DataScience using Python Training
We are the best Training institute for learning DataScience using Python Training in Chennai . We have expert trainers and excellent materials to transform your skills to fit into the job market.
Module 1 : Introduction, Data Science Overview, Recommender Overview
- Data Science Overview
- Use Cases
- Project Lifecycle
- Data Acquisition
- Evaluating Input Data
- Data Transformation
- Data Analysis and Statistical methods
- Fundamentals of Machine Learning
- Recommender Overview
- Basic Introduction to Apache Mahout
Module 2 – Use Cases, Project Lifecycle
- What is Data Science?
- What Kind of Problems can you solve?
- Data Science Project Life Cycle
- Data Science-Basic Principles
- Data Acquisition
- Data Collection
- Understanding Data- Attributes in a Data, Different types of Variables
- Build the Variable type Hierarchy
- Two Dimensional Problem
- Co-relation b/w the Variables- explain using Paint Tool
- Outliers, Outlier Treatment
- Boxplot, How to Draw a Boxplot
Module 3 – Data Acquisition
- Discussion on Boxplot- also Explain
- Example to understand variable Distributions
- What is Percentile? – Example using Rstudio tool
- How do we identify outliers?
- How do we handle outliers?
- Outlier Treatment: Using Capping/Flooring General Method
- Distribution- What is Normal Distribution?
- Why Normal Distribution is so popular?
- Uniform Distribution
- Skewed Distribution
Module 4 : Machine Learning
- Discussion about Boxplot and Outlier
- Goal: Increase Profits of a Store
- Areas of increasing the efficiency
- Data Request
- Business Problem: To maximize shop Profits
- What are Interlinked variables
- What is Strategy
- Interaction b/w the Variables
- Univariate analysis
- Multivariate analysis
- Bivariate analysis
- Relation b/w Variables
- Standardize Variables
- What is Hypothesis?
- Interpret the Correlation
- Negative Correlation
- Machine Learning
Module 5 – Data Analysis and Statistical Methods, Implementing Recommenders with Apache
- Mahout, Data Transformation
- Correlation b/w Nominal Variables
- Contingency Table
- What is Expected Value?
- What is Mean?
- How Expected Value is differ from Mean
- Experiment – Controlled Experiment, Uncontrolled Experiment
- Degree of Freedom
- Dependency b/w Nominal Variable and Continuous Variable
- Linear Regression
- Extrapolation and Interpolation
- Univariate Analysis for Linear Regression
- Building Model for Linear Regression
- Pattern of Data means?
- Data Processing Operation
- What is sampling?
- Sampling Distribution
- Stratified Sampling Technique
- Disproportionate Sampling Technique
- Balanced Allocation-part of Disproportionate Sampling
- Systematic Sampling
- Cluster Sampling
- 2 angels of Data Science-Statistical Learning, Machine Learning
Module 6 – Experimentation and Evaluation, Production Deployment and Beyond
- Multi variable analysis
- linear regration
- Simple linear regration
- Hypothesis testing
- Speculation vs. claim(Query)
- Step to test your hypothesis
- performance measure
- Generate null hypothesis
- alternative hypothesis
- Testing the hypothesis
- Threshold value
- Hypothesis testing explanation by example
- Null Hypothesis
- Alternative Hypothesis
- Histogram of mean value
- Revisit CHI-SQUARE independence test
- Correlation between Nominal Variable
Module 7 – Various Algorithms on Business, Simple approaches to Prediction, Model Building,
Deploy the model
- Machine Learning
- Importance of Algorithms
- Supervised and Unsupervised Learning
- Various Algorithms on Business
- Simple approaches to Prediction
- Predict Algorithms
- Population data
- Disproportionate Sampling
- Steps in Model Building
- Sample the data
- What is K?
