Best Data Science Training in Bangalore

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  • 5/ 5
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Get the best Data Science training in Bangalore from expert IT professionals. Apponix is glad to state that it is one of the best Data Science course provider in Bangalore, we give best learning background and results to our esteemed students, All our faculty are extremely experienced IT experts and love to impart their viable learning to our students.

Data Science Training course is intended to suit all dimensions of understudies to give top to bottom learning about Data Science. All classes will be directed by IT Industry specialists who will provide and deliver you all through the course to use Data Science to influence you to prepare for your fantasy work.

Preparing in Apponix advances primarily centers around the present extent of Data Science and constant necessities which will acquaint another learning background with the beginners. The example of the course structure fastidiously intended for learners and experts who needed to begin or enable their aptitudes on Data Science.

Each area of module and the code test leads on each Data Science idea will help your coding ability. Continuous activities like scratching a site or mechanising a day by day redundant undertaking with your insight causes you to get premium and find out additional. On fruitful finishing of the course, you will venture out with 100% fulfilment and information to accomplish your objectives.

Data Science instructional class conceals a to date and most applicable subjects which are required by the vast majority of the organisations, our Data Science educator knows great on points and schedule to be secured.

How we standout among other training institutes ?

@ Apponix @ Other institutes
Course fees Very competitive and affordable. Most of the institutes provide at less fees but compromise with the quality of the training
Placement assistance We have a dedicated HR team to help students in placement and tied with leading job portals. Most of the institutes may make false promises
Dedicated HR team Yes None
Working Professionals as trainers Yes Very Few
Trainers Experience Min 7+ Years experience Most of the institutes hire full time trainers with very less real time experience
Student Web Portal We have a dedicated students portal where you will find course materials and technical questions hr questions prepared by it professionals None
Class Room Infrastructure All classrooms are Air conditined to make sure our students feel comfortable Very few institutes
Reference Pay We pay Rs 1000 for every student you refer. None
Pay After Job Yes, for most of the courses students can pay part of the fees after they get a job None, You need to pay full fess before joining
Instalments Yes its very flexible, you can pay the fees in installments, we understand the finacial situation of the students Very few institutes
Lab Infrastructure For most of the courses each student is given with laptop or desktop throughout the course None
Who are our trainers?
IT consultants,IT project managers, Solutions Architects, Technical Leads Most of the institutes hire full time trainers with very little experience
Student's Ratings 5 ***** ratings from more than 4000 students Mixed
Trust & Credibility  Very High Moderate.
Fees Negotiable? Definitely yes we understand the financial situation of each student Very few
Refer and Win We run refer and win a holiday every 6 months, All referers will have chance to win holiday to goa, please refer this link None

Why should you learn data Science?

The following are the few motivations to pick Data Science:

  • Data Science has basic sentence structure which is straightforward
  • A large number of occupations open doors for Data Science Engineers.
  • Data Science is the most favoured for Artificial Intelligence, Robotics, Web Development and DevOps.
  • Data Science is a standout amongst the most chief, adaptable, and amazing open-source language that is anything but difficult to learn.
  • Data Science is anything but difficult to utilise, and has incredible libraries for information control and investigation.
  • For over 10 years, Data Science has been utilised in logical registering and exceedingly quantitative areas, for example, money, oil and gas, material science, and flag handling starting today.

Data Science Training Classes in Bangalore course objectives:

  • Make an interpretation of business inquiries into Machine Learning issues to comprehend what your information is letting you know
  • Investigate and examine information from the Web, Word Documents, Email, Twitter channels, NoSQL stores, Relational Databases and that's only the tip of the iceberg, for examples and patterns significant to your business
  • Construct Decision Tree, Logistic Regression and Naive Bayes classifiers to make expectations about your clients' future practices just as different business basic occasions
  • Use K-Means and Hierarchical Clustering calculations to all the more successfully fragment your client showcase or to find exceptions in your information
  • Find concealed client practices from Association Rules and Build Recommendation Engines dependent on personal conduct standards
  • Use organically propelled Neural Networks to gain from observational information as people do
  • Examine connections and streams between individuals, PCs and other associated substances utilising Social Network Analysis

What are the prerequisites for Data Science Training?

