Tech Learning Consulting

DataNStats is a for-profit MOOC provider that teaches industry-relevant programming skills and offers credentials endorsed by leading technology providers. DataNStats courses give college students the opportunity to learn advanced technical skills at a significantly lower cost and time period compared to traditional universities.


Target Segment and Pipeline Development


DataNStats identified a large skill gap in advanced technical skills throughout the workforce specially college pass out students. The company’s founders anticipated that future technology jobs would require these skills and created a platform to help individuals gain the necessary expertise. The program focuses on building job-relevant skills from the first day through projects-based learning that emphasizes use in the real world. These are evaluated by experts in the technology industry. DataNStats’s Nanodegree programs are open to all learners except for the advanced programs. These have specific prerequisites to ensure the right fit and prevent drop-outs. The program targets students of various ages and educational backgrounds who are looking to develop technical skills to advance their career path. The majority of students are early to mid-career professionals from 24 to 35 years old. Early-career professionals often are looking to launch a new occupation in technology, while mid-to-late career professionals seek to enhance their skills within their current professions. The majority of DataNStats Nanodegree applicants first interact with the program by testing the content’s free courses. Some students hear about the program through partner companies that help develop its course curriculum. DataNStats also reaches out to companies directly to make them aware of its capabilities in advancing the skills of existing employees.

Our courses


Machine learning


Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, an algorithm learns from labeled training data to predict the output for new data. In unsupervised learning, the algorithm finds patterns or relationships in the data without any prior training. In reinforcement learning, the algorithm learns from the consequences of its actions in a specific environment.


  • Audience: College Students/IT Professionals
  • Duration : 1-2 Weeks
  • Result : Able to apply ML Model
  • Mode : In-person workshop

Data Engineering


Data engineering is the aspect of data science that focuses on the practical aspects of collecting, storing, and managing data. It is the foundation on which data science and analytics are built, and involves the design, construction, and maintenance of the systems and infrastructure that support the data pipeline. Data engineers work to optimize data architecture, design and build scalable systems and data pipelines, and ensure the quality, security, and accessibility of data. They also work on big data technologies such as Hadoop, Spark, and NoSQL databases, to store, process and analyze large amounts of data. Data engineering is a critical function in any organization that relies on data to make decisions, as it enables data scientists and analysts to quickly and easily access the data they need to perform their analysis.


  • Audience: College Students/IT Professionals
  • Duration : 1-2 Weeks
  • Result : Able to apply ML Model
  • Mode : In-person workshop

Data science


Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It is a combination of various tools, techniques, and methods from statistics, data mining, and machine learning to extract insights and knowledge from data. Data science involves the entire process of acquiring, cleaning, transforming, and modeling data, as well as visualizing and communicating the insights and findings. The goal of data science is to turn raw data into actionable insights that can inform business decisions and drive innovation.


  • Audience: College Students/IT Professionals
  • Duration : 1-2 Weeks
  • Result : Able to apply DS Model
  • Mode : In-person workshop

Data Analytics


A data analyst is a professional who is responsible for collecting, cleaning, analyzing, and interpreting large sets of data to inform business decisions. They use statistical techniques and software tools to analyze data and uncover insights and trends. Data analysts work with a variety of data sources, such as databases, spreadsheets, and social media, and use statistical methods to extract insights, patterns and trends from the data. They also use data visualization tools to present the findings in a clear and understandable way. They may also create predictive models to forecast future trends or identify potential issues. Data analysts work in a variety of industries such as finance, healthcare, retail, and technology. They may work independently or as part of a team, and often collaborate with data scientists, engineers, and business leaders to ensure that data-driven decisions are being made.


  • Audience: College Students/IT Professionals
  • Duration : 1-2 Weeks
  • Result : Able to apply Analystic Model
  • Mode : In-person workshop

Web development


Web development is the process of creating and maintaining websites. It includes a variety of tasks such as designing the layout and user interface, writing the code, creating and implementing content management systems, and configuring servers. Web developers typically use a combination of programming languages such as HTML, CSS, JavaScript, and back-end languages like Python, Ruby, and PHP to build websites. HTML (Hypertext Markup Language) is used to create the structure of web pages, CSS (Cascading Style Sheets) is used for styling and layout, and JavaScript is used to create interactive and dynamic elements on the page. Back-end languages are used to connect the website to a database and handle server-side tasks.


  • Audience: College Students/IT Professionals
  • Duration : 1-2 Weeks
  • Result : Create webpages
  • Mode : In-person workshop

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