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Data Science BootCamp Training

  • ID: 4828813
  • Training
  • 30 Days
  • KnowledgeHut Solutions
  • Training Dates: November 16, 2019
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Launch Your Career as a Data Scientist by Working on Real-World Projects and Data Sets

Weekend batch - training is four hours per day, Saturday and Sunday only.

  • 120 hours of Immersive Hands-on Instructor led Training
  • Learn by working on projects and data sets along with Mentors
  • Master Python, Machine Learning Methods, Data Science and Big Data Tools
  • Showcase abilities by building a portfolio with professional projects

Key Highlights

  • 120 hrs of Instructor-Led Sessions
  • 300+ hours of MCQs & Assignments
  • 14 Case studies and Projects
  • Immersive Practical Hands-on Workshops
  • Get timely support from specialized Mentors
  • Get taught by the practitioners
  • Career Mentoring Support provided

You Will Learn

  • Data Science Tools & Technologies
  • Statistics for Data Science
  • R for Data Science Foundation
  • Python for Data Science
  • Exploratory Data Analysis
  • Data Visualization using Python
  • Advanced Statistics & Predictive Modeling
  • Data Science Using Python
  • Machine Learning
  • Deep Learning
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1 Intro to Data Science

Topics:

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

Learning Outcome:

  • Get an idea of what is data science. Why data science is "Rosy" or "Handy" or "Fascinating"
  • Get acquainted with various analysis and visualization tools used in  data science

2 Probability & Statistics

Topics:

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

Learning Outcome:

  • Visit basics like mean (expected value), median and mode
  • Distribution of data in terms of variance, standard deviation and interquartile range
  • Basic summaries about the data and the measures. Together with simple graphics analysis
  • Basics of probability with daily life examples
  • Marginal probability and its importance with respect to data science
  • Learn Baye's theorem and conditional probability
  • Learn alternate and null hypothesis, Type1 error, Type2 error, power of the test, p-value,"

3 Basics of Python for Data Science

Topics:

  • 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

Learning Outcome:

  • Get a taste of how to start work with data in Python. Learn how to define variables, sets and conditional statements, the purpose of having functions and how to operate on files to read and write data in Python. Learn how to use pandas, a must-have package for anyone attempting data analysis in Python
  • Learn to visualization data using Python libraries like matplotlib, seaborn and ggplot

4 Basics of R for Data Science

Topics:

  • 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

Learning Outcome:

  • Learn the basics of R and write your own R scripts. Use R to solve problems related to data science. Learn vectors, lists, matrix, arrays and data frames. Read and write data in R
  • Learn to visualization data using R; Grammar of Graphics and ggplot2 and create beautiful graphics and charts

5 Exploratory Data Analysis

Topics:

  • Data Transformation & Quality Analysis
  • Exploratory Data Analysis

Learning Outcome:

  • It is essential to transform raw data. Learn to Merge, Rollup, Transpose and Append, analyze Missing data, detect Outliers treat them
  • Summarising Important Characteristics of Data, Univariates, Bivariates, Crosstabs, Covariance and Correlation

6 Linear Regression

Topics:

  • ANOVA
  • Linear Regression (OLS)
  • Case Study: Linear Regression

Learning Outcome:

  • Analysis of Variance and its practical use
  • Linear Regression with Ordinary Least Square Estimate to predict a continuous variable. It covers strong concepts, model building, evaluating model parameters, measuring performance metrics on Test and Validation set. Further it covers enhancing model performance by means of various steps like feature engineering & regularization
  • Real-Life Case Study with Linear Regression

7 Logistic Regression

Topics:

  • Logistic Regression
  • Case Study: Logistic Regression

Learning Outcome:

  • Binomial Logistic Regression for Binomial Classification Problems. Covers evaluation of model parameters, model performance using various metrics like sensitivity, specificity, precision, recall, ROC Curve, AUC, KS-Statistics, Kappa Value
  • Real-Life Case Study with Binomial Logistic Regression

8 Dimensionality Reduction

Topics:

  • Principal Component Analysis (PCA)
  • Factor Analysis
  • Case Study: PCA/FA

Learning Outcome:

  • Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. Covers techniques to find the optimum number of components/factors using scree plot, one-eigenvalue criterion
  • Real-Life case study with PCA & FA

9 Decision Trees

Topics:

  • 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

Learning Outcome:

  • Decision Trees - for regression & classification problem. Covers both Classification & regression problem. Candidates get knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index, CHAID
  • Real Life Case Study with Decision Tree

10 Time Series Forecasting

Topics:

  • 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

Learning Outcome:

  • Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data
  • Understand the Exponential Smoothing Model and when to use the same
  • Use Holt's model when your data has both Constant Data and Trend Data. How to select the right smoothing constants.
  • Use Holt's model when your data has Constant Data, Trend Data and Seasonal Data. How to select the right smoothing constants.
  • Use Auto-Regressive Integrated Moving Average Model for building Time Series Model
  • Real Life Case Study with ARIMA

11 Introduction to Machine Learning

Topics:

  • 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

Learning Outcome:

  • Look at real-life examples of Machine Learning and how it affects society in ways you may not have guessed! Explore many algorithms and models like Classification, Regression, Clustering. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning
  • Understand various optimization techniques like Batch Gradient Descent, Stochastic Gradient Descent, ADAM, RMSProp

12 Supervised Learning

Topics:

  • Linear Regression (SGD)
  • Logistic Regression (SGD)
  • Neural Network (ANN)
  • Support Vector Machines

Learning Outcome:

  • Learn Linear Regression with Stochastic Gradient Descent with real-life case study. Covers hyper-parameters tuning like learning rate, epochs, momentum
  • Learn Logistic Regression with Stochastic Gradient Descent with real-life case study. Covers hyper-parameters tuning like learning rate, epochs, momentum and class-balance
  • Learn Artificial Neural Network with real-life case study. Covers hyper-parameters like number of hidden layers, number of neurons in each hidden layer, activation function to be used in the hidden & output layers
  • Learn how Support Vector Machines can be used for a classification problem with real-life case study. Covers hyper-parameter tuning like regularization

13 Unsupervised Learning

Topics:

  • K-Means Clustering
  • Hierarchical Clustering

Learning Outcome:

  • Learn about unsupervised learning technique - K-Means Clustering and Hierarchical Clustering with real-life case study

14 Recommender Engines

Topics:

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

Learning Outcome:

  • Hands-on implementation of Association Rules. Use Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift. Also known as Market Basket Analysis when applied in the retail domain
  • Learn what is UBCF and how is it used in Recommender Engines. Covers concepts like cold-start problems
  • Learn what is IBCF and how is it used in Recommender Engines
  • Real Life Case Study with Recommender Systems

15 Ensemble Machine Learning

Topics:

  • Ensemble Techniques
  • 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

Learning Outcome:

  • Cover basic ensemble techniques like averaging, weighted averaging & max-voting
  • Learn about bootstrap sampling and its advantages
  • Learn about aggregating bootstrap sample models
  • Learn Random Forest with real-life case study and how it helps avoid overfitting compared to decision trees
  • Boost model performance with Boosting
  • AdaBoost which uses Boosting technique to enhance its model performance
  • Learn about Gradient Boosting Method with real-life case study
  • Real-life case study with heterogeneous ensemble machine learning techniques

16 Neural Networks

Topics:

  • The Biological Inspiration
  • Multi-Layer Perceptrons
  • Activation Functions
  • Backpropagation Learning
  • Case Study: Multi-Class classification

Learning Outcome:

  • "Learn advanced machine learning techniques using the Neural Networks algorithms. Neural Networks can enable pattern recognition based on a large number of inputs. Learn how NN algorithms work, and end up with an introduction to deep learning Covers various activation functions like sigmoid, hyperbolic-tangent, Rectified Linear Units, Leaky Rectified Linear Units"
  • Real-life case study in Multi-Class classification

17 Deep Learning

Topics:

  • 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

Learning Outcome:

  • Learn how to build convolutional networks and use them to classify images (faces, melanomas, etc.) based on objects that appear in them. Use these networks to learn data compression and image denoising
  • Use modern deep learning frameworks (Keras, TensorFlow) to build multi-layer neural networks, and analyze real data
  • Real life case study on Neural networks using deep learning frameworks (Keras, Tensorflow)
  • Learn to install H2O and use it to build models on large datasets
  • Real life case study on Neural networks using H2O
  • Build your own recurrent networks and long short-term memory networks with Keras and TensorFlow; perform sentiment analysis and generate new text
  • Real life case study using LSTM

18 Natural Language Processing (NLP)

Topics:

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

Learning Outcome:

  • Become an expert in the main components of Natural Language Processing, including speech recognition, sentiment analysis, and machine translation. You’ll learn to code probabilistic and deep learning models, train them on real data
  • Real life case study using NLP

19 Capstone Project

Topics:

  • Industry-relevant capstone project under experienced industry-expert mentor

Learning Outcome:

  • An industry mentor guided group project to handle a real-life project. The same way you would execute a data science project in any business problem

20 Interview Preparation

Topics:

  • Mock Interview - 2 sessions

Learning Outcome:

  • Prepare yourself for the interview. Mock interviews to have you grilled through what you have learned throughout the course
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This data science bootcamp has been designed for people with prior experience in statistics and programming, such as Engineers, software and IT professionals, analysts, and finance professionals.

Pre-requisites

  • Coding experience with a general-purpose 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.
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