PART 1 : INTRODUCTION TO DATA SCIENCE:

1. What is Data Science? – Introduction.
2. Importance of Data Science.
3. Demand for Data Science Professional.
4. Brief Introduction to Big data and Data Analytics.
5. Lifecycle of data science.
6. Tools and Technologies used in data Science.
7. Comprehensive R Archive Network
8. Demo of Installing R On windows from CRAN Website
9. Installing R Studios on Windows OS Setting Up R Workspace.
10. Getting Help for R-How to use help system
11. Installing Packages – Loading And Unloading Packages

PART 2 –  STATISTICS

1. Fundamentals of Math and Probability Basic
2. understanding of linear algebra, Matrics, vectors
3. Addition and Multimplication of matrics Fundamentals of Probability
4. Probability distributed function and cumulative distributed function.
5. Class Hand-on
6. Problem solving using R for vector manupulation
7. Problem solving for probability assignments

 

2 Descriptive Statistics

1. Describe or summarise a set of data Measure of central tendency and measure of dispersion.
2. The mean,median,mode, curtosis and skewness
3. Computing Standard deviation and Variance.
4. Types of distribution.

Class Handson:

1. 5 Point summary BoxPlot
2. Histogram and Bar Chart
3. Exploratory analytics R Methods

 

3. Inferential Statistics
4. What is inferential statistics
5. Different types of Sampling techniques
6. Central Limit Theorem
7. Point estimate and Interval estimate
8. Creating confidence interval for population parameter
9. Characteristics of Z-distribution and T-Distribution
10. Basics of Hypothesis Testing
11. Type of test and rejection region
12. Type of errors in Hypothesis resting, Type-l error and Type-ll errors
13. P-Value and Z-Score Method
14. T-Test, Analysis of variance(ANOVA) and Analysis of Co variance(ANCOVA) Regression analysis in ANOVA

 

Class Hands-on:

1. Problem solving for C.L.T
2. Problem solving Hypothesis Testing
3. Problem solving for T-test, Z-score test
4. Case study and model run for ANOVA, ANCOVA

 

4. Hypothesis Testing
5. Hypothesis Testing
6. Basics of Hypothesis Testing
7. Type of test and Rejection Region
8. Type o errors-Type 1 Errors,Type 2 Errors
9. P value method,Z score Method

PART 3 – UNDERSTANDING AND IMPLEMENTING MACHINE LEARNING

1. Introduction To Machine Learning
2. What is Machine Learning?
3. What is the Challenge?
4. Introduction to Supervised Learning,Unsupervised Learning
5. What is Reinforcement Learning?

 

2. Linear Regression
3. Introduction to Linear Regression
4. Linear Regression with Multiple Variables
5. Disadvantage of Linear Models
6. Interpretation of Model Outputs
7. Understanding Covariance and Colinearity
8. Understanding Heteroscedasticity

 

Case Study

• Application of Linear
• Regression for Housing Price Prediction

 

3. Logistic Regression
4. Introduction to Logistic Regression.– Why Logistic Regression .
5. Introduce the notion of classification Cost function for logistic regression
6. Application of logistic regression to multi-class classification.
7. Confusion Matrix, Odd’s Ratio And ROC Curve
8. Advantages And Disadvantages of Logistic Regression.

 

Case Study:

• To classify an email as spam or not spam using logistic Regression.

 

4. Decision Trees And Supervised Learning
5. Decision Tree – data set
6. How to build decision tree?
7. Understanding Kart Model
8. Classification Rules- Overfitting Problem
9. Stopping Criteria And Pruning
10. How to Find final size of Trees?
11. Model A decision Tree.
12. Naive Bayes
13. Random Forests and Support Vector Machines
14. Interpretation of Model Outputs

Case Study:

• Business Case Study for Kart Model
• Business Case Study for Random Forest
• Business Case Study for SVM

 

5. Unsupervised Learning
6. Hierarchical Clustering
7. k-Means algorithm for clustering – groupings
8. of unlabeled data points.
9. Principal Component Analysis(PCA)- Data
10. Independent components analysis(ICA)
11. Anomaly Detection
12. Recommender System-collaborative filtering algorithm

Case Study– Recommendation Engine for

e-commerce/retail chain

 

6. Introduction to Deep Learning
7. INeural Network
8. Understaing Neural Network Model
9. Understanding Tuning of Neural Network

 

Case Study: Case study using Neural Network

 

7. Natural language Processing
8. Introduction to natural Language
9. Processing(NLP).
10. Word Frequency Algorithms for NLP Sentiment Analysis

 

Case Study : Twitter data analysis using NLP

 

8. Apache Spark Analytics
9. What is Spark
10. Introduction to Spark RDD
11. Introduction to Spark SQL and Dataframes
12. Using R-Spark for machine learning
13. Hands-on:
14. installation and configuration of Spark
15. Hands on Spark RDD programming
16. Hands on of Spark SQL and Dataframe programming
17. Using R-Spark for machine learning programming

