Data Science and Machine Learning Training in Hyderabad

What is Data Science and Machine Learning is all about?

Data Science is Data administration and management. As many of the IT corporations are fully loaded with information, security and explosion problems are occurring frequently these days. This data science course gets the deeper and yet knowledgeable course for the data analytics professionals to work on the interdisciplinary field of scientific methods, processes, and systems to extract knowledge, secure Data or insights from data in various forms.

Machine Learning consolidates the computer science and statistics together to employ the predictive skill. As loads of data sources increase the data simultaneously with the computing power, going straight to the data is one of the most outspoken ways to quickly gain insights and make predictions. This might not possible through the direct methods whereas it can be done with the Machine Learning techniques.

Why this Data Science and Machine Learning Course?

This course incorporates all the necessary concepts and explicit learning of leading analytical tools, such as Python for Data Science Training and Machine Learning, R for Data Science and Tableau for Data Analytics through industry case studies. Over the course of 3 months, candidates will not only gain theoretical knowledge of data science tools but also gain exposure to business perspectives and industry best practices through Assignments, Group discussions, multiple project submissions along with hands-on experience in real-time projects and can build a portfolio of demonstrable work.

Data Science Course for Beginners consists of Core Java Basics, Machine Learning with R and Python, Tableau for Data Science, Data Visualization using Tableau, Statistics Using Minitab, Data Processing using SQL, Microsoft Excel, R, and Python

We have two training modes:

1. Data Science Classroom Training at Ameerpet, Madhapur in Hyderabad, India
2. Data Science Training Online

Data Science and Machine Learning Training Course curriculum have been outfitted by acknowledging the current industry standards in mind which are constantly prone to alterations with latest progressions in the subject.

Real-Time case studies are highlighted by our panel of expert trainers. Module based assignments are conducted to ensure consistent understanding among the trainees'. Certifications are awarded by the implementation of Data Science and Machine Learning Projects submitted by every student.

Students are encouraged to practice on their technical ideas and qualified guidance is assured by our Certified/ Analytical Data Science Professionals.

Data Science and Machine Learning Course Highlights

• The program comes with the most-advanced industry-aligned curriculum.
• The course is presented in two modes: Classroom and Online training, using a practical hands-on learning methodology.
• 100% Placement, Internship and Live Project assistance by industry’s professionals.
• Students will be provided Videos, Backup Classes, Revision Classes, Assignments and projects.
• Digital Nest Certificate Guidance
• Deep understanding of Data Analytics and Statistics along with business perspectives and cutting-edge Practices
• Comprehensive coverage of R, Python, Tableau, SQL and Python.

Data Science and Machine Learning Course Structure

Introduction

• Introduction to Data Science?
• Introduction to Machine Learning?
• Introduction to Analytics?
• Introduction to Data analysis and Data Mining?
• Analytics project life cycle
• Real life applications, projects and career paths of Data Science and Big Data

Module 1: Statistics

• Definition and computation of probability
• Measurement of central tendencies and its applications
• Spreads, Distributions(Normal, Z-distribution, Binomial, Poisson) and various types of probability distributions(Continuous and discrete)
• Sampling and Sampling Distributions
• Measures of shape( Skewness and Kurtosis)
• Measures of relationship between variables(Correlation, causation)
• Hypothesis Testing(t-test, Chi-square, ANOVA)
• Measures of Dispersion( Variance, Std. deviation, Range)
• Prediction and Confidence interval-Computation and Analysis
• Missing Value theorem
• Exam

Module 2: Exploratory Data Analysis(EDA) and Data Visualization

• What is EDA and why is it required?
• Outlier treatment
• Data distributions and transformations
• Graphs
• Bar charts
• Histogram
• Box-Whisker plot
• Scatter plot
• Variable selection
• Bubble charts
• Exam

Module 3: Data processing using MS Excel

• Inbuilt functions
• Lookup tables
• Rank determination
• Conditional formatting
• Data Validation
• Pivot tables
• Exam

