Chapter 1 : Introduction to Data Science
Topic : What is data and types of data and different data sources
Content : primary data secondary data qualitative data quantitative data internal data external data sensor data 25 MB ,14:28 MIN , THEORY

Chapter 1 : Introduction to Data Science
Topic : Data, information, knowledge, understanding and wisdom
Content : data, information , knowledge and wisdom triangle 28 MB ,14:47 MIN , THEORY

Chapter 1 : Introduction to Data Science
Topic : difference between data and information
Content : distinguish between data and information 13 MB ,7:25 MIN , THEORY

Chapter 1 : Introduction to Data Science
Topic : introduction to high level languages
Content : High Level Languages 25 MB ,14:0 MIN , THEORY

Chapter 1 : Introduction to Data Science
Topic : IDLE
Content : components of idle 17 MB ,9:5 MIN , THEORY

Chapter 2 : Data Management
Topic : primary data collection methods
Content : qualitative data collection methods, quantitative data collection methods 31 MB ,16:14 MIN , THEORY

Chapter 1 : Introduction to Data Science
Topic : distinguish between primary data and secondary data
Content : difference between primary data and secondary data 11 MB ,7:3 MIN , THEORY

Chapter 1 : Introduction to Data Science
Topic : distinguish between primary data and secondary data
Content : difference between primary data and secondary data 11 MB ,7:3 MIN , THEORY

Chapter 2 : Data Management
Topic : secondary data collection methods
Content : data collection methods 11 MB ,6:26 MIN , THEORY

Chapter 1 : Introduction to Data Science
Topic : types of quantitative data
Content : counter, measurement of physical objects, sensory data, projection of data, quantification of qualitative data 14 MB ,8:10 MIN , THEORY

Chapter 2 : Data Management
Topic : data normalization
Content : min max normalization, decimal scaling and standard deviation 25 MB ,14:15 MIN , THEORY

Chapter 1 : Introduction to Data Science
Topic : types of qualitative data
Content : one-one interview, focus groups, process of observation, longitudinal studies, case studies 19 MB ,10:44 MIN , THEORY

Chapter 2 : Data Management
Topic : data analysis
Content : types of data analysis, process of data analysis 20 MB ,11:25 MIN , THEORY

Chapter 2 : Data Management
Topic : data collection methods
Content : primary data, secondary data 10 MB ,5:57 MIN , THEORY

Chapter 2 : Data Management
Topic : structured and unstructured data
Content : distinguish between structured and unstructured data 9 MB ,4:47 MIN , THEORY

Chapter 2 : Data Management
Topic : data analysis and data modelling
Content : Entity relationship model unified modelling language 17 MB ,9:21 MIN , THEORY

Chapter 1 : Introduction to Data Science
Topic : five v's of data
Content : volume, velocity, variety, value and veracity 9 MB ,5:33 MIN , THEORY

Chapter 2 : Data Management and Processing Systems
Topic : Introduction
Content : Explain Data Management 23 MB ,13:10 MIN , THEORY

Chapter 2 : Data Management
Topic : Data Cleaning/Extraction
Content : Explain data cleaning 23 MB ,11:54 MIN , THEORY

Chapter 1 : Introduction to Data Science
Topic : Exploratory Data Analysis (EDA) + Data Visualization
Content : Explain EDA and data visualization 31 MB ,16:59 MIN , THEORY

Chapter 3 : Data Curation
Topic : data curation
Content : Introduction 19 MB ,11:9 MIN , THEORY

Chapter 3 : Data Curation
Topic : Software Development Tools
Content : GitHub 14 MB ,8:29 MIN , THEORY

Chapter 3 : Data Curation
Topic : Security and Ethical Considerations in relation to authenticating and authorizing access to data on remote system
Content : Explain authentication and authorization for storage system 13 MB ,7:56 MIN , THEORY

Chapter 3 : Data Curation
Topic : Structured/Schema based systems as users and acquirers of data
Content : Explain what is structured and unstructured data in detail 17 MB ,9:20 MIN , THEORY

Chapter 3 : Data Curation
Topic : Web Crawling
Content : What is Web Scraping? 18 MB ,10:9 MIN , THEORY

Chapter 3 : Data Curation
Topic : Software Development Tools
Content : Version Control / Source Control 14 MB ,8:28 MIN , THEORY

Chapter 3 : Data Curation
Topic : Amazon Web Services
Content : Explain AWS in brief 24 MB ,14:55 MIN , THEORY

Chapter 3 : Data Curation
Topic : Paradigms of Distributed Database Storage
Content : Explain Paradigms of Distributed Database Storage 24 MB ,13:8 MIN , THEORY

Chapter 3 : Data Curation
Topic : Data Curation Life Cycle
Content : Explain Data Curation Life Cycle in detail 12 MB ,7:40 MIN , THEORY

Chapter 3 : Data Curation
Topic : Query Languages and Operations to Specify and Transform Data
Content : Query Languages 11 MB ,6:46 MIN , THEORY

Chapter 3 : Data Curation
Topic : NoSQl
Content : Write a short note on NoSQL 40 MB ,21:5 MIN , THEORY

Chapter 3 : Data Curation
Topic : Explain what is structured, semi-structured and unstructured data in detail
Content : XML 25 MB ,14:50 MIN , THEORY

Chapter 3 : Data Curation
Topic : Query Languages
Content : Relational Algebra 20 MB ,11:6 MIN , THEORY

