Knowledgehut INC

Data Science BootCamp

Knowledgehut INC
  • Knowledgehut INC

Preis auf Anfrage

Wichtige informationen

Tipologie Kurs
Methodologie Online
Dauer 4 Weeks
Beginn nach Wahl
Online Campus Ja
Versendung von Lernmaterial Ja
Beratungsservice Ja
Virtueller Unterricht Ja
  • Kurs
  • Online
  • Dauer:
    4 Weeks
  • Beginn:
    nach Wahl
  • Online Campus
  • Versendung von Lernmaterial
  • Beratungsservice
  • Virtueller Unterricht

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.

Wichtige informationen
Ist dieser Kurs für mich?

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.

Voraussetzungen: 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.

Einrichtungen (1)
Wo und wann
Beginn Lage
nach Wahl
Beginn nach Wahl

Was lernen Sie in diesem Kurs?

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