Duration

16 weeks

5 sessions/week, 2-3 hrs/session

Course Starts

Course opens soon

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Prerequisites

None

Recommended for everyone

Combines business analytics and data science, this course is tailored to interviews!

Started from July 2018, newly updated since 2019, we designed two tracks - data science and business analytics. For the first two months, you will take the fundamental sessions. For the next one month, you will take your own sessions catered to the track you choose. You can also take sessions from both tracks.

The course will be taught by experienced data scientist and machine learning experts from top tech companies. Faculty student ratio reaches 1:5. The course is tailored to meet industrial demands for artificial intelligence and data science positions. 20+ instructors to help you master the most cutting-edge skills in data science.

The companion coursework dives you into the most recent and relevant trends in the data science world: user stickiness analysis, text clustering, spark program development, and deep learning.

Students who took the course have gained offers in technology, finance and consulting industries including data scientist, machine learning engineers, data analytics and business analytics positions.

AI & Data Engineering Introduction
Free Trial Session

Course Introduction

Instructor: Jason

One Course, Two Tracks

4 weeks of separate course tracks catered towards your career paths and interview requirements. The tracks are shared and connected with the option to be on both, so you can explore multiple opportunities at the same time.

Track I

Business Analyst Track

Focus on developing your business sense with emphasis on business analytics, mathematical statistics, case studies, A/B testing and other necessary skills. Boosts your SQL and Python proficiency to get you ready for business analyst positions.

Data Analysis
Data Manipulation
Data Visualization
Business Communication
Case Studies
  • Highlights the specific requirements of Business Analyst interviews.
  • 4+ data challenges and case studies, help you to improve your resume.
  • Focus on data visualization, data manipulation and business soft skills trainings.
  • Instructed by experts with years of experience working as business analyst in IT, Finance, Energy, Consulting industries.

Track II

Data Scientist Track

Gives you an in-depth training of cutting-edge technologies such as distributed systems and deep learning. With a higher standard in coding, we will prepared you for data scientist positions through 4+ machine learning projects.

Big Data
Deep Learning
Machine Learning
Apache Spark
TensorFlow
  • Covers essentials of data science positions: coding, models, statistics theories, big data systems and deep learning.
  • Challenges your mind with 4+ state of the art machine learning projects.
  • Helps you crack data science job interviews through mock interview classes.
  • Instructed by Apache Spark core development engineers and data scientists.

10+ Spark, Machine Learning & Business Analysis Projects

  • Banking Customer Churn Prediction and Analysis

    Customer churn is a common business metric across different industries, like telecommunication, music, and video streaming service, SaaS. Therefore, it is very important to know how to analyze this metric which will influence the company strategy and future.

    In this project, we will use different supervised machine learning models to predict if a banking customer will churn or not and do further analysis, then we'll also figure out the key factors related to customer churn, and guide the company to do better business actions to retain valuable customers. By completing the project, you will learn how to use Pandas for data exploration, data analysis, data preprocessing and Sklearn for machine learning models.

    Pandas
    Customer Churn Prediction and Analysis
    Sklearn
    Machine learning
  • E-commerce Reviews Analysis and Topic Modeling

    With the rise of Internet, customers are increasingly willing to express their opinions. We can find that customers are highly likely to check reviews before purchases and also prefer to share their reviews and user experience, especially for online shopping. So through analysis of customer reviews, the company can better understand customers’ opinions and needs, and can make more informed business decisions. In this project, we’ll use machine learning to analyze an e-commerce company’s customer reviews data and find out the insights and internal relations of the reviews. Further, we can use these information to help us solve some business use cases, like improve conversion rate.

    TFIDF
    PCA
    Python
    K-means cluster
    Latent Dirichlet Analysis
  • San Francisco Crime Data Analysis & Abnormal Events Prediction

    Big data analysis is an essential skill for data scientist. Data scientist needs to build an entire pipeline includes data collection, data cleaning and data modeling.

    This project is based on crime data in the San Francisco area. It will lead students to establish a data analysis workflows including data collection, cleaning, storage, and analysis. Based on analyzing and modeling for the crime and weather data, a possible crime event prediction model was established.

    Spark RDD
    Spark SQL
    OLAP
    Regression
    Data Pipeline
  • Netflix Movie Data Analysis & Recommendation System

    Recommendation system is the most profitable department in Google, Facebook, Airbnb, Uber and other startup companies. The ability to design and build a recommendation system is the most important and attractive capability for a data scientist.

    This project will lead you to become an expert in building a recommendation system for big data. Netflix movie rating data are used to build the recommendation system, and help you to be and expert in recommendation system by mastering of machine learning algorithm to system implementation. You would come to master the skills on Spark machine learning pipeline building and collaborative filtering model automatically tuning, and apply the built model on Netflix movie rating data.