Module 4: Data manipulation using SQL

• Introduction to SQL and Databases
• SQL developer installation
• Data types
• Data types and Operators
• Create and Drop database
• DDL, DML, DCL , TCL, Sorting commands and other keywords
• Advanced SQL-Wild cards, Constraints, Joins, Unions, NULL, Alias, Truncate, Views, Subqueries
• Exam

Module 5: Introduction to R

• Why R and importance of R in Analytics?
• Installation of R and R-studio
• Data types
• Variables
• Operators
• Decision making
• Loops
• Lists
• Vectors
• Strings
• Matrices
• Arrays
• Factors
• Functions (Built-in and User defined functions)
• Importing Data from texts and spreadsheets
• Data frames
• Packages, libraries and their installation
• Data manipulation and re-shaping
• Data Visualization using R
• Exam

Module 6: Machine Learning

• Supervised Learning
  • What is supervised learning
  • Algorithms in Supervised learning
  • Steps in Supervised learning
• Regression Analysis
  • Regression vs classification
  • Computation of co-relation coefficient and Analysis
  • Performance and accuracy measurement of a Model
  • Model Training, Validation and Testing
  • Ordinary Least squares
  • Variable selection
  • R-Square coefficient and RMSE as a strength of model
  • Prediction and confidence interval determination and application
  • Proviso of Regression
  • Dummy variables
  • Types of Regression: Linear and Logistic( Simple and multiple)
  • Sum of least squares
  • ROC and AUC curves
  • Homoscedasticity and Heteroscedasticity
  • Multicollinearity and vif
  • Confusion matrix
  • Techniques to improve accuracy and performance of regression models
  • Assignment
• Decision Trees and Random Forest Test
  • Decision nodes and leaf nodes
  • Variable Selection, Parent and child nodes branching
  • Stopping Criterion
  • Tree pruning
  • Depth of a tree
  • Overfitting
  • Metrics for decision trees-Gini impurity, Information Gain, Variance Reduction
  • Regression using decision tree
  • Interpretation of a decision tree using If-else
  • Pros and cons of a decision tree
  • Introduction to Random forest test and it’s applications
  • Why Random forest test?
  • Tree bagging
  • Models and algorithms in Random Forest test
  • Training Data set Tree grouping and decision making on majority voting
  • Boosting algorithms-Gradient Boosting, Adaptive boosting-Adaboost, XP boost ( Advanced)
  • Accuracy estimation using cross-validation
• KNN-algorithm
  • What is KNN and why do we use it?
  • KNN-algorithm and regression
  • Curse of dimensionality and brief introduction to dimension reduction
  • KNN-outlier treatment and anomaly detection
  • Cross-Validation
  • Pros and cons of KNN
• Support Vector Machines
  • Linear and Non-Linear SVM’s
  • SVM regression
  • Train time and Runtime complexities
  • Kernel Methods

Module 7: Unsupervised Learning

• Introduction to unsupervised learning?
• Algorithms in unsupervised learning
• Steps in unsupervised learning
• Dimensionality Reduction
  • Introduction to dimensionality reduction and it’s necessity
  • Principal Component Analysis(PCA)
  • Singular Value Decomposition(SVD)
  • Kernel-PCA
  • Linear Discriminant Analysis
  • Feature extraction
  • Advantages and applications of Dimensionality reduction
• Clustering
  • Introduction to clustering
  • Real-life applications of clustering
  • Distance measurement methods
  • Hierarchical clustering
  • K-Means clustering and skew plot
  • Assignment

Module 8: Text Mining

• Introduction to Text Mining
• Applications
• Structured and unstructured data
• Extracting unstructured text from files and websites
• Data cleaning and reshaping
• Terminologies in Text Mining
• Text clustering and categorization
• Word cloud
• N-gram charts
• Sentiment Analysis
• Twitter Analytics
• Natural Language processing
• Assignment

