Data Science Training in Bangalore

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Data Scientist Training at Apponix offers the best and comprehensive methods to learn the analytics in Bangalore. This training program has been prepared with extensive inputs from experts in the industry with many years of industry experience. As a student, you will learn various components and tools in Data analytics, Machine Learning algorithms and more to develop valuable business insights.

There is a boom in demand for data analytics skilled professionals who have expertise in working with data.
There is an exponential explosion in data analytics in modern times.

What will you learn in this Data Science training?

  • Roles & responsibilities of a Data Scientist.
  • Testing, assessing and managing data of a organization.
  • Prediction/Forecast and analysis breakdownusingvarioustools .
  • Sampling techniques.
  • Working with recommender software and systems
  • Installation and working with analytics tools
  • Linear and logistic regression approaches.
  • Deploying clustering for analysis.

Who should take up this Online Course in Data Science from Apponix?

  • Big Data, BI and Analyst Professionals
  • Big Data Statisticians
  • Machine Learning Professionals
  • Predictive Analytics and Information Architects
  • Candidates looking for a career in Data Science.

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 conditioned 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 financial 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 referrers will have chance to win holiday to goa, please refer this link None

What are the prerequisites to consider for taking Data Science training from Apponix?

  • Fresh graduates or diploma holders
  • Anybody who is interested to start their career in Data Science and Data analytics

What is the market trend for Data Science in India?

The Data analytics market is steaming up in the India and around the worlds due to the exponential rise of data being gathered.
As users grow their data is being captured by the software’s & business and the business use this data to forecast the consumer behavior.
So, the market offers immeasurable opportunities to skilled and certified Data Scientists employed in India since Data analytics market is booming in our country.

Data analytics is used in daily life in the following instances:

  • Data analytics is used by banks to predict customer risks and NPAs.
  • Data analytics is also used by Insurance Companies for risk analysis.

90% of the world’s existing data has been stored in various types in last decade and is expected to grow exponentially 100 times by 2025

Job roles for candidates with knowledge in Data Science

  • Data Engineer
  • Data Scientist
  • Data Visualizer
  • Data Analyst
  • Business Analyst

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, 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

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

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