Tuesday, March 26, 2019

best Data Science with Python training in marathahalli, bangalore

Course Duration: 45 hours
Attend 3 Free Classes to Check Training Quality
100% Real Time Practical Training with Placement Assistance
(Trained by 15+ years experienced working professionals )

Data Science with Python Training Course Content

Statistical Analysis & Data Manipulation

Statistical Analysis & Data Manipulation - Session 1

  • The Information age
  • Data in every domain
  • The Big Data Problem
  • Enablers
  • What is Data Science
  • Qualities and Responsibilities
  • Application of Data Science
  • Types of Analytics
  • Analytics Life Cycle

Statistical Analysis & Data Manipulation - Session 2

  • Population vs Sample
  • Type of Data Variable
  • Discrete
  • Continuous
  • Binary
  • Nominal
  • Ordinal
  • Random Variable
  • Probability Distribution: Discrete and Continuous

Statistical Analysis & Data Manipulation - Session 3

  • The Measures of Central Tendency
  • The Measure of Spread
  • Summarizing Data
  • Frequency Distribution, Histogram, Cumulative plot, BoxPlot
  • Data Cleansing
  • Missing Value Imputation
  • Outlier Treatment
  • Data Preprocessing
  • Data Standardization
  • Data Normalization

Statistical Analysis & Data Manipulation - Session 4

  • Characteristics of Normal Distribution
  • Application with example
  • Standard Normal Distribution
  • Z-Score Table
  • Application of Z Score
  • Identifying Normal Distribution
  • Skewness and Kurtosis
  • Q-Q Plot

Statistical Analysis & Data Manipulation - Session 5

  • Sampling
  • Sample statistics and population parameters
  • Central Limit Theorem
  • Examples
  • Point Estimate & Confidence Interval
  • Confidence Interval Quiz

Statistical Analysis & Data Manipulation - Session 6

  • Hypothesis Testing
  • Key steps in Hypothesis Testing
  • Criteria for Decision Making
  • Test Statistics
  • P Value/Significance test
  • Two- Tailed Test

Introduction to Python

Introduction to Python - Session 1

  • Introduction to Python
  • Why Python
  • Tips and Reminders
  • Spyder ,Jupyter Software
  • Python Syntax
  • Data Structures
  • Flow Control
  • Functions

Introduction to Python - Session 2

  • Data Analysis with Python

Introduction to Python - Session 3

  • Data Processing with Python

Introduction to Machine Learning- Supervised

Introduction to Machine Learning- Supervised - Session 1

  • Introduction to Machine Learning
  • Parametric Vs Non Parametric
  • Supervised, Unsupervised and Semi-supervised
  • Bias Vs Variance
  • Overfitting and Underfitting
  • Training Validation and Prediction
  • Introduction to regression analysis
  • Types of regression models

Introduction to Machine Learning- Supervised - Session 2

  • Introduction to Linear Regression
  • Types of Regression Model
  • Regression Function
  • The Error Term-Residual
  • OLS Regression Properties
  • Interpretation of the Slope and the Intercept
  • Assumption in Regression Analysis
  • General Multiple Linear Regression Model
  • Strengths and Weakness
  • Quality of Fit- R2

Introduction to Machine Learning- Supervised - Session 3

  • Introduction to Logistic Regression
  • Logistic Function
  • Preparing Data for Logistic Regression
  • Strengths and Weakness of Logistic Regression
  • Summary

Introduction to Machine Learning- Supervised - Session 4

  • Implementation of Linear and Logistic Regression in Python

Introduction to Machine Learning- Supervised - Session 5

  • Introduction to K Nearest Neighbour
  • Curse of Dimensionality
  • Working of KNN
  • Preparing Data for KNN
  • Strengths and Weakness of KNN
  • Summary

Introduction to Machine Learning- Supervised - Session 6

  • Implementation of KNN in Python

Introduction to Machine Learning- Supervised - Session 7

  • Introduction to CART
  • Example of Decision Tree
  • Basic Algorithm
  • Stopping Criteria
  • Pruning
  • Strengths and Weakness
  • Summary

Introduction to Machine Learning- Supervised - Session 8

  • Implementation of CART in Python

Introduction to Machine Learning- Supervised - Session 9

  • Introduction to Random Forest
  • Bootstrapping
  • Bagging
  • Random Forest Classifier
  • Estimate Performance
  • Variable Importance
  • Strength and Weakness
  • Summary

Introduction to Machine Learning- Supervised - Session 10

  • Implementation of Random Forest in Python

Introduction to Machine Learning- Supervised - Session 11

  • Introduction to SVM
  • Working of SVM
  • Margin Classifier
  • Classification with Hyperplanes-Linearly and non-Linearly separable data
  • Preparing Data for SVM
  • Strengths and Weakness of SVM
  • Summary

Introduction to Machine Learning- Supervised - Session 12

  • Implementation of SVM in Python

Introduction to Machine Learning- Supervised - Session 13

  • Introduction to Naïve Bayes
  • Working of Naïve Bayes
  • Preparing Data for Naïve Bayes
  • Strengths and Weakness of Naïve Bayes
  • Summary

