Data Science Online Training

Data Science Training by Experts of Zenith Trainings. Data Science is a Software here distributing and processing the large set of data into the cluster of computers.

This Course is designed to Master yourself in the Data Science Techniques and Upgrade your skill set to the next level to maintain your career in ever changing the software Industry.

This course training covers from the basics of Data Science to Big Data Hadoop, Python, Apache Spark & etc.

  • Introduction to BIG Data Science/Data Analytics
  • What background is required?
  • What is Data Science?
  • Why Data Science?
  • BIG Data Science/Analytics trend
  • What is Machine Learning?
  • Data Science Life Cycle
  • Tools for Data Science/Analytics
  • Anaconda Distribution package
  • Open Source: Python/R
  • Visualization tools: Matplotlib, Seaborn, introduction of Tableau
  • Data Analytics Problems/Use-cases
  • From Kaggle competitions
  • Types of Data: Structured, Unstructured (Image, Text…..)
  • Predictive Analytics Problems: Classification, Regression, Recommenders
  • Descriptive Analytics Problems: Clustering, Market Basket Analysis, PCA
  • Business Verticals: Real Estate, Retail, Financial, Banking, Social, Web, Medical, Scientific & Logistics
  • Visualization tools:
  • Matplotlib,
  • Seaborn,
  • Introduction of Tableau
  • Statistics for Data Scientist
  • Descriptive Statistics for single variables
  • Mean, Median, Mode, Quartile, Percentile
  • Interquartile Range
  • Standard Deviation
  • Variance
  • Descriptive Statistics for two variables
  • Z-Score
  • Co-variance
  • Co-relation
  • Chi-squared Analysis
  • Hypothesis Testing
  • Calculus for Data Scientist
  • Limits
  • Derivatives
  • Partial Derivatives
  • Gradients
  • Significance of Gradients
  • Probability for Data Scientist
  • Basic Probability
  • Conditional Probability
  • Properties of Random Variables
  • Expectations
  • Variance
  • Entropy and cross-entropy
  • Covariance and correlation
  • Estimating probability of Random variable
  • Understanding standard random processes
  • Data Distributions
  • Normal Distribution
  • Binomial Distribution
  • Multinomial Distribution
  • Bernoulli Distribution
  • Probability, Prior probability, Posterior probability
  • Bayes Theorem
  • Naive Bayes
  • Naive Bayes Algorithm
  • Normal Distribution
  • Mastering Python/R Language
  • How to install python (Anaconda)
  • How to install sciKit Learn (Anaconda)
  • How to work with Jupyter Notebook
  • How to work with Spyder IDE
  • Strings
  • Lists
  • Tuples
  • Sets
  • Dictionaries
  • Control Flows
  • Functions
  • Formal/Positional/Keyword arguments
  • Predefined functions (range, len, enumerates etc…)
  • Data Frames
  • Packages required for data Science in R/Python
  • Lab/Coding
  • Introduction to NumPy
  • One-dimensional Array
  • Two-dimensional Array
  • Pre-defined functions (arrange, reshape, zeros, ones, empty)
  • Basic Matrix operations
  • Scalar addition, subtraction, multiplication, division
  • Matrix addition, subtraction, multiplication, division and transpose
  • Slicing
  • Indexing
  • Looping
  • Shape Manipulation Stacking
  • Introduction to Pandas
  • Series
  • DataFrame
  • GroupBy
  • crosstab
  • apply
  • map
  • Decision Trees
  • What are Decision Trees?
  • Gini, Entropy criterions
  • Decision trees in Classification
  • Decision trees in Regression
  • Ensembles
  • Random Forest
  • Boosting (Ada, Gradient, Extreme Gradient)
  • SVM
  • Ensembles
  • Overfitting/Under fitting
  • Understand what is overfitting and under fitting model
  • Visualize the overfitting and under fitting model
  • How do you handle overfitting?
  • Data Preparation Techniques
  • Structured Data Preparation
  • Data Type Conversion
  • Category to Numeric Conversion
  • Numeric to Category Conversion
  • Data Normalization: 0-1, Z-Score Handling Skew Data: Box-Cox Transformation
  • Handling Missing Data
  • Re-sampling Techniques
  • K-fold
  • Repeated Hold-out Data
  • Bootstrap aggregation sampling
  • Exploratory Data Analysis (EDA)
  • Statistical Data Analysis
  • Data Visualization (Matplotlib, Seaboarn)
  • Exploring Individual Features
  • Exploring Bi-Feature Relationships
  • Exploring Multi-feature Relationships
  • Feature/Dimension Reduction: PCA Intuition behind PCA
  • Covariance & Correlation Relating PCA to Covariance/Correlation
  • Intuition to math
  • Applications of PCA: Dimensionality Reduction
  • Feature Engineering (FE)
  • Combine Features
  • Split Features
  • Data Visualization
  • Bar Chart
  • Histogram
  • Box whisker plot
  • Line plot
  • Scatter Plot
  • Heat Map
  • Tree Based Algorithms
  • Gini Index
  • Entropy
  • Information Gain
  • Tree Pruning
  • Classification (Supervised Learning)
  • What is Classification?
