As a VI student, you will be given access to online learning materials in our e-learning portal.

To get you ready for learning, this essential pre-work will familiarize you with the basics of the key concepts and tools we will be using throughout the course.

Although you will learn these topics remotely before you arrive in class, you won’t be far away from the resources of the VI community. Make use of our Telegram channel to leverage connections with students, alumni, instructors and experts. At the end of your pre-work, you’ll be ready for the fast pace on campus!

After the course, you can choose to participate in follow-up sessions with your instructor, either in a group and/or individually, included as part of the course fee.

• Welcome to data science  
• Introduction to Python Fundamentals 
  • Why Python?
  • Introduction to Data Types
  • Introduction to Jupyter Notebook
  • Python Variables
  • Arithmetic, relational and logical operators
  • Python Datatypes (String, lists, tuples, dictionaries)
  • Indexing and printing
  • Documentation of codes

• Python 
  • String functions (indexing, concatenation and formatting)
  • Control flow (if…else statements, for and while loops, try and except)
  • Importing Packages

• Numpy 
  • Ndarray object
  • Basic operations of ndarrays 
  • Indexing and iterations
• Pandas 
  • Dataframe & series
  • Basic operations of series (arithmetic operations, evaluating values)
  • Basic operations of dataframes (mathematical operations)
  • Importing files into dataframes
  • Joins in Pandas

• Data Visualization- Matplotlib & Seaborn
  • Barplot, histogram/density plot, line chart, scatter plot, boxplot, heatmap
  • Graph parameters (changing size, color, style markers, titles, legends and label orientation) in matplotlib
• Data Cleaning and Exploratory Data Analysis 
  • Introduction to data cleaning
  • Common steps in Data Cleaning
  • Exploratory Data Analysis (filtering and sorting, column manipulation, group by/aggregate functions, handling missing data, using functions)

• Intro to Modeling: Linear Regression
  • Modeling and Predictions
  • Introduction to Linear Regression
  • Introduction to scikit-learn package (fitting the data, comparing models)
  • Introduction to Statsmodels
  • Feature Scaling
• Pre-processing tools
  • Standardization
  • MinMax

• K-nearest neighbors 
  • Introduction to Classification
  • Introduction to KNN
  • Advantages and Drawbacks of KNN
  • Training knn using scikit-learn using Iris dataset
• Logistic Regression 
  • Binary Class
  • Probability estimation dilemma
  • Odds Ratio
  • Log Odds
• Decision Trees and Random Forest Classification
  • Algorithm walk-through
  • Training decision tree using scikit-learn using the Titanic dataset
  • Training random forest scikit-learn

• GridSearch 
  • Review of initial EDA strategies
  • Intuition behind GridSearch
  • Implement changes and updates to KNN model using GridSearch
  • Find optimal hyperparameters of a model
• Final Project Presentation 

Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together.

Python’s simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms, and can be freely distributed.

This introductory bootcamp consists of 7 modules to be completed. Our course schedule can either be in a bootcamp (3 full days) or a part-time (7 three-hour lessons). The course is rigorous in nature with class practices, assignments, and capstone project work.

VI courses are created and led by industry practitioners from tech giants such as Apple, Amazon and Alibaba. They combine in-depth experience as practitioners with a passion for nurturing the next generation of tech talent.

All our courses are part-time and classroom-based, held on weekends or weekday evenings to minimize disruption to regular business hours.

Our students will have access to our e-learning portal where they can access course materials and submit assignments before class.

This program is designed for beginners with no prerequisites. Students completely new to data science will have access to pre-work in our e-learning portal to help you prepare for the course. If you do have existing experience with the Python programming, our program will increase your proficiency with best programming practices.

Upon successful completion of the course, students will be able to:

• Acquire knowledge about fundamental programming concepts and the Python programming language
• Code comfortably in Python and understand control flow and conditional programming
• Ingesting data from various files to a pandas dataframe; Manipulate data using pandas operations and methods
• Perform exploratory data analysis with python and pandas dataframe and series
• Understand which data visualization to use to effectively present data
• Build and refine machine learning models to predict patterns from data sets
• Tune data parameters for advanced model evaluation
• Build and refine machine learning models to predict patterns from data sets
• Tune data parameters for advanced model evaluation
• Communicate data driven insights to a technical and non-technical audience alike

Upon successful completion of the course, participants will be awarded a digital certificate by Vertical Institute. VI alumni use their course certificate to demonstrate skills to employers and their LinkedIn network.

Our data science course is well-regarded by top companies, who contribute to our curriculum and use our courses to train their own teams.