Mastering Applied Data Science
- 12 Week Applied Labs Training
- 100% Hands-on Learning Experience
- Work on Real-World Projects
6 Week Data Science Applied Labs
Learn the skills you need to become a data scientist or business intelligence analyst in our 6-week program led by a team of industry experts.
- Build Python skills for programming
- Build and implement predictive models and algorithms using machine learning
- Data mine datasets and data cleaning techniques for analysis of your data set
- Data visualization techniques
- Learning statics model like linear regression, logistics regression, classification models, K mean nearest point and random forest
6 Week Project Based Learning
Project Based Learning is a dynamic approach solving real-world problems to gain knowledge and skills. Through this learning experience, you are able to investigate and respond to an engaging and complex question, problem, or challenge. Our goal into building Project Based Learning is to make you skillful to fulfill your career goal.
- Hands-on learning and real life projects
- Skill improvement to 9 out of 10
- With 100% in-person instruction from the experts in data science
- Well-networked in our data science community
- Get certificate and the help on job recruitment
- Build personal GitHub showcase as portfolio
Mastering Data Science Applied Labs
Session I : Introduction to Data Science with Python
In our first class, we will go over some Python fundamentals, which will cover syntax and built-in functions. We will move onto practicing For Loops and introducing the packages that will be covered over the course and how to install them. Day ends with a hands-on exercise on loops, functions, lambda functions, and conditionals.
Session II : Visualization & Exploratory Data Analysis
We will start by introducing the Cross Industry Standard Process for Data Mining (CRISP-DM) and data mining with supervised learning and unsupervised learning, followed by introduction to NumPy and Pandas. Students will practice on the Titanic dataset before moving onto web scraping techniques and extracting data from APIs.This session concludes with a hands-on analysis of data using the Titanic dataset.
Session III : Data Mining utilizing NumPy & Pandas
We will begin by reviewing NumPy and Pandas before delving deeper into more advanced techniques to clean and munge data showcasing how to analyze and enhance data. Students will learn to visualize and identify trends, using Matplotlib and Seaborn packages. We finish of day number 3 with an example of how to handle categorical data using dummies.
Session IV : Machine Learning with Project 1
Session 4 starts with a review of regression based machine learning algorithms such as simplelinear, multivariable, logistic, ridge, and lasso regression.In the second half of day four studentscomplete their first data science project. The project is an estimation challenge predicting house pricing based on a Kaggle competition.
Session V : Advanced Machine Learning Concepts
We will dive into classification algorithms such as Naïve Bayes, Decision Trees, Random Forest, and other methods like gradient descent. Students are also introduced to scoring using precision, recall, sensitivity, specificity, and accuracy score as well as AUC, and ROC. During the course of the day each algorithm is explained with a hands-on exercise.
Session VI : Recommendation Systems
Students will review machine learning concepts and will start by building their own recommendation system with a MovieLens dataset, understanding dimension reduction with Principal Component Analysis, exploring Support Vector Machines, and learning A/B Testing with T-Tests and P-Values.
Session VII : Natural Language Processing and Sentiment Analysis
Students will explore the Natural Language Toolkit to process and extract text data. Students will then start a Natural Language Processing project with Yelp data before we move onto Sentiment Analysis to predict positive versus negative Yelp reviews.
Session VIII : Big Data with Spark and Splunk
Students will be introduced to Big Data and data engineering with the Hadoop ecosystem, the MapReduce paradigm, and the up-and-coming Apache Spark.
Session IX : Deep Learning and Time Series
We will be introducing deep learning and training neural network and visualizing what a neural network has learned using TensorFlow Playground. Students will also learn time series, what makes them special, loading, and handling time series in Pandas. Understand how seasonality affects trends.
Session X : Computer Vision with OpenCV
Students will be introduced to computer vision fundamentals using OpenCV to detect faces, people, cars, and other objects.
Session XI : Hack Day
In this session, we will host a private Kaggle competition amongst the students. Each student willattempt the Kaggle challenge individually and will be ranked based on Kaggle ranking. To graduate and move to Project Based Learning (PBL) you must rank in the top 50 percentile for the competition.
Session XII : Career Planning
The last session is dedicated to polishing student’s resumes and LinkedIn to showcase their newfound skill sets and to creating an online portfolio of projectson respectable portfolio compliers such as GitHub.
Project Based Learning
Skill Building Project
Students will apply the Cross Industry Standard Process for Data Mining (CRISP-DM) standard in a provided dataset to understand the process behind starting a new project.
Students will undertake a new project from start to finish. This project will allow students to demonstrate their skills in data acquisition, data cleaning, data enrichment, modeling, evaluation, and deployment.
Students are given the option of choosing a project in a domain of their choosing.