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Data Science

4.8 ★★★★★ | 85+ Satisfied Learners | Read Reviews

Data Science online training combines a few traditionally distinct components of Data Mining or Big Data and employs scientific procedures, techniques, statistics, algorithms, and systems to draw knowledge and knowledge-related information from both organized and unstructured data. According to a Harvard Business survey, data science is “The Sexiest Job Of The 21st Century,” and it is the next big thing in innovation.


    Upcoming Batches

    Data Science Live Training 7-March-2023 7:00 am Enroll

    About Course

    Why choose us?
    The top data science course online training for businesses is provided by the OnlineITvidhya Institute. We are determined to provide the most information to our students effectively with the best workforce available in the field of data science, taking into account the broad and hazy concepts in data science that cover a wide range of topics, including measurements, data science, information investigation, and AI. Our instructors will help you understand the concepts and offer necessary information, all while conceptualizing each lesson carefully. Our experts will always be available to answer questions about the course and provide you with the greatest learning experience possible. We will provide you with full assistance to become a knowledgeable Data Scientist.

    Who can learn Data Science?
    Any IT expert, data analyst, developer, IT professional, statistician, graduate, or someone who aspires to become a data scientist can choose OnlineITvidhya’s Data Science Online Training course. In the next ten years, data science will generate $30 billion in revenue, and the typical salary for a data scientist is $1,25,000 per year. At the moment, the market is developing quickly, and job opportunities for data scientists are expanding even faster.

    What are the prerequisites of Online IT Vidhya’s Data Science Online Training program?
    Data is the central focus of data science, as the name suggests. Therefore, having a passion for, a comprehension of, and the capacity to work with data is the first and most crucial requirement for learning data science.
    Big data wranglers can be thought of as data scientists. They examine enormous amounts of organized and unstructured data. The process, analyse, and model of data using computer science, mathematics, and statistics in order to interpret relevant results.

    Placement Assistance:
    Your career will advance if you enroll in OnlineITvidhya institute’s Data Science Online Training program. Following the completion of the data science course, you will receive a data scientist certification that is broadly valid throughout the world for a lifetime. Our in-person and online practice sessions will prepare you for the challenges you may face in the associations or innovation business. Our staff invests a lot of time in teaching you the skills you need to be a good data scientist anywhere in the globe.

    Curriculum

    • Recap of Demo
    • Introduction to Types of Analytics
    • Project life cycle
    • An introduction to our E learning platform
    • 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
    • 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
    • Visualization
    • Data Cleaning
    • Imputation Techniques
    • Scatter Plot
    • Correlation analysis
    • Transformations
    • Normalization and Standardization
    • Principles of Regression
    • Introduction to Simple Linear Regression
    • Multiple Linear 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)
    • R shiny
    • Streamlit
    • 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
    • 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
    • A geneal intuition for DBSCAN
    • Different parameters in DBSCAN
    • Metrics used to evaluate the performance of model
    • Pro’s and Con’s of DBSCAN
    • PCA and tSNE
    • Why dimension reduction
    • Advantages of PCA
    • Calculation of PCA weights
    • 2D Visualization using Principal components
    • Basics of Matrix algebra
    • What is Market Basket / Affinity Analysis
    • Measure of association
    • Support
    • Confidence
    • Lift Ratio
    • Apriori Algorithm
    • 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
    • Workflow from data to deployment
    • Data nuances
    • Mindsets of modelling
    • 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
    • Encoding Methods
      • OHE
      • Label Encoders
      • Outlier detection-Isolation Fores
    • Predictive power Score
    • Recurcive Feature Elimination
    • PCA
    • Splitting data into train and test
    • Methods of cross validation
    • Accuracy methods
    • Bagging
    • Boosting
    • Random Forest
    • XGBM
    • LGBM
    • 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
    • Lasso Regression
    • Ridge Regression
    • 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
    • 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
    • Sentiment Extraction
    • Lexicons and Emotion Mining
    • Probability – Recap    
    • Bayes Rule
    • Naive Bayes Classifier
    • Text Classification using Naive Bayes
    • 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
    • Concept with a business case
    • End to End project Description with deployment using R and Python

    Features

    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.

    Certificate

    After successfully complete your course OnlineITvidhya will give you course completion Certificate.

    Mock Interviews

    Explore what the real-time interviews expect from you.

    Reviews

    C

    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

    R

    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.

    D

    Darshan

    Excellent Training from the trainer and support from the management.

    FAQs

    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.
    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.
    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.
    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.
    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.