    Recommendation System
    Collaborative Filtering
    Matrix Factorization
    Spark ALS Model
  • Sales Forcast and Market Analysis for Google GStore

    Kaggle competition is an important test for every Data job seeker. Achieving a good ranking in the competition is one of the best expression of competence and a very crucial criterion for the company to judge talent. In this kaggle competition,we are challenged to analyze a Google Merchandise Store (also known as GStore) customer dataset to predict revenue per customer. You will use LGBM, PyTorch DeepModel to implement your algorithm. This project will also help you get familiar with common strategy for Kaggle and get a good place.

    LGBM
    PyTorch DeepModel
    Kaggle
  • Movie Recommendation System Based on Auto-Encoder-Decoder

    With the rapid development of deep learning technology, more and more Internet companies are beginning to use deep learning in building recommendation systems. Deep learning enables end-to-end learning, compared to traditional recommendation systems.

    This project is based on the deep learning model auto-encoder-decoder network, using imdb movie data as training data, and tensorflow to build auto-encoder-decoder model. Features of users and movies are extracted through the model, and the automatic recommendation of movies is finally realized.

    Auto-encoder-decoder
    Recommendation System
    Tensorflow
    Movie Recommendation
    End-to-end Training
  • Time Series Data Analysis & Stock Index Prediction

    Time Series data is very common in our daily life. It is a collection of data obtained by measuring the time series of observations at equal time intervals. For example, the annual sales volume of apparel companies, the price of stocks, the annual precipitation of a city in meteorology, the average monthly temperature, and the PM2.5 index variation etc. Therefore, the analysis of time series data is capable for different real-life applications.

    This project is based on the deep learning model LSTM. Students will learn the principle of LSTM models and related technologies for analyzing time series data. This project uses NASDAQ stock data as the training data, and teaches students to build a deep learning model via TensorFlow, which later can be used to predict stock price variation and stock market index.

    Time Series Data
    LSTM
    RNN
    TensorFlow
    Stock Price Prediction
  • NYC Taxi Rides and Stock Market Indexes

    With the advancement of computer technology, it is now easy to dig out hidden information from unrelated data. For example, in the eighteenth century, stock prices fluctuate with the ships coming and going, because the merchant brought the latest news as well as the cargo. Other studies have found that company executives' visits to the White House can predict the future direction of the company's stock. In this project, we will follow the same line of thinking and analyze the relationship between New York taxis and the stock market. Does the seemingly complicated New York traffic have interesting information hidden?

    In this homework, the students will use all the knowledge they have learned to reasonably explore the data, including defining the appropriate business problem, asking reasonable questions, summarizing the data under right metrics, selecting reasonable statistical models, and verifying the conjecture.

    Python Dashboard
    Segmentation Analysis
    Statistical Model
    Poisson Regression
  • E-Commerce Marketing Strategy Optimization

    In 2017, global retail e-commerce turnover reached 2.290 trillion US dollars, accounting for 10.1% of total retail sales, and is expected to reach 4.479 trillion US dollars by 2021. Year 2018 is the year of online and offline retail revolution - "Future Retail" has taken root and flourished.

    In this project, the students will analyze the sales volume and product information of a well-known e-commerce website, systematically learn personalized design, attract new customers and encourage customers to re-shop, optimize commercial marketing channels, and then establish a web product sales forecast model.

    E-commerce
    Business Analysis
    Data Visualization
    Product Insight
  • Data Visualization

    "A picture is worth a thousand words". The capability to understand and communicate the data has become an essential skill for analytics professionals. In this project, we will learn foundation and some best practice of data visualization, use Tableau with classic Global Superstore Retail Dataset to perform exploratory analysis and report on sales business case.

    Tableau
    Python
    Data Visualization
    Data Analysis
  • Search Engine Ads Case Study

    Searching the Internet has become an almost reflexive act. Each day, tens of millions of global citizens fire up their personal computers and handheld devices to troll for information on products or to glean insights that will help guide business decisions. As of 2019, active search engine crawlers include those of Google, Baidu, Bing, and etc. Google dominates the 93% market share and is searched at least 2 trillion times per year. Search engine possessed tremendous impact on social and economic of every modern country. Here, we will learn how search engine is defined and built from scratch. We will further dive into search engine optimization and marketing.

    Search Engineer Optimization
    Business Analysis
    Product Insight
  • Google Stock Index Prediction Using Time Series Analysis

    Time-series analysis is a basic concept within the field of statistical learning that allows the user to find meaningful information in data collected over time. To demonstrate the power of this technique, we'll be applying it to the Google stock price in order to find the best model to predict future stock values.

    This project is based on ARIMA-based techniques and uses Google stock price as training data. Students will have a more comprehensive ability on time series cases. Students will learn how to apply the Python statsmodel library, how to choose the right time series model, and finally how to apply the model to predict stock price.

