About Data Mining Certification Training
What is Data Mining?
Big data!!! Are you demotivated when your peers are discussing about data science and recent advances in big data. Did you ever think how Flip kart and Amazon are suggesting products for their customers? Do you know how financial institutions/retailers are using big data to transform themselves in to next generation enterprises? Do you want to be part of the world class next generation organisations to change the game rules of the strategy making and to zoom your career to newer heights?
Here is the power of data science in the form of Data mining concepts which are considered most powerful techniques in big data analytics.
Data Mining with R unveils underlying amazing patterns, wonderful insights which go unnoticed otherwise, from the large amounts of data. Data mining tools predict behaviours and future trends, allowing businesses to make proactive, unbiased and scientific-driven decisions. Data mining has powerful tools and techniques that answer business questions in a scientific manner, which traditional methods cannot answer. Adoption of data mining concepts in decision making changed the companies, the way they operate the business and improved revenues significantly.
Companies in a wide range of industries such as Information Technology, Retail, Telecommunication, Oil and Gas, Finance, Health care are already using data mining tools and techniques to take advantage of historical data and to create their future business strategies.
Data mining can be broadly categorized into two branches i.e. supervised learning and unsupervised learning. Unsupervised learning deals with identifying significant facts, relationships, hidden patterns, trends and anomalies. Clustering, Principle Component Analysis, Association Rules, etc., are considered unsupervised learning. Supervised learning deals with prediction and classification of the data with machine learning algorithms. Weka is most popular tool for supervised learning.
Topics You Will Learn…
- Introduction to datamining
- Dimension reduction techniques
- Principal Component Analysis (PCA)
- Singular Value Decomposition (SVD)
- Association rules / Market Basket Analysis / Affinity Filtering
- Recommender Systems / Recommendation Engine / Collaborative Filtering
- Network Analytics – Degree centrality, Closeness Centrality, Betweenness Centrality, etc.
- Cluster Analysis
- Hierarchical clustering
- K-means clustering
- Overview of machine learning / supervised learning
- Data exploration methods
- Basic classification algorithms
- Decision trees classifier
- Random Forest
- K-Nearest Neighbours
- Bayesian classifiers: Naïve Bayes and other discriminant classifiers
- Perceptron and Logistic regression
- Neural networks
- Advanced classification algorithms
- Bayesian Networks
- Support Vector machines
- Model validation and interpretation
- Multi class classification problem
- Bagging (Random Forest) and Boosting (Gradient Boosted Decision Trees)
- Regression analysis
Tools You Will Learn…
R is a programming language to carry out complex statistical computations and data visualization. R is also open source software and backed by large community all over the world who are contributing to enhancing the capability. R has many advantages over other tools available in the market and it has been rated No.1 among the data scientist community.