Module 9: Forecasting

• Introduction to forecasting
• Applications
• Data Manipulation and Cleaning
• Time Series
• Time Series Forecasting
• Components of Time Series-Trend, Seasonality, Randomness
• Trend Analysis
• Forecasting methods
• Smoothing Methods
• Modeling Random Components
• Modeling for stationary time series
• ETS Model
• Auto regressive Model
• Moving Average Model
• ARIMA Model
• ETS Model
• Anomaly Detection
• Transformations
• Growth curve
• ARCH & GARCH Models

Module 11: Recommender Systems

• Introduction
• Real-life applications
• Collaborative filtering
• Content-based filtering
• Hybrid Recommender Systems
• Metrics of Recommender Systems

Module 12: Data Science Using Python

• Introduction to Python programming
  • What is Python?
  • History
  • Why is Python preferred for Data Science?
  • Installation of I python/Jupyter Notebook/ SPYDER
• Basics of Python
  • Keywords
  • Built-in functions
  • String Formatting
  • Lists
  • Loops
  • Tuples
  • Indexing
  • Slicing
  • Sequences
  • Dictionaries
  • Sets
  • Importing and exporting data from python into various formats
• Functions
  • User-defined functions
  • Parameters
  • Nested functions
  • Local and Global variables
  • Alternate Keys
  • Lambda functions
  • Sorting Lists and Dictionaries
  • Sorting Collections
• Error and Exception handling
  • Errors in Python
  • Abnormal termination
  • Exception handling methods
  • Ignoring Errors
  • Assertions and effective usage of assertions
• OOPS, Packages and Libraries in Python
  • Methods and Inheritance
  • Abstraction and Encapsulation
  • Classes
  • Walking Directory Trees
  • Initializes
  • Instance methods
  • Class methods
  • Data Static Methods
  • Expressions
  • Module Aliases
  • Math functions
  • Random Numbers
  • Package Installation Methods
  • Introduction to Numpy, Pandas  and other libraries
  • Plotting in Python
  • Creating Data Frames
  • Data Manipulation
  • Slicing and Dicing

Module 13:Machine Learning & AI Using Python

• Introduction to Sckit- Learn
• Pre-processing Data
• Model Selection and Cross-Validation
• Implementing Machine Learning Algorithms using Python
• Implementing neural networks in Python
• Tensor Flow and Keras using Python
• Case Studies

Module 14: Data Visualization Using Tableau

• Introduction to Tableau
  • Installation of Trial Version of Tableau Public
  • Design Flow
  • Data Viewing
  • Connecting Tableau to various Data Files
  • Measures and Dimensions
  • Colors, Labeling and formatting
  • Exporting Worksheet
• Basics of Tableau
  • A-B Ad-hoc Testing
  • Aliases
  • Reference Line
  • Anomaly detection
  • Sorts and Filters
  • Time Series
  • Chart plotting
  • Heat Maps
  • Data Joining
  • Data Blending
• Advanced Concepts of Tableau
  • Trend Line Analysis
  • Dash Board Creation
  • Formatting in tableau
  • Forecasting using Exponential Smoothing
  • Granularity and Trimming
  • Seasonality
  • Animations
  • Assignment

Module 14: Artificial Intelligence

• Deep Learning
  • Deep Learning Overview
  • Conditional probability
  • Joint Probability
  • Naïve Bayes Classification
  • Baye’s Rule
  • Deep learning frameworks- Tensor Flow, Keras, Theano
  • Introduction to Artificial Neural Networks (ANN)
  • Activation functions
  • Network Topology
  • Working& Learning of Artificial Neural Networks
  • Gradient Descent
  • Logistic regression Gradient Descent
  • Backpropagation
  • Convolution neural networks(CNN)
  • Introduction to Recurrent Neural Networks(RNN) and LSTM
• Association rules
  • Introduction
  • Importance of Association rules
  • Metrics of rules-Lift, Support, Confidence, Conviction
  • Apriority Model
  • Algorithm implementation and tuning
  • Applications
  • Assignment

Big Data & Data Science Course Reviews