- Training Data
- Test Data
- Validation data
- Model Building
- Find the accuracy
- Deploy the model
- Linear regression
Module 8 – Prediction & Analysis Segmentation
- Cluster and Clustering with Example
- Data Points, Grouping Data Points
- Manual Profiling
- Horizontal and Vertical Slicing
- Clustering Algorithm
- Criteria for take into Consideration before doing Clustering
- Graphical Example
- Clustering and Classification: Exclusive Clustering, Overlapping Clustering, Hierarchy
- Simple Approaches to Prediction
- Different types of Distances: 1.Manhattan, 2.Euclidean, 3.Consine Similarity
- Clustering Algorithm in Mahout
- Probabilistic Clustering
- Pattern Learning
- Nearest Neighbor Prediction
- Nearest Neighbor Analysis
Module 9 – Integrating R with Hadoop
- R introduction
- How R is typically used
- Features of R
- Introduction to Big data
- Ways to connect with R and Hadoop
- Case Study
- Steps for Installing RIMPALA
- How to create IMPALA packages
Projects 1-Cold Start Problem in Data Science
- Recommendation Algorithms
- Two Ways of Recommendation
- Recommendation Types-Collaborative Filtering Based Recommendation, Content-Based
- Cold Start Problem in Data Science
Project 2-Movie Recommendation, Conclusion
Prediction – Rating Prediction, Item Prediction
- Two Basic Approaches: Memory Based and Model Based
- What is User Based Methods in K-Nearest Neighbor?
- What is Item Based Method?
- Matrix Factorization
- Singular Value Decomposition
- Discuss on Data Science Project
- Collaboration Filtering
- What are the Business Variables?
Data Science Assignment
- Business problem
- What are the various data sets that I should use to solve this problem?
- How many variables are related to solve my problem?
- How do I build my strategy to solve this problem with the available data?
- Descriptive Statistics
Module 1 – Introduction of Python
- Why python
- What you need to get started
- General purpose
- Is python a scripting language?
- Why use python in the RW?
- What can you do with python
- How to install python.
Module 2 – Installation Python/Basic Programming
- Installing python
- Windows Installation
- Linux Installation
- Environment variables
- What is IDE?
- How to download additional Diary.
- Running python program
- Data types
- Object types
- Python core data types
- Strings (Methods)
Module 3 – Regular Expressions, Looping, its Packages and Object Oriented Programming
- Data Types
- How to create notebook in python
- Methods of tuple
- Dictionary Methods
- Advance string methods
- String formatting
- Obtaining keyboard input
- Control flow
- The if statement
- Boolean logic
- Break and continue
- The for loop
- The while loop
- Control flow
- What is a function
- Arbitrary of arguments
Module 4 – File Operations and Deep Dive – Functions, Class, DBAPI, Errors and Exception
- File handling
- File system
- Opening files
- Opening other file types
- Exception handling
- What are exceptions?
- Object Oriented programming (OOP)
- OOP basics
- Defining a class
- Special methods
- Python DB API
- SQLite in python
- Panda quick overview
Module 5 – Introduction to Hadoop and Hadoop with Python
- The state of Data
- Component of Hadoop
- Why Hadoop is scalable
- Hadoop eco system
- Hadoop Incubator
- Stack Implementation
- Architecture of HDFS and Map reduce
- HDFS feature
- Map reduce Architecture
- Map reduce Internals
- Installation overview of Hadoop
Module 6 – Deep Drive for components and Function
- Python Basics revisited
- List components
- Lamda function
- Matplot lib
Module 7 – Machine Learning Using Python
- Introduction of panda
- Key feature of panda
- Importing the panda
- Data structure in Panda
- Importing data
- Read table
- Skip rows
- Machine learning
- Machine learning algorithms
- Unsupervised learning
- Real world example of machine learning
- Statistical learning problem
Module 8 – Manage File System/Sandbox
- How to take remote login of your sandbox
- How to manage HDFS file system
- Maper reducer
Module 9 – Machine Learning with Python
- How to use Scikit-learn
- Shape method
- Get Datahome
- How to do the machine learning
- Spam detection
Module 10 – Server logs/Firewall logs
- Server logs
- Potential uses of server log data
- Pig script
- Firewall logs
- Work flow editor
Module 11 – Detail Project
- Web Logging in Python and End to end connects with all modules
Why choose SimplyAnalytics for DataScience using Python Training in Chennai?
- 1. 100% Practical and placement oriented training.
- 2. We are registered training organization.
- 3. Expert trainers from IT industries.
- 4. Placements Assistance.
- 5. Flexible timings.
- 6. Weekdays and weekend batches.
- 7. Affordable fees.
- 8. Air conditioned classroom.
- 9. Wi-Fi enabled training institute.
- 10. Best Lab specialities.
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- Lectures 1
- Quizzes 1
- Duration 50 hours
- Skill level All level
- Language English
- Students 0
- Assessments Self