A computer basics is good to start with & most important is you need to have good interest in learning data science.

Why Should you choose Apponix as a Top Data Science Training centre?

  • Apponix has very experienced and qualified Data Science trainers.
  • Till today we have 100% fulfilment rate.
  • More than 1000 students have completed data science from Apponix
  • All around prepared lab office, Decent infrastructure.
  • All classrooms are Air-Conditioned.
  • All students are given an individual workstation all through the course with fast WiFi.
  • 100% Placement assistance

Because of intense interest for Data Science engineers and Data Science is incredible for web advancement. As there are absences of occupations accessible in Bangalore city just there is a Hugh interest for Data Science designers too.

Apponix has tied up with numerous enrolment organisations and organisations additionally we are legitimate accomplice of www.talentsarena.com, we have a devoted Human Resource group who will continually working with enlistment offices for any new employments openings and the dynamic group will keep you refreshed with all new employment opportunities.

In other hand our mentors are extremely useful and they will assist you with most expected inquiries questions and replies in Data Science.

Data Science Training in Bangalore. Data Science, that is utilised by a large number of individuals to get things done from testing microchips at Intel, to controlling Instagram, to building computer games. It is the fourth most well-known as per an IEEE overview, behind old works of art Java, C, and C++? So in festivity of our two new Data Science. Learning Data Science is critical in this period. You need to learn Data Science in a Real Time Manner with Practical Examples; you are in a correct Track!

Data Scientist Job Responsibilities

  1. Determine modeling based on business needs 
  2. Create segmentation logic based on business requirements and applying that where necessary 
  3. Create integrated views of data from multiple data sources within a cloud-based visualization tool using data transformation techniques 
  4. Review data results to ensure accuracy.
  5. Work on the end to end flow of a broad range of analytics use cases, to deliver real business value.
  6.  Data mining using different state of art methods
  7. Extending organization data with third party sources of information
  8. Configure data visualizations for stakeholders 
  9. Communicate results and insights to the project team and business partners 
  10. Engaging with business stakeholders and understanding the problem, assisting data engineers in acquiring and processing the data, building and testing the appropriate model, to overseeing the deployment into production.
  11. Contribute to maintaining comprehensive documentation on integrated data tables 
  12. Apply data science techniques to real world use cases, and seeing these designs through to completion and generating value from their models.
  13. Develop client relationships and partner with internal stakeholders.
  14. Understand high performance algorithms and Python statistical software and brief team. 
  15. Working with lambda architectures and batch and real-time data streams.
  16. Architect highly scalable distributed systems, using different open source tools.
  17. Develop business cases for R&D initiatives, provides expert advice to product managers, developers, architects and business partners on data science use cases and options.
  18. Translates business requirements throughout the development process, delivers solutions in accordance with business strategies, standards, and processes.
  19. Manipulate and analyze complex, high-volume, high-dimensionality data from varying sources using a variety of tools and data analysis techniques
  20. Support the use of data science and machine learning within the various PSE engineering DevOps teams.

Data Science With Python

Introduction to Data Science
  • What is Data Science? – Introduction
  • Roles and Responsibilities of a Data Scientist
  • Life cycle of Data Science project
  • Tools and Technologies used
PART – A: STATISTICS FOR DATA SCIENTIST

Module 1: Introduction to Statistics

  • Types of Data
  • Data Measurement Scales
  • Fundamentals of Probability
  • Bayes Theorem

Module 2: Descriptive Statistics

  • What id Descriptive Statistics
  • Measure of Central Tendency (Mean, Mode and Median)
  • Measure of Dispersion/Spread (Range, Variance and Standard Deviation)

Module 3: Inferential Statistics

  • What is Inferential Statistics
  • Types of Sampling Techniques
  • Probability Sampling
  • Non Probability Sampling
  • Central Limit Theorem

Module 4: Probability Distributions

  • Types of Probability Distributions
  • Binomial Distribution
  • Poisson Distribution
  • Hyper Geometric Distribution
  • Normal Distribution
  • Z Distribution
  • T distribution

