Knowledgehut INC

      Data Science BootCamp

      Knowledgehut INC
      • Knowledgehut INC

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      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.

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

      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 
      • 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