Introduction to Machine Learning- Supervised - Session 14

  • Implementation of Naïve Bayes in Python

Introduction to Machine Learning- Supervised - Session 15

  • Introduction to Gradient Descent
  • Working of Gradient Descent
  • Batch Gradient Descent
  • Stochastic Gradient Descent
  • Preparing Data for Gradient Descent
  • Strengths and Weakness of Gradient Descent
  • Summary

Introduction to Machine Learning- Supervised - Session 16

  • Implementation of Gradient Descent in Python

Introduction to Machine Learning- UnSupervised

Introduction to Machine Learning- UnSupervised - Session 1

  • Introduction to Unsupervised Learning
  • Introduction to K-Means Clustering
  • Working of K-Means
  • Choosing appropriate value of K
  • Strengths and Weakness of K-Means
  • Summary

Introduction to Machine Learning- UnSupervised - Session 2

  • Introduction to Hierarchical Clustering
  • Preparing Data for Hierarchical Clustering
  • Strengths and Weakness of Hierarchical Clustering
  • Implementation of K-Means and Hierarchical clustering in Python

Introduction to Machine Learning- UnSupervised - Session 3

  • Introduction to Principal Component Analysis
  • Working of PCA
  • Preparing Data for PCA
  • Strengths and Weakness of PCA
  • Summary

Introduction to Machine Learning- UnSupervised - Session 4

  • Implementation of PCA in Python

Introduction to Machine Learning- UnSupervised - Session 5

  • Introduction to Apriori
  • Working of Apriori
  • Preparing Data for Apriori
  • Strengths and Weakness of Apriori
  • Summary

Introduction to Machine Learning- UnSupervised - Session 6

  • Implementation of Apriori in Python

Deep Learning

Deep Learning - Session 1

  • Introduction to Neural Networks
  • Working of Neural Networks
  • Preparing Data for Neural Networks
  • Strengths and Weakness of Neural Networks
  • Summary

Deep Learning - Session 2

  • Implementation of Neural Network in Python
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Course Fee : Talk to our Customer SupportDuration : 45 Hours
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SDLC training providing the limited batch size, so we can provide quality teaching. If you want to get trained individually, we are also providing.

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SDLC training providing the 100% job assistance and mock interviews.

How you people will help in the projects?

SDLC training is providing the training with live projects and real-time practice.

How you people will provide the doubt clarification?

SDLC training providing the 24/7 interact access with faculties and after course also engagement between the faculties and students.

What are the extra services?

SDLC training providing the back up classes, soft skill training, interview skills workshop and resume preparation assistance.

How you people will help to enhance the students knowledge?

SDLC training providing the topics wise ppts, case studies, assignments and doubt solving.

 

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    review rating 5  Training experience was excellent, trainers taught each and every topic in full detail even they explained whatever doubt I had. Also the management is very cooperative guided me how to prepare resume which will helpful to get the job. Overall experience was excellent

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  • review rating 5  I joined phyton course recently based on my experience trainer is professional and clear and gives pratical examples real time scenarios and also SDLC team very helpful in all the terms and am very happy with “SDLC” training.I would recommend everyone to join this institute.

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    thumb Gaurav justdial1
    2/02/2019

    review rating 5  Great place to learn python,RPA ,Data science ,Angular,Java ,Oracle SCM,Oracle DBA rac, SDLC Trainers are working professional in MNC, They have a Good communication skills They are providing Realtime project Practically , I joined PYTHON course in SDLC Telraining Marathahalli Branch Trainer took great pains to explain with Good example….

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    1/19/2019

Best Data Science with Python Training in Bangalore

Python is a multi-paradigm programming language which the foundation to enter in the coding world. It will introduce you to various programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. Understanding of Python concepts is really important to learn deeply about Data Science with Python.

In Data Science with Python you will learn data analysis, data manipulation and cleaning techniques by using python pandas data science library. With the help of this course will be able to take tabular data, clean it, manipulate it, and run data analysis.

SDLC training institute providing the Data Science with Python real-time online training classes, classroom training classes for the weekend and regular batches. Get JOB with our free Placement Assistance Program.

There are various sectors where you can g too.

  • Next generation mobile apps
  • Business functions
  • Gaming
  • Communication

How we will start the course?

  • Learn from basics
  • Practice coding
  • Set your algorithm carefully
  • Trace your codes on paper
  • Read sources on Data Science with Python regularly

At end of the course?

  • Trainees will understand the core concepts of Data Science with Python.
  • Participants will have an understanding of how to create and implement algorithms.
  • Candidates will have detailed knowledge about Data Science with Python.
  • Real-time project experience.
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Features of SDLC​

  • Limit the batch size so we can provide personal attention to everyone in the session
  • Real-time practice
  • Live projects
  • 24/7 interact access with faculties
  • Experienced and passionate trainers
  • After course engagement
  • We give topics wise ppt, case studies, assignments and doubt solving
  • 100% job assistance
  • 24/7 support
  • Classroom training, Online training and Corporate training
  • Student can attend their missed classes
  • Soft skill training, interview skills workshop, resume preparation assistance
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