Data Science Professional Training

Overview

To accelerate the career, acquire masters in the data science training in India at Max Online Trainings. We offer you world class teaching facility by industry standards teachers. Data science and machine learning is two most important subject which is most demand right now. With us you can easily gain the experience of working with the real-time projects and get open to key technologies which are commonly used for it. Technologies like python, Hadoop, Spark, and R are some of them.
Data science is one of the most popular courses which everyone is getting into. Whether you are professionals or a fresher interested in the data industry job, this is the right training you must get for you.


The things which you will learn


The Data science online training will let you open up the career choice a lot. Max online training will offer you data science online corporate training which is perfectly designed for professionals for career growth.


Here are the subjects you will learn in the data science training program:
  • Prediction analysis using various latest coding languages
  • Python
  • R
  • Machine learning
  • Data visualization
  • Big data management
  • Natural language processing

Prerequisites

If you are an IT professional or a fresher, you can do this program easily. But a minimum of bachelor degree is essential to take up the courses. But no coding experience is required for the course. An online training on the big data ensures you a big package salary which you are waiting for.


Career Aspect with Data Science Online Training Course

Check out these job opportunities you can get once you complete the data science certification course:

  • Data Analyst
  • Data scientist
  • Data engineer
  • Machine learning engineer
  • Decision scientist
  • Product analyst.

So if you are still thinking about the future prospect, join the online training of the data science right now at the Max Online Training.

Data Science is a combination of algorithms, tools, and machine learning technique which helps you to find common hidden patterns from the given raw data.

In the sampling process, there are three types of biases, which are:

  • Selection bias
  • Under coverage bias
  • Survivorship bias

Logistic Regression is also called as the logit model. It is a method to forecast the binary outcome from a linear combination of predictor variables.

A decision tree is a popular supervised machine learning algorithm. It is mainly used for Regression and Classification. It allows breaks down a dataset into smaller subsets. The decision tree can able to handle both categorical and numerical data.

It is a subclass of information filtering techniques. It helps you to predict the preferences or ratings which users likely to give to a product.

Prior probability is the proportion of the dependent variable in the data set while the likelihood is the probability of classifying a given observant in the presence of some other variable.

Resampling is done in below-given cases:

  • Estimating the accuracy of sample statistics by drawing randomly with replacement from a set of the data point or using as subsets of accessible data
  • Substituting labels on data points when performing necessary tests
  • Validating models by using random subsets

Three disadvantages of the linear model are:

  • The assumption of linearity of the errors.
  • You can't use this model for binary or count outcomes
  • There are plenty of overfitting problems that it can't solve

The power analysis is an integral part of the experimental design. It helps you to determine the sample size requires to find out the effect of a given size from a cause with a specific level of assurance. It also allows you to deploy a particular probability in a sample size constraint.

Bias is an error introduced in your model because of the oversimplification of a machine learning algorithm." It can lead to underfitting.

Collaborative filtering used to search for correct patterns by collaborating viewpoints, multiple data sources, and various agents.

Linear regression is a statistical programming method where the score of a variable 'A' is predicted from the score of a second variable 'B'. B is referred to as the predictor variable and A as the criterion variable.

The Naive Bayes Algorithm model is based on the Bayes Theorem. It describes the probability of an event. It is based on prior knowledge of conditions which might be related to that specific event.:

AB testing used to conduct random experiments with two variables, A and B. The goal of this testing method is to find out changes to a web page to maximize or increase the outcome of a strategy.

They are not many differences, but both of these terms are used in different contexts. Mean value is generally referred to when you are discussing a probability distribution whereas expected value is referred to in the context of a random variable.

Eigenvectors are for understanding linear transformations. Data scientist need to calculate the eigenvectors for a covariance matrix or correlation. Eigenvalues are the directions along using specific linear transformation acts by compressing, flipping, or stretching.

Artificial Neural networks (ANN) are a special set of algorithms that have revolutionized machine learning. It helps you to adapt according to changing input. So the network generates the best possible result without redesigning the output criteria.

Cross-validation is a validation technique for evaluating how the outcomes of statistical analysis will generalize for an Independent dataset. This method is used in backgrounds where the objective is forecast, and one needs to estimate how accurately a model will accomplish.

Random forest is a machine learning method which helps you to perform all types of regression and classification tasks. It is also used for treating missing values and outlier values….