PART  4 – R PROGRAMMING

1. R Basics, background
2. Comprehensive R Archive Network
3. Demo of Installing R On windows from CRAN Website
4. Installing R Studios on Windows OS
5. Setting Up R Workspace.
6. Getting Help for R-How to use help system
7. Installing Packages – Loading And Unloading Packages

 

2. Getting familiar with basics
3. Operators in R – Arithmetic,Relational,Logical and Assignment Operators
4. Variables,Types Of Variables,Using variables Conditional statements,ifelse(),switch
5. Loops: For Loops,While Loops,Using Break statement,Switch

 

3. The R Programming Language- Data Types
4. creating data objects from the keyword.
5. How to make different type of data objects.
6. Types of data structures in R
7. Arrays And Lists- Create Access the elements
8. Vectors – Create Vectors,Vectorized Operations,Power of Vectorized Operations Matrices- Building the first matrices,Matrix Operations,Subsetting,visualising subset
9. Data Frames- create and filter data frames,Building And Merging data frames.

 

4. Functions And Importing data into R
5. Function Overview – Naming Guidelines
6. Arguments Matching,Function with Multiple Arguments
7. Additional Arguments using Ellipsis,Lazy Evaluation Multiple Return Values Function as Objects,Anonymous Functions
8. Importing and exporting Data into R- importing from files like excel,csv and minitab.
9. Import from URL and excel Files
10. Import from database.

 

5. Data Descriptive
6. Statistics,Tabulation,Distribution
7. Summary Statistics for Matrix Objects. apply() Command. Converting an Object into a Table
8. Histograms, Stem and Leaf Plot, Density
9. Normal Distribution

 

6. Graphics in R – Types of graphics
7. Bar Chart,Pie Chart,Histograms- Create and edit.
8. Box Plots- Basics of Boxplots- Create and Edit Visualisation in R using ggplot2.
9. More About Graphs: Adding Legends to Graphs,Adding Text to Graphs, Orienting the Axis Label.

PART 5 – PYTHON FOR DATA SCIENCE

1. Python Programming Basics
2. Installing Jupyter Notebooks
3. Python Overview
4. Python 2.7 vs Python 3
5. Python Identifiers
6. Various Operators and Operators Precedence
7. Getting input from User,Comments,Multi line Comments.

 

2. Making Decisions And Loop Control
3. Simple if Statement,if-else Statement if-elif Statement.
4. Introduction To while Loops.
5. Introduction To for Loops,Using continue and break.

 

3. Python Data Types: List,Tuples,Dictionaries
4. Python Lists,Tuples,Dictionaries
5. Accessing Values
6. Basic Operations
7. Indexing, Slicing, and Matrixes
8. Built-in Functions & Methods
9. Exercises on List,Tuples And Dictionary

 

4. Functions And Modules
5. Introduction To Functions
6. Why Defining Functions
7. Calling Functions
8. Functions With Multiple Arguments.
9. Anonymous Functions – Lambda Using Built-In Modules,User-Defined Modules,Module Namespaces,Iterators And Generators.

 

5. File I/O And Exceptional Handling
6. Opening and Closing Files
7. open Function,file Object Attributes
8. close() Method ,Read,write,seek.Exception Handling,the try-finally Clause
9. Raising an Exceptions,User-Defined Exceptions
10. Regular Expression- Search and Replace
11. Regular Expression Modifiers
12. Regular Expression Patterns,re module.

 

6. Numpy
7. Introduction to Numpy. Array Creation,Printing Arrays
8. Basic Operations- Indexing, Slicing and Iterating Shape Manipulation – Changing shape,stacking and spliting of array Vector stacking

 

7. Pandas And Matplotlib
8. Introduction to Pandas
9. Importing data into Python
10. Pandas Data Frames,Indexing Data Frames ,Basic Operations With Data frame,Renaming Columns,Subletting and filtering a data frame.
11. Matplotlib – Introduction,plot(),Controlling
12. Line Properties,Working with Multiple Figures,Histograms

PART 6 – DEEP LEARNING

PART 7 – NLP

PART 8 – Computer Vision

PART 9 – HADOOP

PART 10 – Neural Networks

9. Connecting to data source
10. Creating dashboard pages
11. How to create calculated columns
12. Different charts
13. Hands-on:
14. Hands on on connecting data source and data clensing
15. Hands on various charts
16. Hands on deployment of Predictive model in visualisation

FINAL PROJECT(S)

MENTORSHIP

Course Highlights

1.  A Dedicated Portal For Practicing.
2. Real Time Project Data Models to Work
3. 1-1 Mentorship
4. Internship Offers for Freshers.
5. Weekly Assignments.
6. Weekly Doubt Sessions
7. Advanced Curriculum
8. Certificates On successful Completion of Project .
9. Resume Preparation Tips
10. Interview Guidance And Support.
11. Dedicated HR Team for Job Support And Placement Assistance.
12. Experienced Trainers.