Chapter 3 : Data Curation
Topic : Query Languages
Content : Aggregate/Group Functions 12 MB ,6:45 MIN , THEORY

Chapter 3 : Data Curation
Topic : Query Languages
Content : SQL Structured Query Language 21 MB ,11:41 MIN , THEORY

Chapter 3 : Da
Topic : XML
Content : XQUERY 16 MB ,9:55 MIN , THEORY

Chapter 3 : Data Curation
Topic : Semi-Structured Systems as Users and Acquirers of Data
Content : JSON 19 MB ,10:26 MIN , THEORY

Chapter 3 : Data Curation
Topic : Web Scraping
Content : What is Web Scraping? 6 MB ,3:54 MIN , THEORY

Chapter 3 : Data Curation
Topic : Lasso Regression
Content : Explain Lasso Regression 22 MB ,12:5 MIN , THEORY

Chapter 3 : Data Curation
Topic : HBase
Content : Write a short note on HBase 16 MB ,8:33 MIN , THEORY

Chapter 3 : Data Curation
Topic : XML
Content : XPath 22 MB ,11:50 MIN , THEORY

Chapter 3 : Data Curation
Topic : MongoDB
Content : Write a short note on MongoDB 65 MB ,40:19 MIN , THEORY + PRACTICAL

Chapter 5 : Data Transformation
Topic : Smoothing and Aggregating
Content : Smoothing 19 MB ,10:55 MIN , THEORY

Chapter 5 : Data Transformation
Topic : Introduction
Content : Explain the concept of data transformation 22 MB ,12:58 MIN , THEORY

Chapter 4 : Statistical Modelling and Machine learning
Topic : Introduction to Model Selection
Content : Introduction 20 MB ,11:39 MIN , THEORY

Chapter 4 : Statistical Modelling and Machine learning
Topic : Cross Validation
Content : Cross Validation 15 MB ,8:29 MIN , THEORY

Chapter 4 : Statistical Modelling and Machine learning
Topic : Bias/ Variance Trade off E.g. Parsimony
Content : Bias and Variance Trade Off 38 MB ,20:39 MIN , THEORY

Chapter 6 : Supervised Learning
Topic : Regression
Content : Explain Regression in detail with the help of an example 20 MB ,11:6 MIN , THEORY

Chapter 6 : Supervised Learning
Topic : Classification
Content : Explain classification in detail with help of an example 14 MB ,7:12 MIN , THEORY

Chapter 6 : Supervised Learning
Topic : Logistic Regression
Content : Explain Logistic regression with help of an example 12 MB ,6:13 MIN , THEORY

Chapter 7 : Unsupervised Learning
Topic : Introduction
Content : Explain unsupervised learning with help of an example 15 MB ,8:20 MIN , THEORY

Chapter 6 : Supervised Learning
Topic : Time Series Analysis
Content : Explain time series analysis with its components 19 MB ,10:19 MIN , THEORY

Chapter 7 : Unsupervised Learning
Topic : Ensemble Learning
Content : What is need of Ensemble Learning 23 MB ,12:24 MIN , THEORY

Chapter 6 : Supervised Learning
Topic : Forecasting
Content : What do you mean by Forecasting 21 MB ,12:33 MIN , THEORY

Chapter 4 : Statistical Modelling and Machine learning
Topic : AIC
Content : Explain AIC in detail with its mathematical formula 17 MB ,10:13 MIN , THEORY

Chapter 4 : Statistical Modelling and Machine learning
Topic : BIC
Content : Explain BIC in detail with its mathematical formula 19 MB ,11:48 MIN , THEORY

Chapter 4 : Statistical Modelling and Machine learning
Topic : Ridge Regression
Content : Explain Ridge Regression 25 MB ,13:54 MIN , THEORY

Chapter 5 : Data Transformation
Topic : Dimension Reduction
Content : Explain Dimensionality reduction with example 18 MB ,10:22 MIN , THEORY

Chapter 5 : Data Transformation
Topic : Aggregation
Content : What is aggregation in data transformation? 12 MB ,7:59 MIN , THEORY

Chapter 5 : Data Transformation
Topic : Methods for Dimensionality Reduction
Content : Explain various methods for dimensionality reduction 21 MB ,11:8 MIN , THEORY

Chapter 6 : Supervised Learning
Topic : K-Nearest Neighbour (K-NN)
Content : Explain KNN 30 MB ,16:55 MIN , THEORY

Chapter 7 : Unsupervised Learning
Topic : K-Means
Content : Explain K-Means Clustering algorithm with an example 20 MB ,11:17 MIN , THEORY

Chapter 6 : Supervised Learning
Topic : Separating Hyperplane
Content : Explain various ways to find the hyperplane 26 MB ,13:50 MIN , THEORY

Chapter 7 : Unsupervised Learning
Topic : Principal Component Analysis(PCA)
Content : Explain principal component analysis method with the steps required to find PCA 38 MB ,20:49 MIN , THEORY

Chapter 7 : Unsupervised Learning
Topic : Hierarchical Clustering
Content : Explain hierarchical Clustering along with its different approaches 15 MB ,8:12 MIN , THEORY

Chapter 4 : Statistical Modelling and Machine learning
Topic : Regularization
Content : Explain Regularization 19 MB ,10:50 MIN , THEORY