Data Science & Analytics Bootcamp
Give your learners the skills and knowledge to spearhead data analysis and digital innovation in today’s workplace.
Corporate digitization is increasing at rates 20 to 25 times faster than previously thought possible, leading to a widening workforce skills gap and a deficit in qualified candidates in data analytics and other sectors. Without skilled data analytics professionals that can effectively and accurately evaluate the increasing amount of data generated by artificial intelligence tools, organizations would be left struggling with what to do with performance metrics and action tasks for maintaining continued success.
Thankfully, this is where data scientists and data analysts come in to identify, analyze, and solve complex problems through statistical and numerical data analysis. Professional data analysts are trained to see patterns in data not visible to the naked eye. Armed with the right training, ThriveDX learners in the Data Science & Analytics Bootcamp explore methods for managing and analyzing large datasets, including data wrangling, statistics and probability, and artificial intelligence, to be on the edge of data-driven innovation.
The Data Science & Analytics Professional Bootcamp is built for learners of any professional background who have a strong affinity for technical solutions, enjoy aspects of conceptual and visual design, and seek creative ways to solve problems. The program provides learners with the skills and hands-on experience companies seek in qualified data science professionals and data analysts. This ensures that learners complete the program with a robust GitHub portfolio of real-world projects that demonstrate they are ready to join the workforce and contribute to a company’s ability to solve complex problems.
In their first 30 hours, learners are introduced to core data science and analytics topics, such as computing basics and the data life cycle. Course participants will have the opportunity to learn the basics of a Jupyter notebook and interact with a Titanic dataset. They will pull the project materials from a GitHub repository and walk through it in a Google Colaboratory notebook. Learners can then look forward to completing three more projects throughout the remainder of the program to add to their personal portfolios.
In one of their first courses, learners are introduced to SQL, which is a standardized programming language used to query databases. Learners apply how to import data into databases, query data, join data together, filter and sort data, create views, and export data using SQL language. Further, learners are introduced to database design and learn how to manage their own databases.
This course aims to enlighten learners on how, to what extent, and in what way statistics and probability are used in business decision-making. This course aids the learner in building a strong foundation in descriptive statistics, conditional probability, and advanced modeling techniques. Learners develop the ability to approach real-world problems from an analytical perspective with confidence, using Microsoft Excel to provide practical application to theoretical class problems and data discussions.
Learners discover the power of a story and how to develop a story arc around their data goals. Successfully communicating data insights depends on the audience of stakeholders and the story points that speak to their needs and expectations. Learners continue to keep a data story thread throughout their entire data wrangling adventure as they frame their data goals with purpose.
This milestone project allows learners to explore their skills in the areas of statistics, Excel, SQL, and data storytelling. Learners are able to demonstrate their ability to clean and manipulate a dataset. Additionally, learners perform advanced statistical analysis on the data using summary statistics, linear regression, and modeling. Finally, they act as professional data analysts to put their visualizations and insights into a coherent data story to present to their classmates. The data analytics milestone project is formally reviewed by the instructional team. Learners then incorporate their projects into their GitHub portfolio.
In this course, learners revisit their previous knowledge from the SQL and Databases course to build upon advanced concepts that delve into SQL programming and covers specialized topics such as stored procedures, functions, common table expressions (CTEs), and query optimization. Learners also demonstrate the aptitude of a data scientist to develop ETL scripts and data pipelines combining the use of SQL and Python.
Learners explore the fundamental concepts of programming and how to structure their data analyses based on data science industry best practices. Topics include core programming concepts such as expressions, data types, variables, functions, loops, and arrays. Learners practice their coding skills through building highly structured and maintainable code using Jupyter notebooks.
Just as data is crucial for metrics, visual representation of data through different mediums, graphics, charts are also necessary for skilled data analysts to acquire through training to be an effective member of a data science team. In this course, learners explore visual dynamics and principles to produce effective data visualizations that show the most important parts of data to stakeholders in a clear and simplified way.
This milestone project focuses on developing the learner’s ability to attain, transform, investigate, and present data throughout a data project life cycle. Learners demonstrate their ability to build data pipelines and wrangle data into a usable format for downstream data visualization and analytics. For final evaluation by the instructional team, learners present their reports and findings to classmates and then incorporate their projects into their GitHub portfolio.
Learners build upon prior visual communication concepts by application of popular industry business intelligence tools to create insightful analyses and visualizations. Learners also develop and apply best practices for data analysis reporting, graphs and charts, and dashboards in a way that can be applied in any business intelligence application.
Learners analyze a variety of use cases in a business context for determining appropriate machine learning methods to apply. Through a series of Python lectures and labs, and using Jupyter notebooks, learners investigate and apply supervised and unsupervised machine learning algorithms, including classification, clustering, association rules, and time-series forecasting. Next, learners explore several advanced methods, including natural language processing, neural networks, and deep learning.
In this course, learners examine core concepts and methods used for big data and IoT, including characteristics of big data, data warehousing, data lakes, data virtualization, and cloud-based data infrastructure services. Learners build upon their previous knowledge of Python by using PySpark to access big data and create analytics models.
Following an introduction to machine learning-created AI in the previous module, learners gain exposure to a variety of common tools used by data science analyst professionals such as pre-packaged AI cloud services offered by leading providers like Microsoft, Amazon, and Google. Learners later easily identify and understand the ways in which AI provides a more targeted data analytics experience, producing a greater amount of data insights through various applications of computer vision, speech recognition, natural language processing, and robotics.
For their capstone project, learners assume the professional lens of a data science analyst to approach the challenge of presenting their data insights and visualizations both clearly and with ease to diverse types of stakeholders. As learners individually synthesize their knowledge and skills into a tangible asset, they actively practice how to organize goal-oriented data visualizations and integrate a story arc to keep their audience engaged. Learners achieve this milestone in a culminating project presentation that showcases their data analysis, data storytelling, and presentation skills, which they share by uploading to their professional-grade GitHub portfolios.
This course is dedicated to supporting the learner’s job search, pairing learners with the career curriculum team to acquire the knowledge and skills to be successful in the digital job market. Learners emerge confident in job placement and are equipped with the tools to readily vet, apply, and interview for roles in in-demand professional data science careers and data analyst careers.
To remain aligned with the demands of today’s employers, learners receive the dynamic, hands-on data science and analytics training needed to enhance their applicable skills and find solutions to industry challenges. Our learners graduate with confidence and the knowledge to operate at peak performance on any team.
The ThriveDX Data Science & Analytics Bootcamp gives learners the chance to join the data science workforce as an analyst. Through participation, learners gain the skills applicable to industry-recognized certifications, including Microsoft Certified: Data Analyst Associate, IBM Data Analyst Professional Certificate, IBM Data Science Professional Certificate and AWS Cloud Practitioner.
By partnering with ThriveDX, you can rest easy knowing that TDX is here to provide enrollment and admission services, marketing support, and talent matching so you can focus on what matters: cultivating a digital workforce that excels.
* While the curriculum covers much of the knowledge needed to perform well on industry exams, this program is not a test-preparation program, where the primary focus is the learner’s performance on the exam. The program is designed to teach in-demand knowledge for today’s workforce. Certification exams are not conducted as part of the program and require additional costs not included in tuition.
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