ABC’s of Data Analytics
- Data Science
- Data Scientist
- How does a Data Scientist differ
- Data - Intelligence - Insights
- Data Scientist Are Found at the Confluence
- Data Products
- Data Analysis
- Data Mining
- Machine Learning
- Learning Techniques
- Supervised Learning
- Unsupervised Learning
- Analytics
- Analytics Value Chain
- What is Data?
- Basis of data Categorization?
- Types of data
- Nominal data
- Ordinal data
- Discrete and Continues data
Raw data
Processed data
Data Collection Types
Forms of Data
Sources of Data
Data Quality Issues
Big Data Landscape
5 Vs of Big Data
Big Data : The Challenges
Tools of Data Processing
Anomaly Detection
Life Cycle Models
- Team Data Science Process (TDSP) – Agile
- Model
- Cross Industry Standard Process for Data Mining (CRISP-DM).
- Knowledge Discovery In Database (KDD)
Exploratory Data Analysis (EDA)
- Missing Data
- Noisy Data
- Inconstant Data
- Univariate, Bivariate and Multivariate
- Analysis.
Data Cleaning – Preprocessing
- Data Preparation
- Why is Data Preprocessing used?
- How is Data Preprocessing performed?
- Tasks of Data Preprocessing
- Data Profiling
- Data Wrangling
- Why Data Wrangling is used?
- How to Check Presence of Data Leakage?
- How is Data wrangling performed?
- Tasks of Data Wrangling
- Balanced and Imbalanced data
- Re-Sampling Data
- Random Under-Sampling
- Random Over-Sampling
- Imbalanced Data Handling Techniques
- SMOTE
- ADASYN
- Near Miss Algorithm
- Random Forest
- Simple Ensemble Methods
- o Max-Voting
- o Averaging
- o Weighted Averaging
- Advanced Ensemble Methods
- Bagging
- Boosting - ADA ,XG
- Stacking
- Navy Bayes
- Time Series Analysis
- Stationary series
- Trend
- Seasonality : ACF,PACF
- ARIMA Model
Machine Learning Algorithms
R –Programming
- Introduction
- Installation
- Data Types
- Data Structures and Vectors
- Data Frames
- List & Arrays & Matrix
- Factors
- Packages
- Importing Data Files
- Exporting & Reading Data in R
- Conditional statements
- Loops
- Functions
Python Programming
- Introduction
- Installation
- Object Oriented Programming Concepts
- Syntax
- Variables
- Data types and Comments
- Data structure
- Exporting & Reading Data in Python
- Conditional statements
- Loops - while,foor,break,repeat,next statements
- Data frame
- Object and Class
- Libraries
- Importing Data Files
- Functions
- Debugging python programs
- Scientific Computing
- Introduction to the Theano library.
- Introduction to the TensorFlow library.
- Introduction to the Keras library.
- Neural Network With Keras.
- Case Studies