    Python
    ARIMA
    RNN
    LSTM
    Time Series Data

Learn from Industry's Leading Experts

20+ instructors to help you master the most cutting-edge skills in data science and achieve your career goals.

Our team consists of senior data scientists, machine learning engineers, and business analysts from Google, Facebook, McKinsey & Company, Linkedin. You will also receive hands-on guidance from Apache Spark/Hadoop contributors and committee members.

Our team consists of senior data scientist, machine learning engineer, and business analyst from Google, Facebook, McKinsey & Company, Hortonworks, Apache Spark, Apache Hadoop, etc.

Designed to Set You Apart

You will learn from 20+ instructors from Google, McKinsey and other top tech and consulting companies. You will also receive hands-on guidance from Apache Spark/Hadoop contributors and committee members.
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Learn from

Industry’s Leading Experts

This updated course is based on the latest trends in Data Science interview, providing 10+ statistic classes, and intersive training on case study and experimental design.
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Updated Statistic Module

to Boost Your Skills

You will take 30+ Python sessions to build up your knowledge of algorithms and data structure and impress your interviewers with top coding skills.
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30+ Python

Fundamental Sessions

You can choose business analytics or data science track based on your interest and career plan. Professional data scientists and senior business analysts will share their insights with you to help you get your dream job.
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Two Tracks Cater to

BA/DA and DS/DE Interviews

Instructors from top tech and consulting companies will also help you on revising your resume and help you imrpove communication skills for job interviews. Meanwhile, instructors will conduct one on one mock interview to get you ready.
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Resume review

and mock interview

LaiOffer maintains positive collaborative relationships with top tech companies in Silicon Valley and hiring agencies. We have strong referral resources to support you.
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Internal Referral Network

to Assist You in Job Hunting

Syllabus

Section 1

Machine Learning Model & Python Fundamentals

You will learn the foundamentals of Data Science including Python basics, linear data structures and search algorithms, and traditional machine learning models.

Frequency: 1 month, 5 sessions/week, 2-3 hrs/session

  • Week 1

    Introduction of Data Science

    Fundamentals of Probability

    [Coding] Python Basics 1 variable and syntax

  • Week 2

    [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

  • Week 3

    [Coding] Python Array Basic Sorting

    Model Evaluation

    [Coding] Python LinkedList and Recursion I

    [Coding] Python LinkedList & Recrusion I cont

    Nonlinear Models I

  • Week 4

    [Coding] Python Practice

    Nonlinear Models II & Feature Selection

    [Coding] Python Advanced Sorting and Practice

    [Coding] Python Review

    PCA & Unsupervised Learning

Section 2

Statistics Essentials & Python Essentials

You will learn Python, data structure and algorithms, improve Coding skills, and enhance your knowledge of mathematical statistics, probability and so on.

Frequency: 3 weeks, 5 sessions/week, 2-3 hrs/session

  • Week 5

    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

  • Week 6

    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

  • Week 7

    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

  • Week 8

    A/B testing 3

    [Coding] Python Review

    A/B testing 4

    Inference in regression

  • Week 9

    [Coding] String I

    SQL I

    [Coding] Recursion III DFS

    [Coding] Recursion III DFS cont

    SQL II

Section 3

Review of Typical OA & Resumes

You will study typical Online Assessment, and enter resume review sessions.

Frequency: 2 week, 5 sessions/week, 2-3 hrs/session

  • Week 10

    [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

Section 4

Through 4+ Case Studies and Data Challenges, you will enhance your business analytics, case studies, SQL and Python skills and get ready for business analyst positions.

Frequency: 1 month, 4 sessions/week, 2-3 hrs/session

  • Week 11

    BA track introduction

    BA track mock interview

    [Coding-for-BA] Queue, Stack

  • Week 12

    eCommerce deep dive 1: System design

    eCommerce deep dive 2: Data driven marketing

    eCommerce deep dive 3: Data lab

    [Coding-for-BA] HashTable

  • Week 13

    eCommerce deep dive 4: Data lab

    Data visualization In Tableau

    Data visualization in Python

    [Coding-for-BA] String practice

  • Week 14

    Case study deep dive 1

    Case study deep dive 2

    Case study deep dive 3

    Anomaly Detection 1

  • Week 15

    Anomaly Detection 2

    Anomaly Detection 3

    Supply chain data 1

    Supply chain data 2

  • Week 16

    Review of BA/DA track

* The syllabus is subject to change at the discretion of LaiOffer.

Frequently Asked Questions

$6,500 USD

  • Dedicated instructors provide step by step coding guidance
  • Interactions on 5 platforms 24/7
  • All-star instructors give live teaching for the whole session
  • 1-on-1 resume revisions, mock interviews and job referral
  • 10 industrial level project to ace your interviews
  • 30+ Python sessions improve your coding skill
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