Python Course List :

  • What is Python?
  • Why Python?
  • Installation of python
  • Conditions
  • Loops
  • Break statement
  • Continue statement
  • Range functions
  • Command line arguments
  • String Object Basics
  • String Methods
  • Splitting and Joining Strings
  • String format functions
  • List Object Basics
  • List Methods
  • Tuples
  • Sets
  • Frozen sets
  • Dictionary
  • Iterators
  • Generators
  • Decorators
  • List Set Dictionary comprehensions
  • Creating Classes and Objects
  • Inheritance
  • Multiple Inheritance
  • Working with files
  • Reading and Writing files
  • Using Standard Modules
  • Creating custom modules
  • Exceptions Handling with Try-except
  • Finally, in exception handling
  • ND array Object
  • Data Types
  • Array Attributes
  • Array Creation Routines
  • Array from Existing Data
  • Array from Numerical Ranges
  • Indexing & Slicing
  • Advanced Indexing
  • Broadcasting
  • Iterating Over Array
  • Array Manipulation
  • Binary Operators
  • String Functions
  • Mathematical Functions
  • Arithmetic Operations
  • Statistical Functions
  • Sort, Search & Counting Functions
  • Byte Swapping
  • Copies & Views
  • Matrix Library
  • Series
  • Data Frame
  • Panel
  • Basic Functionality
  • Re indexing
  • Iteration
  • Sorting
  • Working with Text Data
  • Options & Customization
  • Indexing & Selecting Data
  • Window Functions
  • Date Functionality
  • Time delta
  • Categorical Data
  • Visualization
  • IO Tools
  • Introduction & Installation
  • Format strings in plot function
  • Axes labels
  • Legend
  • Grid
  • Bar chart
  • Histograms
  • Pie chart
  • Save fig
  • Scatter plots
  • Sub plots
  • Introduction & Installation
  • Bar plot
  • Distributed plot
  • Box plot
  • Strip plot
  • Pair grid
  • Violin Plot
  • Cluster Map
  • Heat Map
  • Facet Grid
  • KDE plot
  • Joint plot
  • Reg plot
  • Pair plot
  • Numerical variables
  • Categorical variables
  • Missing Values
  • Outliers
  • Mean and median imputation
  • Random sample imputation
  • Dummy variables
  • One hot encoding
  • Train and test data split
  • Save model using pickle
  • Descriptive Statistics
  • Sample vs Population
  • Random Variables
  • Probability Distribution function
  • Expected value
  • Binomial Distribution
  • Normal Distributions
  • Z-score
  • Central limit Theorem
  • Hypothesis testing
  • Z-Stats vs T-stats
  • Type 1 & Type 2 error
  • Confidence Interval
  • Chi Square test
  • ANOVA test
  • F-Stats
  • Analyzing Bike Sharing Trends
  • Analyzing Movie Reviews Sentiment
  • Customer Segmentation and Effective Cross Selling Analyzing Wine Types and Quality
  • Analyzing Music Trends and Recommendations Forecasting Stock and Commodity Prices
  • What is Machine Learning
  • Machine Learning Types
  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
  • Deep learning
  • Linear regression
  • Multiple linear regression
  • Gradient Descent
  • Ridge regression
  • Lasso regression
  • Logistic regression-Binary classification
  • Logistic regression-Multi Class classification
  • K Nearest Neighbors (KNN)
  • Naive Bayes
  • Decision trees
  • Random forests
  • Un Supervised Learning
  • K Means Clustering
  • K fold cross validation
  • Hyper parameter tuning
  • Grid Search CV
  • Randomized CV
  • Ensemble Methods
  • Boosting
  • Bagging
  • Introduction
  • Sign up for AWS account
  • Setup Cygwin on Windows
  • Quick Preview of Cygwin
  • Understand Pricing
  • Create first EC2 Instance
  • Connecting to EC2 Instance
  • Understanding EC2 dashboard left menu
  • Different EC2 Instance states
  • Describing EC2 Instance
  • Using elastic IPs to connect to EC2 Instance
  • Using security groups to provide security to EC2 Instance
  • Understanding the concept of bastion server
  • Terminating EC2 Instance and relieving all the resources
  • Create security credentials for AWS account
  • Setting up AWS CLI in Windows
  • Creating s3 bucket
  • Deleting root access keys
  • Enable MFA for root account
  • Introduction to IAM users and customizing sign in link
  • Create first IAM user
  • Create group and add user
  • Configure IAM password policy
  • Understanding IAM best practices
  • AWS managed policies and creating custom policies
  • Assign policy to entities (user and/or group)
  • Creating role for EC2 trusted entity with permissions on s3
  • Assigning role to EC2 instance
  • Deploying Machine Learning Model to AWS
  • 3 Real-Time Projects
  • Deployment on multiple platforms
  • Discussion on project explanation in interview
  • Data scientist roles and responsibilities
  • Data scientist day to day work
  • One to One resume Discussion with project, technology and Experience.
  • Mock interview for every student
  • Real time Interview Questions