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Data Science Online Training

Data Science Is The Composure Of A Few Typically Isolated Parts Of Data Mining Or Big Data That Uses The Scientific Methods, Processes, Statistics, Algorithms, And Systems To Extract Knowledge And Bits Of Knowledge From Structured And Unstructured Information. Data Science Manages Profoundly Amazing Equipment, Most Dominant Programming Frameworks, And The Exceptionally Powerful Calculations To Tackle Inquiries. Information Science Has Combinational Ideas Of Measurements, Data Examination, Machine Learning, And Related Strategies. Harvard Business Survey Referenced Data Science As “The Sexiest Job Of The 21st Century”, Data Science Is The Following Greatest Thing In The Realm Of Innovation.

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Upcoming Batches

Data Science Live Training

04/07/2022

7:00 am

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About Course

Why Choose Us?
OnlineITvidhya institute is giving the best data science course online training in the business. considering the huge and misty ideas in data science managing a diverse assortment of subjects, for example, measurements, data science, information investigation, and AI, we are resolved to give the greatest measure of information proficiently to our students with the best workforce accessible in the field of data science. Our teaching staff will facilitate the ideas to make you alright with the subject and addition standard data and conceptualize each module with much attention. Our staff will consistently be accessible to help you with explanations regarding the course and give you the best involvement with learning this course. Complete help will be given by us to you to be an expert Data Scientist.

Who Can Learn Data Science?
Data Science Online Training course by OnlineITvidhya can be picked by any it professional, data analyst, developer, information technology professional, statistician, graduate, or any individual who wants to be a data scientist. Data Science has a business of 30 billion in the following 10 years, the normal bundle a data scientist procures into his pocket is around $1,25,000 per annum. The development rate is going at a high pace at present in the market, openings for work for a data scientist is improving even rapidly.

Improve Your Career
Taking Data Science Online Training with OnlineITvidhya institute will guarantee your career growth. Our data scientist certification you get after the completion of the data science course is substantially valid all around the globe for a lifetime, our classroom and online practice sessions will reinforce you to confront the circumstances in the associations or innovation industry. The capacities that you need, to be a decent data scientist around the world are being instructed by our staff significantly.

Module 1: Data Science Project Lifecycle

Recap of Demo
Introduction to Types of Analytics
Project life cycle
An introduction to our E learning platform

Module 2: Introduction To Basic Statistics Using R And Python

Data Types
Measure Of central tendency
Measures of Dispersion
Graphical Techniques
Skewness & Kurtosis
Box Plot
R
R Studio
Descriptive Stats in R
Python (Installation and basic commands) and Libraries
Jupyter note book
Set up Github
Descriptive Stats in Python
Pandas and Matplotlib / Seaborn

Module 3: Probability And Hypothesis Testing

Random Variable
Probability
Probability Distribution
Normal Distribution
SND
Expected Value
Sampling Funnel
Sampling Variation
CLT
Confidence interval
Assignments Session-1 (1 hr)
Introduction to Hypothesis Testing
Hypothesis Testing with examples
- 2 proportion test
- 2 sample t test
Anova and Chisquare case studies

Module 4: Exploratory Data Analysis -1

Visualization
Data Cleaning
Imputation Techniques
Scatter Plot
Correlation analysis
Transformations
Normalization and Standardization

Module 5: Linear Regression

Principles of Regression
Introduction to Simple Linear Regression
Multiple Linear Regression

Module 6: Logistic Regression

Multiple Logistic Regression
Confusion matrix
- False Positive, False Negative
- True Positive, True Negative
- Sensitivity, Recall, Specificity, F1 score
Receiver operating characteristics curve (ROC curve)

Module 7: Deployment

R shiny
Streamlit

Module 8: Data Mining Unsupervised Clustering

Supervised vs Unsupervised learning
Data Mining Process
Hierarchical Clustering / Agglomerative Clustering
- Measure of distance
  - Numeric – Euclidean, Manhattan, Mahalanobis
  - Categorical – Binary Euclidean, Simple Matching Coefficient, Jaquard’s Coefficient
  - Mixed – Gower’s General Dissimilarity Coefficient
- Types of Linkages
  - Single Linkage / Nearest Neighbour
  - Complete Linkage / Farthest Neighbour
  - Average Linkage
  - Centroid Linkage
Visualization of clustering algorithm using Dendrogram

Module 9: K-Means

Non-Hierarchial
Measurement metrics of clustering – Within Sum of Squares, Between Sum of Squares, Total Sum of Squares
Choosing the ideal K value using Scree plot / Elbow Curve

Module 10: DBSCAN

A geneal intuition for DBSCAN
Different parameters in DBSCAN
Metrics used to evaluate the performance of model
Pro’s and Con’s of DBSCAN

Module 11: Dimension Reduction Techniques

PCA and tSNE
Why dimension reduction
Advantages of PCA
Calculation of PCA weights
2D Visualization using Principal components
Basics of Matrix algebra

Module 12: Association Rules

What is Market Basket / Affinity Analysis
Measure of association
Support
Confidence
Lift Ratio
Apriori Algorithm

