufacturers like Kia Motors and Hartsville International Distributors (HID) contributes significantly to this sector.
Data science is revolutionizing the manufacturing industry by transforming data into actionable insights that optimize processes, improve quality control, and predict potential issues. For example, data science models can analyze sensor data from machines to predict equipment failures before they occur. This allows for preventive maintenance, reducing downtime, and saving on repair costs. Data science can also analyze data on workplace accidents to identify potential safety hazards, allowing manufacturers to implement preventive measures and improve workplace safety.
The technology industry encompasses a wide range of companies that develop, manufacture, support, and research various technological products and services. The tech industry in Atlanta has been booming in recent years, earning the nickname "The Silicon Valley of the South." The city is now a recognized hub for innovation and entrepreneurship, attracting major tech companies, startups, and a skilled workforce. Atlanta boasts a thriving startup culture, with numerous incubators, accelerators, and co-working spaces that nurture young tech companies. Also, the presence of universities like Georgia Tech and Emory University provides a steady stream of well-educated talent for startups.
The tech industry is perhaps the most reliant on data science, as data is the lifeblood of many technological advancements. Data science is used to personalize user experiences across various tech platforms, from social media feeds and search engine results to recommendations on streaming services and online stores. By analyzing user data (search history, browsing behavior, purchase history), data science personalizes content, recommendations, and advertising, leading to a more engaging user experience. Data science is also fundamental to the development and training of AI and ML models. These models require vast amounts of data to learn and improve their capabilities, and data science techniques are used to prepare, clean, and analyze data for AI/ML applications.
The entertainment industry encompasses a broad range of activities that aim to entertain and engage audiences. It involves the creation, distribution, and consumption of various forms of entertainment products and services. Atlanta has become a major player in the entertainment industry, earning the nickname "Hollywood of the South." The city has a thriving film and television production scene, a strong music industry presence, and a growing reputation for gaming and interactive media.
Data science is revolutionizing the entertainment industry by leveraging vast amounts of data to improve content creation, enhance audience engagement, and optimize distribution strategies. Streaming services leverage data science to recommend movies, shows, and music tailored to individual user preferences. This personalization keeps users engaged and exploring new content within the platform. Another example is how data science plays a role in A/B testing different aspects of content delivery such as thumbnails, titles, or even video snippets, to see which version performs better with audiences. This helps optimize the user experience and content discoverability.
The data science field is rapidly growing, and the demand for qualified professionals is high. With the right skills and experience, you can find a rewarding and impactful career in data science. Below are just a few of the many data science jobs available. The specific requirements and titles may vary depending on the industry and organization. Different options include industry-specific data science roles, core data science roles, and specialized roles.
A Data Scientist is the most general role, encompassing tasks like data collection, cleaning, analysis, model building, and communication of insights. Data Scientists are the backbone of data science projects, translating business problems into solutions through data analysis.
Data science follows a general workflow, often referred to as the data science pipeline. The pipeline begins with data acquisition, where Data Scientists first identify and acquire the data they need for a specific project. The next step is data cleaning, where Data Scientists clean the data to ensure its accuracy. Once the data is clean, Data Scientists perform exploratory data analysis to understand its basic characteristics, identify patterns and trends, and formulate hypotheses for further investigation. The pipeline continues into data modeling, model evaluation and refinement, and lastly, Data Scientists use data visualization tools and clear communication to present their findings.
Since Atlanta has a growing tech industry, with a high concentration of universities and research institutions, it produces a skilled data science workforce. A Data Scientist in Atlanta makes between $78,000 and $140,000 per year.
A Business Intelligence (BI) Analyst is a data professional who helps organizations translate raw data into clear, actionable insights to inform business decisions. They act as a bridge between the world of data and business strategy. BI Analysts identify and gather data from various sources like internal databases, CRM systems, marketing automation platforms, sales figures, and external market research data. They ensure the accuracy and completeness of the data, which often involves cleaning and organizing messy datasets.
