# Data Science BootCamp- LIVE VIRTUAL

Kurs

Online

Preis auf Anfrage

Beschreibung

• Tipologie

Kurs

• Methodologie

Online

• Dauer

4 Weeks

• Beginn

nach Wahl

• Online Campus

Ja

• Versendung von Lernmaterial

Ja

• Beratungsservice

Ja

• Virtueller Unterricht

Ja

Beschreibung

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

Lage

Beginn

Online

Beginn

nach Wahl Anmeldung möglich

Themen

• 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

### Inhalte

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

Data Science BootCamp- LIVE VIRTUAL

Preis auf Anfrage