  • Finding Patterns/Fixed Patterns
  • Problems with Fixed Patterns
  • Machine learning approach over fixed pattern approach
  • Decision Tree based classification
  • Ensemble Based Classification
  • Logistic Regression (SGD Classifier)
  • Accuracy measurements
  • Confusion Matrix
  • ROC Curve
  • AUC Score
  • Multi-class Classification
  • Softmax Regression Classifier
  • Multi-label Classification
  • Multi-output Classification
  • Ensemble models
  • Random Forest
  • Bagging
  • Boosting
  • Adaptive Boosting
  • Gradient Boosting
  • Extreme Gradient Boosting
  • Heterogeneous Ensemble Models Stacking
  • Voting
  • Regression (Supervised Learning)
  • What is regression?
  • Regression example in business verticals
  • Solution strategies for Regression Linear Regression
  • Explanation of statistics
  • Evaluation metrics
  • Root Mean Squeare(RMSE)
  • R-Squre,
  • Adj R-Squre
  • Feature selection methods
  • Linear regression
  • Multiple/Polynomial Regression (scikit-learn)
  • Multiple Linear Regressions (SGD Regressor)
  • Gradient Descent (Calculus way of solving linear equation)
  • Feature Scaling (Min-Max vs Mean Normalization)
  • Feature Transformation
  • Polynomial Regression
  • Matrix addition, subtraction, multiplication and transpose
  • Optimization theory for data scientist
  • Optimisation Theory (Gradient Descent Algorithm)
  • Modeling ML problems with optimization requirements
  • Solving unconstrained optimization problems
  • Solving optimization problems with linear constraints
  • Gradient descent ideas
  • Gradient descent
  • Batch gradient descent
  • Stochastic gradient descent
  • Model Evaluation and Error Analysis
  • Train/Validation/Test split
  • K-Fold Cross Validation
  • The Problem of Over-fitting (Bias-Variance tread-off)
  • Learning Curve
  • Regularization (Ridge, Lasso and Elastic-Net)
  • Hyper Parameter Tuning (GridSearchCV)
  • Recommendation Problem
  • What is Recommendation System?
  • Top-N Recommender
  • Rating Prediction
  • Content based Recommenders
  • Limitations of Content based
  • recommenders
  • Machine Learning Approaches for Recommenders
  • User-User KNN model, Item-Item KNN model
  • Factorization or latent factor model Hybrid Recommenders
  • Evaluation Metrics for Recommendation Algorithms
  • Top-N Recommnder: Accuracy, Error Rate
  • Rating Prediction: RMSE
  • Clustering (Unsupervised Learning)
  • Finding pattern and Fixed Pattern Approach
  • Limitations of Fixed Pattern Approach
  • Machine Learning Approaches for Clustering
  • Iterative based K-Means Approaches
  • Density based DB-SCAN Approach
  • Evaluation Metrics for Clustering
  • Cohesion, Coupling Metrics
  • Correlation Metric
  • Support Vector Machine (SVM)
  • SVM Classifier (Soft/Hard – Margin)
  • Linear SVM
  • Non-Linear SVM
  • Kernel SVM
  • SVM Regression
  • PCA (Unsupervised Learning)
  • Dimensionality Reduction
  • Choosing Number of Dimensions or Principal Components
  • Incremental PCA
  • Kernel PCA
  • When to apply PCA?
  • Eigen vectors
  • Eigen values
  • Model Deployment
  • Pickle (pkl file)
  • Model load from pkl file and prediction
  • Association Rules
  • A priori Algorithm
  • Collaborative Filtering (User-Item based)
  • Collaborative Filtering (User-User based)
  • Collaborative Filtering (Item-Item based)
  • Deep Learning:
  • Introduction to Deep Learning Tensorflow
  • Keras
  • Setting up new environment for Deep Learning
  • Perceptron model for classification and regression
  • Perceptron Learning
  • Limitations of Perceptron model
  • Multi-layer FF NN model for classification and regression
  • ML-FF-NN Learning with backpropagation
  • Applying ML-FF-NN and parameter tuning
  • Pros and Cons of the Model
  • Image classification
  • Image Data Preparation
  • Converting to gray scale
  • Pixel Value Normalization
  • Building Pixel Intensity Matrix
  • Neural Networks
  • Fully connected Neural Networks
  • Feed Forward Neural Networks
  • Convolution Neural Networks
  • Filters, Max Pooling
  • Functional APIs
  • Text analytics:
  • Bag of words
  • Glove Dictionary
  • Text Data Preparation
  • Normalizing Text
  • Stop word Removal
  • Whitespace Removal
  • Stemming
  • Building Document Term Matrix
  • NLP (Natural Language Processing)
  • Highlights of the Course:
  • Teaching is oriented towards –
  • Practical oriented & Hands on clear understanding of basics
  • what to expect as an interview question while subject discussion
  • Complete Access to a variety of latest interview questions and answers
  • Work on real-time Scenarios
  • Certification guidance & Material
  • Assistance in Resume preparation Interviews guidance
  • Corporate level Training
  • Finally, this training gives you all that are needed to secure a wanted job and keeps you get going in your job!