Data science plays a crucial role in business intelligence by providing the tools and techniques for extracting valuable insights from data to inform better decision-making. Data science helps business intelligence go beyond basic reporting by uncovering hidden patterns and relationships within data, leading to more actionable insights. On a more advanced level, data science algorithms, like clustering, can be used to uncover hidden patterns and relationships within large datasets. These insights can be crucial in business intelligence for identifying customer segments, optimizing marketing campaigns, and detecting fraudulent activities.
Atlanta is a great hub for BI Analysts, with a thriving tech industry and a growing demand for data-driven decision-making. In Atlanta, a BI Analyst can make between $80,000 and $120,000 per year.
A Data Analyst is a problem-solver who uses their skills to extract meaningful insights from data. They collect, clean, analyze, and interpret data to identify trends, patterns, and valuable information. This information is then used to inform business decisions, improve processes, or answer specific questions. Data Analysts are the backbone of many data-driven organizations. They play a crucial role in transforming raw data into actionable insights that can improve business performance, optimize marketing campaigns, or gain a deeper understanding of customer behavior.
Data science empowers Data Analysts with a toolkit of techniques and skills to extract deeper insights from data that can inform better decision-making. While Data Scientists might build the initial data pipelines, Data Analysts often use data wrangling techniques like sorting, filtering, and identifying trends to explore and understand data from various sources like databases, customer surveys, and sales figures. Data science skills, like data mining, can be used to unearth hidden patterns within this data. Data science provides a strong foundation in statistics, allowing Data Analysts to go beyond basic descriptive statistics. They can leverage statistical methods like hypothesis testing to assess the significance of findings, adding a layer of credibility to their analysis.
Due to Atlanta's thriving data scene, Data Analysts are in high demand across various industries. They can make between $78,000 and $118,000 per year.
In a world overflowing with information, Data Journalists act as translators, using their data analysis skills to uncover stories hidden within numbers. They combine journalism with statistical analysis to bring data to life in a way that informs and engages the public. Data Journalists don't simply report raw numbers. They act as detectives, sifting through large datasets (like government reports, public records, or social media data) to find patterns, trends, and anomalies. They collaborate with Data Scientists and Data Analysts to access and understand complex datasets.
Not simply for number crunching, Data Journalists use data science approaches throughout their investigative process. For example, data visualization is a core strength of data journalism, and data science can provide advanced tools and techniques. Data Journalists can leverage data science libraries and software to create interactive visualizations that tell a compelling story. Techniques like heat maps, network graphs, and geospatial visualizations can make complex datasets easier to understand for the public.
Atlanta offers a thriving environment for Data Journalists with data analysis skills. You can raise your chances of landing data journalism opportunities by networking and building a solid portfolio. Data Journalists make between $67,000 and $87,000 annually.
A Machine Learning Engineer (MLE) is the bridge between the world of data science and the real-world. They take the complex models and algorithms created by Data Scientists and turn them into functioning applications that businesses and individuals can use.
While Data Scientists might identify and specify data requirements, MLEs play a crucial role in data acquisition. They collaborate with Data Engineers to build or utilize data pipelines to extract data from various sources and ensure it's in a usable format for machine learning models. MLEs understand the problem statement and the chosen machine learning algorithm. They then translate the data science model into production-ready code, ensuring it can handle large datasets efficiently. This might involve using specialized machine learning libraries and frameworks like TensorFlow or PyTorch.
Atlanta's tech industry caters to a wide range of sectors, including healthcare, finance, logistics, and manufacturing. Each industry has unique applications for machine learning, further amplifying the demand for MLEs who can develop specialized models. An MLE in Atlanta can make between $98,000 and $149,000 per year.
A Marketing Scientist is a business professional who blends marketing expertise with data analysis skills to solve marketing problems and optimize campaigns. They leverage the power of data science to understand customer behavior, predict trends, and measure the effectiveness of marketing initiatives.
Data science techniques like clustering algorithms allow Marketing Scientists to segment customers into distinct groups based on shared characteristics, behaviors, and needs. This helps tailor marketing messages and offerings to resonate better with each segment. Also, recommending products that customers are likely to buy next is a marketing sweet spot. Data science algorithms can analyze purchase history and browsing behavior to suggest relevant products, increasing conversions and customer satisfaction.
