15 weeks

5 sessions/week, 2-3 hrs/session

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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
  • 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

  • Apache Flink Stream Computing

    Stream Computing can provide real-time data for Business Intelligence system and Artificial Intelligence system, so that users can obtain up-to-date information either directly through statements or indirectly through algorithm model.

    This project is based on mobile game data. It will lead you to calculate real-time scores and ranking list through Flink SQL, and master the basic principles of Stream Computing.

    Stream Computing
    Flink SQL
  • Natural Language Processing and Topic Modeling

    Topic modeling is a frequently used method that helps in discovering hidden topics, annotating documents, and organizing, searching and summarizing texts.

    You will cluster unlabeled textual documents into groups and discover latent semantic structures using Python. You will learn to preprocess text by tokenizing, stemming and stopwords removing, and extract features by term frequency-inverse document frequency (TF-IDF) approach. Using these data, you will train unsupervised learning models and learn to visualize model training results.

    Unsupervised Machine Learning
    K-Means Clustering
    Latent Dirichelet Allocation
    Principal Component Analysis
  • Youtube User Comments Semantic Analysis

    With big data and machine learning, Data Scientist can now understand users better. Learning how to use Spark ML to process large scale natrual language data will help you get more interview opportunities.

    You will build a ML model to identify user 's preference over the Youtube video based on Pyspark. You will design the reasonable metric to evaluate the proposed ML model, clean users' comments by Spark ML related NLP techniques, and build a supervised model to classify users comments. You will further need to handle unbalanced data and labeling missing issues. You will use AutoML technique to speedup the tuning procedure. Finally, you will generate a business report and introduce the way to increase user's engagement with Youtube.

    Spark ML
    Comment Prediction
    Topic 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
    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
  • Click-through Rate Forecasting System

    In online advertising, click-through rate (CTR) is a very important indicator for evaluating the effectiveness of your ads. CTR click-through rate forecast is an assessment on the clicks of each ad, which is widely used to sponsorship search and real-time bidding. CTR is often evaluated in data science interviews.

    This project is based on the user's daily click-through data, and involves three main processes: ETL, OLAP and statistical analysis, machine learning modeling and forecast. Spark dataframe is used for preprocessing. Spark SQL is used for big data analysis and statistical modeling, and Spark ML pipeline is used for classification and regression models. We will introduce the principles of XGboost, optimization, and so on.

    CTR Prediction
    Spark ML
  • 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.

    Recommendation System
    Movie Recommendation
    End-to-end Training
  • Semantic Analysis for Twitter Streaming Data

    Stream data processing would be the next generation computation. Streaming data analysis reduces the workload of data analysis caused by data landing. Real-time streaming data analysis, processing and modeling would be the killing skill for finding a job from top technical company.

    This project is based on Twitter's stream data and leads you to build a complete stream data processing pipeline. This project is based on Kafka's workflow for data redistribution, and then uses Spark Streaming and Spark Structured streaming to clean and analyze the stream data. Finally, it enable you to build a Spark ML's offline text data analysis model to identify user’s sentiment from streaming Twitter data.

    Streaming Data
    Social Network
    Semantic Analysis
    LDA Model
    Spark Streaming
    Spark Structured Streaming
    Abnormal Detection
  • Image Classification Based on Deep Learning Model CNN

    Image classification is one of the most important tasks of computer vision, and it has also been applied to large-scale applications by major IT companies. The Convolutional Neural Network (CNN) has yielded very good results on Imagenet, the image classification big data set.

    Based on CNN and Imagenet’s weighted model, we use tensorflow+transfer learning technology to optimize user-defined data set, and establish a deep learning model for car image classification and related image search.

    Image Classification
    Transfer Learning
    Fine-tune Model
    Pre-train Model
  • 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
    Stock Price Prediction
  • Supply Chain Data Analysis and Forecasting

    In many commercial fields, such as retail, manufacturing, and medical industries, accurate forecasting of product demand is closely related to corporate income. Excessive forecasting will lead to higher storage costs and shorter product life; on the contrary, too conservative forecasts will bring a shortage of stocks, which will weaken the willingness to consume and affect the brand image. Therefore, for large enterprises in the transition period of Industry 4.0, how to use the effective information in the big data wave to forecast product demand has become an important issue.

    In this project, we will analyze and process historical sales and product data of several well-known traditional enterprises, and establish models to predict the future needs of new and old products. At the same time, we will have a better understanding of the supply chain and related job opportunities in traditional companies in the era of big data.

    Supply Chain
    Product Demand Forecast
    Sales Data
  • Payment Fraud Detection

    In various industries, such as Finance, E-commerce, resource sharing, etc, there are all kinds of hidden fraudulent activities. These activities result in direct financial loss. It is a huge challenge for these companies to pinpoint the rare fraudulent activities and minimize financial loss, while maintain good user experience. In this project, we will analysis E-commerce transaction data, study the insight/pattern, and build machine learning solution to give actionable business recommendation for deployment.

    Payment Fraud Detection
    Machine Learning
    Pattern Study
  • 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.

    Business Analysis
    Data Visualization
    Product Insight

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, Hortonworks. 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.

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.

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.

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.

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.

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.

Internal Referral Network

to Assist You in Job Hunting


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 & Linear Regression

    [Coding] Python Basics 1 variable and syntax

  • Week 2

    [Coding] Python Basics 2 function and class

    Logistic Regression I

    [Coding] Python Basics 3 base data structure

    [Coding] Python Binary Search

    Logistic Regression II & Model Evaluation

  • Week 3

    [Coding] Python Array Basic Sorting

    Nonlinear Models

    [Coding] Python LinkedList and Recursion I

    [Coding] Python LinkedList & Recrusion I cont

    Feature Selection

  • Week 4

    [Coding] Python Practice

    Unsupervised Learning

    [Coding] Python Advanced Sorting and Practice

    [Coding] Python Review

    Data Manipulation in Python 1

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

    [Coding] Python Queue and Stack

    Data Manipulation in Python 2

    [Coding] Python Review

    [Coding] Exam 1

    Machine Learning Project 1 - Customer Churn Prediction

  • Week 6

    [Coding] Python Binary Tree

    Machine Learning Project 2 - NLP and Topic Modeling

    [Coding] Recursion II - recursion on tree

    [Coding] Python Practice

    Introduction to statistics

  • Week 7

    [Coding] Python Binary Search Tree

    [Coding] Python review

    Hypothesis testing 1

    [Coding] Python Heap

    Hypothesis testing 2

    A/B testing 1

  • Week 8

    [Coding] Python Review

    A/B testing 2

    [Coding] Python Hashtable

    Inference in regression

    [Coding] String I

  • Week 9

    SQL I

    Python review

    [Coding] Recursion III DFS

    [Coding] Recursion III DFS cont

    SQL II

    [Coding] Exam 2

Section 3

Review of Typical OA & Resumes

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

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

  • Week 10


    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 (& mock interview) & final project presentation

    eCommerce deep dive 1: System design

    [Coding-for-BA] Queue, Stack

    eCommerce deep dive 2: Data driven marketing

  • Week 12

    eCommerce deep dive 3: Data lab

    Case study deep dive 1

    [Coding-for-BA] HashTable

    Case study deep dive 2

  • Week 13

    Case study deep dive 3

    Data visualization In Tableau

    [Coding-for-BA] String practice

    Data visualization in Python

  • Week 14

    Anomaly Detection 1

    Anomaly Detection 2

    Anomaly Detection 3

    Supply chain data 1

  • Week 15

    Supply chain data 2

    SQL Lab

    Mock interview session 1

    Review of BA/DA track

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

Frequently Asked Questions

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$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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