来Offer-人工智能与数据科学强化课程|价值56000

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第1部分

机器学习理论+Python编程基础

您将学习Python语法、基本的线性数据结构和搜索算法、以及工业界主流的传统机器学习模型,夯实数据科学基础。

上课频率: 1个月,每周5节课,每节课2-3小时

  • 第1周

    Introduction of Data Science

    Fundamentals of Probability

    [Coding] Python Basics 1 variable and syntax

  • 第2周

    [Coding] Python Basics 2 function and class

    Linear Regression & Logistic Regression I

    [Coding] Python Basics 3 base data structure

    [Coding] Python Binary Search

    Logistic Regression II & Regularization

  • 第3周

    [Coding] Python Array Basic Sorting

    Model Evaluation

    [Coding] Python LinkedList and Recursion I

    [Coding] Python LinkedList & Recrusion I cont

    Nonlinear Models I

  • 第4周

    [Coding] Python Practice

    Nonlinear Models II & Feature Selection

    [Coding] Python Advanced Sorting and Practice

    [Coding] Python Review

    PCA & Unsupervised Learning

    第2部分

    概率与统计知识 & Python编程进阶

    您将进一步学习Python、数据结构和算法知识,锻炼Coding能力,并学习数理统计、概率等相关的重要知识点。

    上课频率: 3周,每周5节课,每节课2-3小时

    • 第5周

      Data Manipulation in Python 1

      [Coding] Python Queue and Stack

      [Coding] Python Advanced Sorting and Practice

      Data Manipulation in Python 2

      [Coding] Python Review

      [Coding] Python Review

      [Coding] Exam 1

    • 第6周

      Machine Learning Project 1 – Customer Churn Prediction

      [Coding] Python Binary Tree

      [Coding] Recursion II – recursion on tree

      Machine Learning Project 2 – NLP and Topic Modeling

      [Coding] Python Practice

    • 第7周

      Introduction to statistics

      [Coding] Python Binary Search Tree

      [Coding] Python review

      Resume and Interview Preparation I

      Resume and Interview Preparation II

      A/B testing 1

      [Coding] Python Heap

      A/B testing 2

    • 第8周

      A/B testing 3

      [Coding] Python Review

      A/B testing 4

      Inference in regression

    • 第9周

      [Coding] String I

      SQL I

      [Coding] Recursion III DFS

      [Coding] Recursion III DFS cont

      SQL II

       

    第3部分

    OA经典案例分析与简历辅导

    本阶段,您将学习经典Online Assessment破题思路,了解如何选择track,并获得深入准备和提升简历。

    上课频率: 2 周, 每周5节课,每节课2-3小时

    • 4+案例分析与项目实战,加强您的分析能力和统计知识,夯实SQL和Python基础,提升沟通等软实力,帮助您顺利通过商业分析岗位面试。

      上课频率: 1个月,每周4节课,每节课2-3小时

      • 第11周

        BA track introduction

        BA track mock interview

        [Coding-for-BA] Queue, Stack

      • 第12周

        eCommerce deep dive 1: System design

        eCommerce deep dive 2: Data driven marketing

        eCommerce deep dive 3: Data lab

        [Coding-for-BA] HashTable

      • 第13周

        eCommerce deep dive 4: Data lab

        Data visualization In Tableau

        Data visualization in Python

        [Coding-for-BA] String practice

      • 第14周

        Case study deep dive 1

        Case study deep dive 2

        Case study deep dive 3

        Anomaly Detection 1

      • 第15周

        Anomaly Detection 2

        Anomaly Detection 3

        Supply chain data 1

        Supply chain data 2

      • 第16周

        Review of BA/DA track

         

      7+个机器学习项目实战,深入讲解分布式系统Spark和深度学习TensorFlow等前沿知识,帮助您拿到数据科学岗位offer。

      上课频率: 1月, 每周4节课, 每节课2-3小时

      • 第11周

        Big data and ML I: Data pipeline

        Big data and ML I: Course project introduction

        [Coding] Advanced Tree (complete tree, segment tree, trie tree)

      • 第12周

        Big data and ML II: Spark RDD, SQL, DataFrame

        Big data and ML III: Spark ML

        Big data and ML IV: Spark ML and CTR

        [Coding] Graph Search Algorithm

      • 第13周

        Big data and ML V: Recommendation system

        Big data and ML VI: Apache Spark and Flink Streaming

        ML Advanced Topics I – Model Implementation

        [Coding] Graph Search Algorithm Cont

      • 第14周

        ML Advanced Topics II – Gradient Boosting Machine

        ML Advanced Topics III – Data Science Case Study

        ML Advanced Topics IV – XGBoost Practice

        [Coding] Python practice: mock interviews

      • 第15周

        Deep Learning I – Neural Network Basics

        Deep Learning II – Implement Neural Network from Scratch

        Spark lab practice and code review

        Deep Learning III – Convolutional Neural Network

      • 第16周

        Deep Learning IV – Recursive Neural Network

        第10周

        [Coding] Exam 2

        SQL III

        Stats review

        [Coding] Probability, Sampling, Randomization

        Resume and interview preparation

        Career guide: BA vs DS

        Online Assessment – deep dive 1

        Online Assessment – deep dive 2

       

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