Module 5: Hypothesis Testing

  • What is Hypothesis Testing
  • Types of Hypothesis Testing
  • Parametric Hypothesis Testing
  • 2 Independent Samples T test
  • Paired T test
  • ANOVA (Analysis of Variance)
  • Chi – Square test for Independence
  • Chi – Square test for Goodness of Fit
  • Non Parametric Hypothesis Testing
  • Mann – Witney U Test
  • Wilcoxon Signed Rank Test
  • Kruskal – Wallis Test
  • Types of Errors (Type | and Type || Errors)
  • Co-variance, Correlation and Regression
PART – B: PYTHON FOR DATA SCIENTIST

Module 1: Python Programming

  • Introduction to Python with Anaconda Distribution
  • Introduction to Jupiter Notebook
  • Crash Course on Python Programming
  • Types of Operators
  • Python Data Types
  • List
  • Tuple
  • Dictionary
  • Sets
  • Data Types Operations & Methods
  • Flow Controls
  • If…..Else Statements
  • If ….Elif ….Else Statements
  • For Loops
  • While Loops
  • Functions
  • List Compressors
  • Lambda, Map and Filter

Module 2: Introduction to Essential Python Libraries for Data Science

  • Numpy, Pandas, Matplotlib, Seaborn and Scikit Learn Libraries

Module 3: Numpy

  • Introduction to Numpy Library
  • Numpy Arrays
  • Numpy Indexing and Selection
  • Numpy Operations

Module 4: Pandas

  • Introduction to Pandas Library
  • Pandas Series and Data Frames
  • Pandas Indexing and Selection
  • Pandas Operations

Module 5: Data Mugging / Wrangling with Pandas

  • Handling Missing Data
  • Group by Method
  • Merging, Joining and Concatenating Data Frames.
  • Pivot Table
  • Reshaping the Data Frame
  • Cross Tab / Contingency Table

Module 6: Data Visualization

  • Various types of Plots and their Applications
  • Introduction to Matplotlib Library
  • Creation of plots
  • Plot Styles
  • Introduction to Seaborn Library
  • Distribution Plots
  • Categorical Plots
  • Matrix Plots
  • Regression Plots
  • Pandas Built-in Visualizations
PART – C: Machine Learning

Module 1: Data Pre-processing Techniques

  • Sanity Checks
  • Missing Value Detection and Treatment
  • Outlier Detection and Treatment
  • Variable Transformation Techniques
  • Exploratory Data Analysis
  • Uni-Variate Analysis
  • Bi-Variate Analysis

Module 2: Machine Learning Basics

  • Types of Machine Learning Techniques
  • Steps Followed in ML Model Building
  • Train set, Validation set and Test set
  • Bias and Variance Trade-off Study

Module 3: Supervised Machine Learning Models

  • Linear Regression
  • Simple Linear Regression
  • Multivariate Linear Regression
  • Logistic Regression
  • Decision Tree
  • Support Vector Machine
  • k Nearest Neighbours (kNN)
  • Naïve Bayes

Module 4: Unsupervised Machine Learning Models (Clustering)

  • Types of Clustering Algorithms
  • k Means Clustering Algorithm
  • Hierarchical Clustering Algorithm
  • Evaluation of Clustering Techniques

Module 5: Dimensionality Reduction Techniques

  • PCA (Principal Component Analysis)
  • LDA (Linear Discriminate Analysis)

Module 5: Other Topics

  • Bagging Methods
  • Random Forest
  • Boosting Methods
  • Ada Boost (Adaptive Boosting)
  • XG Boost
  • Cross Validation Techniques (Pre-processing step)
  • K-Fold Cross Validation
  • Stratified K Fold Cross Validation

Module 6: Interview Questions Discussions

Loops and Decision Making
  • if statements
  • ..else statements
  • nested if statements
  • while loop
  • for loop
  • nested loops
  • Loop Control Statements
  • 1) break statement
  • 2) continue statement
  • 3) pass statement