Knowledgehut INC

Data Science BootCamp

Knowledgehut INC
  • Knowledgehut INC

Preço para verificar

Informação importante

Tipologia Curso
Metodologia Online
Duração 4
Início Datas a escolher
Campus online Sim
Envio de material didático Sim
Serviço de informação Sim
Aulas virtuais Sim
  • Curso
  • Online
  • Duração:
  • Início:
    Datas a escolher
  • Campus online
  • Envio de material didático
  • Serviço de informação
  • Aulas virtuais

The Data Science Bootcamps conducted are interactive in nature and fun to learn as a substantial amount of time is spent on hands-on practical training, use-case discussions, and quizzes.

Informação importante
Esta formação é para mim?

This course has been designed for people with prior experience in statistics and programming, such as Engineers, software and IT professionals, analysts, and finance professionals.

Requisitos: Coding experience with a generalpurpose programming language (e.g., Python, R, Java, C++) is preferred. Comfortable with basic mathematics and statistics - probability and descriptive statistics, including concepts like mean and median, standard deviation, distributions, and histograms.





Datas a escolher Inscrições abertas

O que se aprende nesse curso?

Data science
Exploratory Data Analysis
Linear Regression
Logistic Regression
Time Series Data
Parametric algorithm
Non parametrics algorithm
Machine learnig


Module 1 Intro to Data Science
  • What is Data Science?
  • Analytics Landscape
  • Life Cycle of a Data Science Projects
  • Data Science Tools & Technologies

Module 2 Probability & Statistics
  • Measures of Central Tendency
  • Measures of Dispersion
  • Descriptive Statistics
  • Probability Basics
  • Marginal Probability
  • Bayes Theorem
  • Probability Distributions
  • Hypothesis Testing

Module 3 Basics of Python for  Data Science 
  • Python Basics
  • Data Structures in Python
  • Control & Loop Statements in Python
  • Functions & Classes in Python
  • “Working with Data”
  • Analyze Data using Pandas
  • Data Visualization in Python

Module 4 Basics of R for Data Science 
  • Intro to R Programming
  • “Data Structures in R Control & Loop Statements in R”
  • “Functions and Loop Functions in R”
  • “String Manipulation & Regular Expression in R”
  • “Working with Data in R”
  • Handling missing values in R
  • Data Visualization in R

Module 5 Exploratory Data Analysis 
  • Data Transformation & Quality Analysis
  • Exploratory Data Analysis

Module 6 Linear Regression 
  • Linear Regression (OLS)
  • Case Study: Linear Regression

Module 7 Logistic Regression 
  • Logistic Regression
  • Case Study: Logistic Regression

Module 8 Dimensionality Reduction 
  • Principal Component Analysis (PCA)
  • Factor Analysis
  • Case Study: PCA/FA

Module 9 Decision Trees 
  • Introduction to Decision Trees
  • Entropy & Information Gain
  • Standard Deviation Reduction (SDR)
  • Overfitting Problem
  • Cross Validation for Overfitting Problem
  • Running as a solution for Overfitting
  • Case Study: Decision Tree

Module 10 Time Series Forecasting 
  • Understand Time Series Data
  • Visualizing TIme Series Components
  • Exponential Smoothing Holt’s Model
  • Holt-Winter’s Model
  • Case Study: Time Series Modeling on Stock Price

Module 11 Introduction to Machine Learning 
  • Machine Learning Modelling Flow
  • How to treat Data in ML
  • Parametric & Non-parametric ML Algorithm
  • Types of Machine Learning
  • Performance Measures
  • Bias-Variance Trade-Off Overfitting & Underfitting
  • Optimization

Module 12 Supervised Learning 
  • Linear Regression (SGD)
  • Logistic Regression (SGD)
  • Neural Network (ANN)
  • Support Vector Machines

Module 13 Unsupervised Learning 
  • K-Means Clustering
  • Hierarchical Clustering

Module 14 Recommender Engines
  • Association Rules
  • User-Based Collaborative Filtering
  • Item-Based Collaborative Filtering
  • Case Study: Build a Recommender Engine

Module 15 Ensemble Machine Learning 
  • Ensemble Technqiues
  • Bootstrap Sampling Bootstrap Aggregation (Bagging)
  • Supervised Learning - Random Forest
  • Boosting
  • Supervised Learning - AdaBoost Algorithm
  • Supervised Learning - Gradient Boosting Machine
  • Case Study: Heterogeneous Ensemble Machine Learning

Module 16 Neural Networks 
  • The Biological Inspiration
  • Multi-Layer Perceptrons
  • Activation Functions
  • Back propagation Learning
  • Case Study: Multi-Class classification

Module 17 Deep Learning 
  • Convolutional Neural Networks (CNN)
  • Introducing Tensorflow
  • Neural Networks using Tensorflow
  • Introducing Keras 
  • Case Study: Neural Networks using Tensorflow
  • Case Study: Neural networks using Keras Introducing H2O
  • Case Study: Neural networks using H2O
  • Recurrent Neural Networks (RNN)
  • Long Short Term Memory (LSTM)
  • Case Study: LSTM RNN with Keras

Module 18 Natural Language Processing (NLP) 
  • Natural Language Processing (NLP)
  • Case Study: Case Study using NLP

Module 19 Capstone Project 
  • Industry relevant capstone project under experienced industry-expert mentor

Module 20 Interview Preparation 
  • Mock Interview - 2 sessions