Data Science Advanced Bootcamp, Chennai

Binary Chefs

WHO CAN APPLY?

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.

PRE-REQUISITES

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.

Course Content

Introduction to Python
Fundamental Statistics
Modelling and Forecasting
Databases
Power BI
Deep Learning
Introduction to Python

• 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
Fundamental Statistics

Visualizing relationships in data
Seeing relationships in data.
Making predictions based on data.
Simpson's paradox.

Probability
Introduction to Probability.
Bayes Rule.
Correlation vs. Causation.

Estimation
Maximum Likelihood Estimation.
Mean, Median, Mode.
Standard Deviation and Variance.

Outliers and Normal Distribution
Outliers, Quartiles.
Binomial Distribution.
Manipulating Normal Distribution.

Inference
Confidence Intervals.
Hypothesis Testing.

Regression
Linear regression.
Correlation.
Modelling and Forecasting

Main theme:

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
Introduction to MySQL 

MYSQL
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

MongoDB
• 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
Microsoft Power BI
• Getting data from various data sources
• Data Modeling
• Data Visualizations
• Publishing and sharing
• Introduction to DAX
Introduction to deep learning
• Neural Networks Basics
• Shallow neural networks
• Deep Neural Networks
• Optimization algorithms
• Hyper parameter tuning, Batch Normalization and Programming Frameworks
• Convolutional Neural Networks

let's start

Have questions? Connect with us today. 
Call: +91 79042 52907
Binary Chefs 2, 1st Floor, Alwarpet Street, Chennai
(+91) 79 04 25 29 07

WRITE TO US!

We will revert within 24 Hours!

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