Data Science and Business Intelligence

Machine Learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this course is to give you in-depth knowledge of the various data analytics techniques that can be performed using R or Python and various statistical concepts. This course is packed with multiple real-time projects and hands-on exercises.

Duration: 35 Hours

Course Topics

1. Introduction
            What is Data Science and why we need
            Different tools of Data Science

2. R Or Python Programming

3. Data Management and Basic Statistics
            Data Types
            Data Cleaning/Transformation
            Measure of Central Tendency/Dispersion/Kurtosis
            Missing Value/Outlier Treatments

4. Inferential Statistics
            Hypothesis Testing
            Parametric Testing

5. Regression and Multivariate Data Analysis
            Regression Analysis and Exploratory Data Analysis
            Testing Assumptions of Regressions
            Feature Selection and Model Building
            Q-Q Plots
            Principal Component Analysis (PCA)
            Exploratory Factor Analysis

6. Machine Learning
            Difference between Classification and Regression problems
            How is it different from the traditional statistical learning?
            What are the types of Machine learning? (Supervised/Unsupervised)
            Supervised learning algorithms
                        Decision trees (CART, CHAID, C5.0, and QUEST)
                        KNN (K nearest neighbor)
                        Logistic regression
                        ANNs (Artificial neural networks)
                        Time Series
            Un-supervised learning algorithms
                        Hierarchical Clustering
                        Market basket analysis (Association rule mining)

Tools to be used:
            R Or Python

Projects :
            We will be covering 4 projects as part of this complete course.

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