Q. Why Should I Learn Data Science From Zenith Trainings?

A: Zenith Trainings provides the best Data Science training for professionals looking to master this exciting and challenging field. In this training course you will learn about Data Science, methods of data acquisition, project life cycle, deploying machine learning and statistical methods along with studying about Apache Mahout, data transformation and working with recommends. You will be working on real time projects that have high relevance in the corporate world, step by step assignments and curriculum designed by industry experts. Upon completion of the training course you can apply for some of the best jobs in top MNCs around the world at top salaries. Zenith Trainings offers lifetime access to videos, course materials, 24/7 Support, and course material upgrading to latest version at no extra fees. Hence it is clearly a one-time investment.

Q. Can I Request For A Support Session If I Need To Better Understand The Topics?

A: Zenith Trainings is offering the 24/7 query resolution and you can raise a ticket with the dedicated support team anytime. You can avail the email support for all your queries. In the event of your query not getting resolved through email we can also arrange one-to-one sessions with the trainers. You would be glad to know that you can contact Zenith Trainings support even after completion of the training. We also do not put a limit on the number of tickets you can raise when it comes to query resolution and doubt clearance.

Kamlesh Kumar

The course content was very accessible to the novice learner. The teaching method of the instructor was really good. I had a great experience.

Kavya

Purchased the course after watching their sample videos. Best training class I ever had in my life. This is a good course for anybody, new to Data Science or an experienced. This is delivered by a data science expert.

Contact Us

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Offer:
Get Self-Paced Videos Free With This Course!

Self-Paced ($300)

  • Lifetime access with high-quality content and class videos
  • 40 hours of course presentations by hands-on experts
  • 26 hours of lab time
  • 24×7 online support

Live Online Training ($300)

Mon -Fri (6 Weeks)——————————————————
Mon -Fri (6 Weeks)

Project Support ($500)

  • Daily 2 hours session
  • 6 Days support per week