Launching Course 'Bundles' with Financing Options

Posted on Jul 24, 2019

NYC Data Science Academy is excited to announce the launch of five bundled courses, offered at a discounted rate. Designed to address the unique needs of students and working professionals, the bundles are tailored to individual schedules, interest areas and career objectives.

Launching this summer, the bundles offered by NYC Data Science Academy at their NYC campus and online platforms will help students boost their data science and data analytics skills. Designed to provide mastery in most demanded job areas, these bundles are aimed to provide a strong foundation in Data Science, Data Analysis, Machine Learning and Visualization using Python and R. All the bundles are supported with a full- financing option by Climb Credit with a "Start Now, Pay Later" feature.

"These bundles are designed to benefit individuals from rich academic and professional resources available at NYC Data Science Academy, through a module that is exclusive and befitting for working professionals or college students," said Vivian Zhang, CTO NYC Data Science Academy. She believes that the bundles have the potential to address several needs of the future data science community. "By providing the training that can be taken either in-person or online with full-flexibility of choosing the dates to suit individual schedules, we want to play a significant role in helping future scientists, engineers and analysts advance their career in the field of their choice."

The below bundles were launched a few days ago and are available on the website for enrollment:

  1. Bootcamp Prep: This helps individuals prepare for the Immersive Data Science Bootcamp with courses in Python, R, Data Analysis and Visualization. This combination of four courses builds a foundation in both R and Python by teaching the basic concepts in linear algebra, calculus, and statistics combined with the practical tools needed in data analysis and visualization.
  2. Data Science with Python: Starts with an introduction to Python and advances to a comprehensive application to data science with Python. This prepares individuals to excel in Machine Learning, Data Analysis and Visualization with Python by choosing a combination of language basics such as NumPy, SciPy, Pandas, Matplotlib, and Seaborn. After successfully completing this bundle, one can analyze complex datasets and make predictions in Python.
  3. Data Science with R: This bundle prepares individuals for statistical programming in R with Machine Learning, Data Analysis and Visualization. The intermediate courses offer a full understanding of data processes, data analysis and manipulation, creating advanced visualizations,  report generation, implementing machine learning algorithms and documenting codes.
  4.  Data Analyst Mastery: Foundational knowledge in data wrangling, predictive and statistical analysis, data analytics and visualization tools to become a data analyst. These four courses will help individuals boost their skills to gain a deeper sense of comfort by ‘learning through practice’ in reviewing data and sharing findings.
  5. Data Science Mastery: This bundle covers the most advanced data science topics like Deep Learning, Big Data with Amazon Cloud, Hadoop, Spark and Docker. There is a heavy focus on data mining, regression models, tree models, discriminant analysis and Naive Bayes, key components of Apache Hadoop and more. 

The bundles have applications across various industries such as manufacturing, technology, healthcare, consulting, marketing, education, media, finance, economics and more. The bundles are also discounted to enable students to harness the complex data using advanced tools. The application for the bundles is on an ongoing basis. For more information, visit https://nycdatascience.edu/course-bundles/

About Author

Pranjali Galgali

Pranjali Galgali is a Marketing and Communications Associate, NYC Data Science Academy. She is a Master's in Digital Media and Strategic Communications from Rutgers University. She enjoys reading and writing about data science, upcoming technologies and loves interviewing...
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