Binary Chefs

Anyone who is inquisitive about Data, ready to put in the challenge of mastering multiple intersecting disciplines such as Analytics, Statistics, Mathematics, Business domain knowledge. It takes a lot to become a Data Scientist.

Knowledge of math’s and statistics until the 12th grade level. Past coding experience is ideal.

Note: If you do not know coding, we will teach you the basics to prepare you for the Bootcamp program.

Note: If you do not know coding, we will teach you the basics to prepare you for the Bootcamp program.

• Installing Python & Jupyter Notebook

• Setup & Hello World

• Drawing a Shape

• Variables & Data Types

• Working with Strings

• Working with Numbers

• Getting Input from Users

• Building a Basic Calculator

• Mad Libs Game

• Lists

• List Functions

• Tuples

• Functions

• Return Statement

• If Statements

• If Statements & Comparisons

• Building a better Calculator

• Dictionaries

• While Loop

• Building a Guessing Game

• For Loops

• Exponent Function

• 2D Lists & Nested Loops

• Building a Translator

• Comments

• Try / Except

• Reading Files

• Writing to Files

• Modules & Pip

• Classes & Objects

• Building a Multiple Choice Quiz

• Object Functions

• Inheritance

• Python Interpreter

Seeing relationships in data.

Making predictions based on data.

Simpson's paradox.

Introduction to Probability.

Bayes Rule.

Correlation vs. Causation.

Maximum Likelihood Estimation.

Mean, Median, Mode.

Standard Deviation and Variance.

Outliers, Quartiles.

Binomial Distribution.

Manipulating Normal Distribution.

Confidence Intervals.

Hypothesis Testing.

Linear regression.

Correlation.

Probabilistic versus non-probabilistic modeling and supervised versus unsupervised learning

o classification

o regression

o clustering methods

o sequential models

o matrix factorization

o topic modeling and model selection

Methods

• linear and logistic regression

• support vector machines

• tree classifiers

• boosting

• maximum likelihood and MAP inference

• EM algorithm

• hidden Markov models

• Kalman filters

• k-means

• Gaussian mixture models

o Understanding Relational Databases

o Queries to Extract Data from Single Tables

o Queries to Summarize Groups of Data from Multiple Tables

o Queries to Address More Detailed Business Questions

• Data Extraction Fundamentals

• Data in More Complex Formats

• Data Quality

• Data Modelling in MongoDB

• Introduction to PyMongo

• Field Queries

• Examples of Aggregation Framework

• The Aggregation Pipeline

• Aggregation Operators: $match, $project, $unwind, $group

• Getting data from various data sources

• Data Modeling

• Data Visualizations

• Publishing and sharing

• Introduction to DAX

• Neural Networks Basics

• Shallow neural networks

• Deep Neural Networks

• Optimization algorithms

• Hyper parameter tuning, Batch Normalization and Programming Frameworks

• Convolutional Neural Networks

We will revert within 24 Hours!