Data Science for Professionals
Data Science for Professionals
Our Data Science for Professionals program is based on our Applied Labs teaching concept with 100% hands-on learning. The program runs for 7 days (Sunday–Saturday), and features 100 plus hours with theDevMasters team (70 hours in-class, 20 hours pre-class, and 30 hours of post-class mentoring).
What is Applied Labs?
Simply defined, Applied Labs not your typical classroom, college or boot camp style learning. In Applied Labs every assignment and, project is designed to build your programming, analytical, statistical, and domain expertise skills. We focus on peer-to-peer learning, bringing statisticians, programmers, and domain experts or entrepreneurs together to learn from and teach each other.
Who should attend this program?
This program is geared towards professionals from various backgrounds, and no specific background is required other than completing our pre-course material. This program is very fast paced, and while we do everything in our power to ensure all students keep up we’d recommend students who anticipate needing extra time to consider one of our other programs such as Mastering Applied Data Science. As with our other programs we’re proud to offer our 100% repeat guarantee – you’re welcome to return for another session for free in case you’d like to review any of the content.
Prerequisite Learning :
- Mode of delivery (Webinar, In-class Self-Learning, Mentoring)
- Python 101
- Stats 101
- SQL 101
- Web 101
All of the material will be provided to you
Software, Hardware and Cloud Requirements: A laptop
Any operating system ( Windows, Mac, or Linux) is fine.
Amazon Web Services account(requires credit card) for big data.
Required software will be provided and installed by staff in class
Session I : Python 101
Whether you are familiar with programming or not, our Python PreWork sessions introduce the fundamentals of Python, such as variables, string fundamentals, if-else statements, try & except statements, for loops, while loops, break & continue statements, & lambda functions, as well as certain data types relevant to data science, like lists, tuples, dictionaries, & sets for beginning exposure. The activities done in these sessions will be guides to student’s questions moving forward in the classes.
Session II : Statistics 101
The hands-on portion of statistics in PreWork is to establish the surface level understanding of concepts such as mathematical variables, like numerical vs categorical, nominal vs ordinal, interval vs discrete; measurements of statistics, like when to use mean, when to consider median, & when to revised to mode; relationship between variables, like correlation & independence; ending with hypothesis testing & p-value, but only to the degree of applying the mindset towards data science. These concepts will be reviewed in the program to ensure that student’s clarifications are addressed.
Session III : SQL 101
While some of the tools used in Python will take the place of SQL functions & methods, it is still beneficial to understand the origins of these tools as well as be able to replicate them when applied in future work’s expectations. A solid portion of demand in data science jobs ask for big-query experience with SQL, like Microsoft SQL & PostgreSQL vs NoSQL, like MongoDB & DynamoDB, which we will glimpse at scenarios to further solidify the students’ candidacy.
Session IV : Web 101
An introduction to HTML & CSS is key to future project building & publications of the blog posts of student progress throughout the program. A proportion of relevant data is out there in the web for us to utilize & using the most open source methods, like HTML & CSS to be able to grab that information within our Python environments will be introduced in Day 3 & furthermore, once students are in Project Based Learning, GitHub portfolios are best displayed in themes that students choose & customize with HTML & CSS.
Data Science Applied Labs
Session I : Introduction to Data Science overview
We will start by reviewing why Data Science is getting such attention and why data driven companies are predicted to outperform their competition. Furthermore; introducing the Cross Industry Standard Process for Data Mining (CRISP-DM) and data mining with supervised learning and unsupervised learning. We will also review fundamentals of data science, and machine learning using python.
Session II : Exploring Data using techniques Data Visualization
The afternoon session will start by introducing NumPy and Pandas and showcasing how to clean, manipulate, and analyze data. Students will practice on the Titanic dataset before moving onto web scraping techniques and extracting data from APIs.
Session I : 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. Using Matplotlib and Seaborn packages. Students will also learn how to handle categorical data using dummies.
Session II : Time Series
Students will also learn time series, what makes them special, loading and handling time series in Pandas. Understand how seasonality affects trends. Build a time series ARIMA models
Session I : Machine Learning concepts
Starting with a review of regression based machine learning algorithms such as simple linear, multivariable, logistic, ridge, and lasso regression. Students will explore these machine learning algorithms with examples during the first half of the day such as linear regression, logistic regression, ending the day with ridge and lasso regression.
Session II : Advanced Machine learning concepts
We will dive into classification algorithms, such as Naïve Bayes, Decision Trees, Random Forest,and other methods like gradient descent. Along with scoring using precision, recall, sensitivity, specificity, and accuracy score, AUC, and ROC.
Students will complete two projects based on the first three days of instruction. This day is 100% hands-on.
Session I :
Choice of one estimation project. Examples include: real estate pricing, stock market prediction, etc.
Session II :
Choice of one classification project . Examples include: predicting survival chances for titanic passengers, customer loan repayment etc
Session I : 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 II : 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 Sentimental Analysis to predict positive versus negative Yelp reviews.
Session I : Big Data with Spark
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 II : Computer Vision with OpenCV and Hack Project
Students will be introduced to computer vision fundamentals using OpenCV to detect faces, people, cars, and other objects. We will conclude the day with a hack challenge. Students will be grouped into teams and will showcase their group project at the end of class.
Session III : Deep Learning
We will be introducing deep learning and training neural network and visualizing what a neural network has learned using TensorFlow Playground
Session II : Hack Day
There are 2 projects divided into 2 session. This is 100% hand on day. This projects are based on your first 2 days and prerequisite learning.
Session I :
Choice of one project based on NLP, Recommendation system
Session II :
Choice of one project based on Computer vision, deep learning