Course Detail

Course Description

Data Science and Data Analytics Introduction (Week-1)

• What is Data Science
• Differentiate between Database Datawarehouse Hadoop Bigdata and Data Science
• Why Data Science is in demand on the top of Hadoop Ecosystem
• Components in data Science
• Real time examples and applications of Data Science
• What is Statistics
• Introduction to R Language
• Introduction to R Language and Statistics
• Statistics in Excel Sheet
• Introduction to Python Language
• IQuestions and Answers

Introduction to R Language (Week-2)

• Harnessing the power of R
• Assigning Variables
• Printing an output
• Numbers are of type numeric
• Characters and Dates
• Logicals

Arrays, Vectors and Matrices in R Language (Week-3)

• Creating an Array
• Indexing an Array
• Operations between 2 Arrays
• Operations between an Array and a Vector
• Outer Products
• Data Structures are the building blocks of R
• Creating a Vector, The Mode of a Vector
• Vectors are Atomic
• Doing something with each element of a Vector
• Aggregating Vectors
• Operations between vectors of the same length
• Operations between vectors of different length
• Generating Sequences
• Using conditions with Vectors
• Find the lengths of multiple strings using Vectors
• Generate a complex sequence (using recycling)
• Vector Indexing (using numbers)
• Vector Indexing (using conditions)
• Vector Indexing (using names)
• A Matrix is a 2-Dimensional Array
• Creating a Matrix
• Matrix Multiplication
• Merging Matrices
• Solving a set of linear equations

Factors, Lists, Data Frames,Regression Quantifies Relationships Between Variables in R Language (Week-4)

• What is a factor?
• Find the distinct values in a dataset (using factors)
• Replace the levels of a factor
• Aggregate factors with table()
• Aggregate factors with tapply()
• Introducing Lists
• Introducing Data Frames
• Reading Data from files
• Indexing a Data Frame
• Aggregating and Sorting a Data Frame
• Merging Data Frames
• Introducing Regression
• What is Linear Regression?
• A Regression Case Study : The Capital Asset Pricing Model (CAPM)

Linear Regression and Data Visualization using R and Excel (Week-5)

• Linear Regression in Excel : Preparing the data
• Linear Regression in Excel : Using LINEST()
• Linear Regression in R : Preparing the data
• Linear Regression in R : lm() and summary()
• Multiple Linear Regression
• Adding Categorical Variables to a Linear model
• Robust Regression in R : rlm()
• Parsing Regression Diagnostic Plots
• Data Visualization
• The plot() function in R
• Control color palettes with RColorbrewer
• Drawing barplots
• Drawing a Heatmap
• Drawing a Scatterplot Matrix
• Plot a line chart with ggplot2

Getting Started With Python and Statistics, Probability Refresher in Python (Week-6)

• Introduction to Python Language
• Getting What You Need in Python Library
• Installation
• Python language Basics
• Running Python Scripts
• Types of Data
• Mean, Median, Mode
• Using mean, median, and mode in Python
• Variation and Standard Deviation
• Probability Density Function; Probability Mass Function
• Common Data Distributions
• Percentiles and Moments
• matplotlib plotting library
• Covariance and Correlation
• Conditional Probability
• Conditional Probability usecases
• Bayes’ Theorem

Predictive Models and Machine Learning with Python (Week-7)

• Linear Regression
• Polynomial Regression
• Multivariate Regression, and Predicting Analysis
• Multi-Level Models
• Supervised vs. Unsupervised Learning, and Train/Test
• Using Train/Test to Prevent Overfitting a Polynomial Regression
• Bayesian Methods: Concepts
• Implementing a Spam Classifier with Naive Bayes
• K-Means Clustering
• Clustering Example
• Measuring Entropy
• Install GraphViz
• Decision Trees: Concepts
• Decision Trees: Predicting Hiring Decisions
• Ensemble Learning
• Support Vector Machines (SVM) Overview
• Using SVM to cluster people using scikit-learn

Project and Profile Discussion with Mock Interview Session (Week-8)

• How to work in Real time Project
• Real time Project Scenarios
• Frequent Challanges in Projects and solutions
• Mock Interview session
• Profile discussion
• Mock Test
• Questions and Answers

Additional Benifits

• Trainer is Working It Professionals
• POCs and Material will be provided by Institute
• Once Registered can come and join multiple batches
• We also provide Combination of Hadoop and Data Science