Summer immersion Program offer the same coverage with Our 16-week program, but more intensive schedule, which contains basic data science knowledge teaching, in-progressing Kaggle Competition and several advanced practical projects. Students will master the problem-solving skills through the process of identifying market needs, preparing data, feature engineer, model selection, model optimization, and final result presentation. We will provide our students with the most cutting-edge technology and tools in data science industry, to help them turn into a professional data scientist from scratch, step by step.
In our 16-week real internship projects, trainees will apply what they have learned from lectures in the first 8-week into practice. You will go through the End-to-End project, from identifying market and technical needs, developing the solution, to testing the initial product and deploying the final product.
Part I focuses on elementary data structures, sorting, and searching. Topics include array list, linked list, stack/queue, hash table, binary search trees, and heap. Part II focuses on graph and three categories of algorithm: Divide and Conquer, Dynamic Programming and Greedy.
Topics related with Syntactic Processing, Semantic Analysis, Information Retrieval, Chart Parsing and etc, will be covered. Practical projects with real-world data will also be offered to students, along with step-by-step instruction on how to improve your results.
Pre-requirement class for Big Data Engineer Bootcamp This course introduces fundamental structured and object-oriented programming concepts and techniques, using Java, and is intended for all who plan to use computer programming in their studies and careers. Topics covered include basics of java, like variables, arithmetic operators, control structures, arrays, functions, recursion, dynamic memory allocation, files, […]
Add to Cart Financial technology, also known as “FinTech”, is the use of new technology and innovation in the marketplace of traditional financial institutions and intermediaries. The FinTech Big Data program teaches students the cutting-edge technology and the best practices in the industry with case studies and real-world projects. For Chinese introduction, Please see […]
Add to Cart Full-stack Data Science Engineering is the integration of data science and data engineering. A full-stack data scientist has a broad understanding of data science product life-cycle shown as blow: For Chinese introduction, Please see here: Career Goal: Software architect: a software expert who makes high-level design choices and […]
Add to Cart Supply Chain Management(SCM) boot camp is an intensive 4 weeks training program to equip students with industry-specific skills for supply chain management within the big data context. For Chinese introduction, Please see here: Topics: – Predictive modeling technology, simulations – Network design, routing, inventory optimization – SCM applications with […]
Data Scientist Interview Skills Training Time: 3.9-4.4（4 Weeks，40 Hrs） Tuesday（5-7PM）、Thursday（5-7PM）、Saturday（9AM-12PM）、Sunday（1-4PM） Training Brief: 1） Sat, Sun：Lecture, Problem Overview（6 hours） 2） T, Th：Interview Questions Practice(4 hours) 3） Topic coverage: Probability/Statistic/ML/Data Challenge/SQL/Case Interview/Python Programming/Algorithm Add to Cart
点击提交课程申请 Data Scientist 面试冲刺班 面试是你拿到offer的临门一脚，很多求职者的知识，技能和经验已经达到工作要求的水平，但是由于不熟悉面试的过程和窍门，没有发挥出水平，面试失败。 数据应用学院（Data Application Lab）帮助过大量转行数据的学员找到数据科学家，数据分析师和商业分析师的工作，其中一个重要的帮助就是为学员进行Mock Interview。我们通过面试官和我们的学员收集了大量的面试真题，像我们准备GRE一样的，我们通过题目的练习，加上导师的讲解，帮助学员在面试的过程中，发挥出高水平，拿到Offer。 根据同学们的要求，数据应用学院推出了一项新的training内容，数据科学面试技能冲刺班。这个训练班的目标是通过系统的知识梳理，大量面试题的训练，让同学们在面试中发挥高水平，拿到工作offer。 本期课程: 时间： 3月9日-4月4日（4 周，共计40课时） 课程安排： 周二（5-7PM）、周四（5-7PM）、周六（9AM-12PM）、周日（1-4PM） 课程内容： 1） 周六、周日：理论学习（6 hours） 2） 周二、周四：面试题型精讲和练习(4 hours) 3） 涵盖Probability/Statistic/ML/Data Challenge/SQL/Case Interview/Python Programming/Algorithm 购买
This course is also offered in other languages. For language in 中文, please refer to this webpage(dataapplab.com/ai) for details. Week 1 & 2 – Machine Learning Topic 1 Regression Topic 2 Classification Topic 3 Dimension Reduction Topic 4 Clustering Week 3 & 4 – Deep Learning Basic & Computer Vision Topic 1 Computer Vision, Neural […]