To improve your chances of gaining a position as a Marketing Scientist, you can tailor your resume and cover letter to emphasize your marketing expertise alongside your data science proficiency. It is also beneficial to mention relevant marketing channels (social media, email marketing), marketing analytics tools, and your experience with data visualization. A Marketing Scientist in Atlanta makes between $67,000 and $89,000 per year.
Emory University offers a Business Intelligence for Data Science and Visualization Program for students to enhance their data analytics and visualization skills. Coursework includes learning how to gather, extract, mine, analyze, visualize, and present business data using tools like Advanced Excel, SQL, Microsoft Power BI, and Tableau. With practical projects and assignments, their applied learning approach enables students to apply their knowledge in authentic settings. On Saturdays, they provide live online classes that are all recorded for review.
Data Science Training in Atlanta is offered by SynergisticIT. The course provides thorough instruction in data science that conveys mastery of data science technologies. It provides students with practical experience applying data science concepts. Training is led by certified Data Scientists with over ten years of experience in the industry. A well-crafted program revolves around a variety of interdisciplinary skills, including artificial analysis, Python, data structures, and data analysis.
By enrolling in the Emory Data Analytics Bootcamp powered by Fullstack Academy, students will gain the experience, knowledge, and career guidance needed to find a position as a Data Analyst. Part-time and full-time program options are offered on-campus in Atlanta, GA. Students in the bootcamp will gain the most recent abilities and expertise to traverse and examine real-world datasets. In the program, students will gain proficiency with the most popular analytics tools on Amazon Web Services and learn advanced features and formulas in Microsoft Excel, to name a few topics.
Take Data Science Bootcamp through General Assembly and create a portfolio of projects highlighting the lessons students have learned in each unit. A capstone project will involve students working through a real-world data problem from start to finish. They will identify and gather pertinent data, create a pitch and problem statement, carry out an exploratory data study, and construct a predictive model. A presentation, technical report, and non-technical summary will be used to record and disseminate the student’s findings. As an intermediate-level data science course, students should have a solid background in mathematics and working knowledge of Python and programming concepts.
Attend Noble Desktop’s Python For Data Science Bootcamp to acquire proficiency in Python programming foundations and explore data analysis to unleash the potential of Python for data-driven decision-making. Offered in-person in NYC or Live Online via Zoom, this course will teach you why Python is used for data science, how to create programs, work with data in Python, create data visualizations, and use statistics to create machine learning models. Included in your tuition is hands-on learning with expert instructors, top-notch curricula, and supplemental materials among others.
Gain a ||CPN411|| through Noble Desktop, online or in-person in NYC. This curriculum is designed for beginners and will teach you the fundamentals of database manipulation and data analysis. The program will equip you for roles in Python engineering and data science. In this certificate program, you will learn the programming languages and libraries used by Data Scientists, complete real-world projects to add to your portfolio and show employers, and receive assistance with your job search, resume, and portfolio development in one-on-one mentoring sessions with an experienced Data Scientist.
The best corporate training programs in NYC have been developed and provided by Noble Desktop for more than 20 years. We have a great deal of expertise in creating curricula for a range of professional settings. Our programs are fully tailored to your team's unique needs. Noble Desktop can run training sessions onsite or live online through a teleconferencing platform like Zoom. Companies can facilitate corporate data analytics training at Noble Desktop’s midtown Manhattan facility or purchase bulk vouchers for regularly scheduled classes, offering employees the freedom to choose their own training schedule.
With practical corporate training in data science through Noble Desktop, you can upskill or reskill your workforce. Training will provide employees with the most important data analytic skills, including Excel, SQL, Tableau, and analyzing insights, to name a few. Add SQL Server, Python, machine learning, financial modeling, data science, or automation to your custom curriculum to advance your team's training. Group classes on these specific subjects are also available for voucher participants. Contact Noble Desktop for more information about corporate data science training programs or to schedule a complimentary consultation.