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Data Sciences

Business intelligence (BI) is often described as "the set of techniques and tools for the transformation of raw data into meaningful and useful information for business analysis purposes".
Course Info

Business intelligence (BI) is often described as "the set of techniques and tools for the transformation of raw data into meaningful and useful information for business analysis purposes". The term "data surfacing" is also more often associated with BI functionality. BI technologies are capable of handling large amounts of unstructured data to help identify, develop and otherwise create new strategic business opportunities.

Course Detail:

The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course:

  • The first is a conceptual introduction to the ideas behind turning data into actionable knowledge.
  • The second is a practical introduction to the tools that will be used in the program

These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.

Course Fee : Rs 12,500 per month

Course Overview
1. Introduction
1.1 What is AI and ML
1.2 AI as compared to Conventional Computing
1.3 ML, AI, Data Science and Big Data
1.4 Practical Scenarios: Smart Robots, Self Driving Cars and Genetic Engineering
2. Data Exploration & Visualization
2.1 Variable Identification
2.2 Missing Value Treatment
2.3 Variable Transformation
2.4 Common methods of Variable Transformation
2.5 Correlation
2.6 Histograms
2.7 Density Plots
2.8 Correlation Matrix Plot
2.9 Scatterplot Matrix
2.10 Correlation Matrix Plot
3. Predictive Analytics, Classification and Regression
3.1 Linear Algebra and Problem Equations
3.2 Extracting Predictor and Target Variables from Dataset
3.3 Building Problem Equation
3.4 What is Classification?
3.5 Linear Classification
3.6 Decision Trees
3.7 Naive Bayes
3.8 Conditional Probability
3.9 Bayes' Rule
3.10 Independence
3.11 Linear Regression
3.12 Logistic Regression
3.13 Regularization
3.14 Cost function for Logistic Regression
3.15 Applications
4. Evaluation and Testing
4.1 Train/Test Split
4.2 Accuracy
4.3 Precision
4.4 Recall
4.5 Cross-Validation
4.6 Bias/Variance Tradeoff
4.7 Classification Metrics
4.8 Regression Metrics
5. Unsupervised Learning and Clustering
5.1 Clustering
5.2 K-Means Clustering
5.3 Applications
6. Natural Language Processing and Text Analytics
6.1 Unstructured/Raw Data
6.2 Clensing: Stemming, Lemmatization, Stopwords Removal
6.3 Vectorizers
6.4 Count Vectorizer
6.5 Hashing Vectorizer
6.6 TF-IDF
7. Recommendation Systems
7.1 Introduction
7.2 Collaborative Recommendations
7.3 Content-based Recommendations
7.4 Text Analytics
8. Neural Networks and Deep Learning
8.1 Neural Networks Introduction
8.2 Layers and Perceptrons
8.3 Perceptron Learning Procedure
8.4 Backpropagation
8.5 Recurrent Neural Networks
8.6 Introduction to Deep Learning
8.7 Applications