Module 13: Recommender System

User-based collaborative filtering
Measure of distance / similarity between users
Driver for recommendation
Computation reduction techniques
Search based methods / Item to item collaborative filtering
Vulnerability of recommender systems

Module 14: Introduction To Supervised Machine Learning

Workflow from data to deployment
Data nuances
Mindsets of modelling

Module 15: Decision Tree

Elements of Classification Tree – Root node, Child Node, Leaf Node, etc.
Greedy algorithm
Measure of Entropy
Attribute selection using Information Gain
Implementation of Decision tree using C5.0 and Sklearn libraries

Module 16: Exploratory Data Analysis - 2

Encoding Methods
- OHE
- Label Encoders
- Outlier detection-Isolation Fores
Predictive power Score

Module 17: Feature Engineering

Recurcive Feature Elimination
PCA

Module 18: Model Validation Methods

Splitting data into train and test
Methods of cross validation
Accuracy methods

Module 19: Ensembled Techniques

Bagging
Boosting
Random Forest
XGBM
LGBM

Module 20: KNN And Support Vector Machines

Deciding the K value
Building a KNN model by splitting the data
Understanding the various generalization and regulation techniques to avoid overfitting and underfitting
Kernel tricks

Module 21: Regularization Techniques

Lasso Regression
Ridge Regression

Module 22: Neural Networks

Artificial Neural Network
Biological Neuron vs Artificial Neuron
ANN structure
Activation function
Network Topology
Classification Hyperplanes
Best fit “boundary”
Gradient Descent
Stochastic Gradient Descent Intro
Back Propogation
Intoduction to concepts of CNN

Module 23: Text Mining

Sources of data
Bag of words
Pre-processing, corpus Document-Term Matrix (DTM) and TDM
Word Clouds
Corpus level word clouds
- Sentiment Analysis
- Positive Word clouds
- Negative word clouds
- Unigram, Bigram, Trigram
Vector space Modelling
Word embedding
Document Similarity using Cosine similarity

Module 24: Natural Language Processing

Sentiment Extraction
Lexicons and Emotion Mining

Module 25: Naive Bayes

Probability – Recap
Bayes Rule
Naive Bayes Classifier
Text Classification using Naive Bayes

Module 26 - Forecasting

Introduction to time series data
Steps of forecasting
Components of time series data
Scatter plot and Time Plot
Lag Plot
ACF – Auto-Correlation Function / Correlogram
Visualization principles
Naive forecast methods
Errors in forecast and its metrics
Model Based approaches
- Linear Model
- Exponential Model
- Quadratic Model
- Additive Seasonality
- Multiplicative Seasonality
Model-Based approaches
AR (Auto-Regressive) model for errors
Random walk
ARMA (Auto-Regressive Moving Average), Order p and q
ARIMA (Auto-Regressive Integrated Moving Average), Order p, d and q
Data-driven approach to forecasting
Smoothing techniques
- Moving Average
- Simple Exponential Smoothing
- Holts / Double Exponential Smoothing
- Winters / HoltWinters
De-seasoning and de-trending
Forecasting using Python and R

Module 27: Survival Analysis

Concept with a business case

Module 28: End To End Project Description With Deployment

End to End project Description with deployment using R and Python

Lifetime Access

You will be provided with lifetime access to presentations, quizzes, installation guides and notes.

Assessments

After each training module there will be a quiz to assess your learning.

24*7 Support

We have a lifetime 24*7 Online Expert Support to resolve all your Technical queries.

Forum

We have a community forum for our learners that facilitates further learning through peer interaction and knowledge sharing.

Chaitu

I thought it was very difficult but after attending these classes this is very easy for me. Thank U so much onlineITvidhya... Staff is very good... Good response for

Rishab

It's a great opportunity for me to learn DataScience as a fresher. Thank you very much for the training. The management is good. Thank You.

Darshan

Excellent Training from the trainer and support from the management.

FAQs

What if I miss a class?

You will never miss a lecture at OnlineITvidhya You can choose either of the two options: View the recorded session of the class available in your LMS. You can attend the missed session, in any other live batch.

What if I have queries after I complete this course?

Your access to the Support Team is for a lifetime and will be available 24/7. The team will help you in resolving queries, during, and after the course.

Can I Attend a Demo Session before Enrolment?

You can get a sample class recording to ensure you are in right place. We ensure you will be getting complete worth of your money by assigning a best instructor in that technology.

What are the diff modes of training that OITV provides?

At OITV, you can enroll in either the instructor-led online training or self-paced training. Apart from this, OnlineITvidhya also offers corporate training for organizations to upskill their workforce. All trainers at OnlineITvidhya have 12+ years of relevant industry experience, and they have been actively working as consultants in the same domain, which has made them subject matter experts. Go through the sample videos to check the quality of our trainers.

Is it possible to switch from Self paced to instructor led live training?

You can definitely make the switch from self-paced training to online instructor-led training by simply paying the extra amount. You can join the very next batch, which will be duly notified to you.

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(+91) 81433-53888