Data Science BootCamp- LIVE VIRTUAL
Course
Online
Launch your career as a Data Scientist by working on real-world projects and data sets!
-
Type
Course
-
Methodology
Online
-
Duration
4 Weeks
-
Start date
Different dates available
-
Online campus
Yes
-
Delivery of study materials
Yes
-
Support service
Yes
-
Virtual classes
Yes
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.
Facilities
Location
Start date
Start date
About this course
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.
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.
Reviews
This centre's achievements
All courses are up to date
The average rating is higher than 3.7
More than 50 reviews in the last 12 months
This centre has featured on Emagister for 7 years
Subjects
- 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
Course programme
- 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