Knowledgehut INC

      Data Science BootCamp

      Knowledgehut INC
      Online
      • Knowledgehut INC

      Preço para verificar
      CURSO PREMIUM

      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:
        4
      • Início:
        Datas a escolher
      • Campus online
      • Envio de material didático
      • Serviço de informação
      • Aulas virtuais
      Descrição

      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.

      Instalações (1)
      Instalações e datas
      Início Localização
      Datas a escolher
      Online
      Início Datas a escolher
      Localização
      Online

      O que se aprende nesse curso?

      NLP
      Networks
      Statistics
      Programming
      Probability
      Analytics
      Data science
      Exploratory Data Analysis
      Linear Regression
      Logistic Regression
      Anova
      Time Series Data
      Parametric algorithm
      Non parametrics algorithm
      Machine learnig

      Programa

      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 
      • ANOVA
      • 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
      • ARIMA
      • 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