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Transform into a skilled data scientist with 360DigiTMG’s Data Scientist Course in Nagpur. Cover essential topics like regression, clustering, NLP, machine learning, and visualization. With practical training and placement support, the course offers a complete pathway for students and professionals to enter the fast-growing field of data science.

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Outcomes of Data Science Course in Nagpur

In this data-driven environment, 360DigiTMG's certification prepares you for the surging demand for Big Data skills and technology in all the leading industries. There is a considerable career prospect available in data science, and our certification program is one of the most comprehensive in the industry today.
This Data science institute in Nagpur is specially designed to suit both data professionals and beginners who want to make a career in this fast-growing profession. This training will equip the students with logical and relevant programming abilities to build database models. They can create simple machine learning algorithms like K-Means Clustering, Decision Trees, and Random Forests to solve problems and communicate the solutions effectively.
In three months, students will also explore critical techniques such as Statistical Analysis, Regression Analysis, Data Mining, Machine Learning, Forecasting and Text Mining, and scripting algorithms with Python and R Programming. Understand the fundamental concepts of Neural Networks and study Deep Learning Black Box techniques like SVM.

Work with various data generation sources

Perform Text Mining to generate Customer Sentiment Analysis

Analyze structured and unstructured data using different tools and techniques

Develop an understanding of Descriptive and Predictive Analytics

Apply Data-driven, Machine Learning approaches for business decisions

Build models for day-to-day applicability

Perform Forecasting to take proactive business decisions

Use Data Concepts to represent data for easy understanding

Who Should Sign Up?

Data Science Course Modules in Nagpur

This data science program follows the CRISP-DM Methodology. The premier modules are devoted to a foundational perspective of Statistics, Mathematics, Business Intelligence, and Exploratory Data Analysis. The successive modules deal with Probability Distribution, Hypothesis Testing, Data Mining Supervised, Predictive Modelling - Multiple Linear Regression, Lasso And Ridge Regression, Logistic Regression, Multinomial Regression, and Ordinal Regression. Later modules deal with Data Mining Unsupervised Learning, Recommendation Engines, Network Analytics, Machine Learning, Decision Tree and Random Forest, Text Mining, and Natural Language Processing. The final modules deal with Machine Learning - classifier techniques, Perceptron, Multilayer Perceptron, Neural Networks, Deep Learning Black-Box Techniques, SVM, Forecasting, and Time Series algorithms. This is the most enriching training program in terms of the array of topics covered.

Learn about insights on how data is assisting organizations to make informed data-driven decisions. Gathering the details about the problem statement would be the first step of the project. Learn the know-how of the Business understanding stage. Deep dive into the finer aspects of the management methodology to learn about objectives, constraints, success criteria, and the project charter. The essential task of understanding business Data and its characteristics is to help you plan for the upcoming stages of development. Check out the CRISP - Business Understanding here.

All About 360DigiTMG & Innodatatics Inc., USA

Dos and Don'ts as a participant

Introduction to Big Data Analytics

Data and its uses - a case study (Grocery store)

Interactive marketing using data & IoT - A case study

Course outline, road map, and takeaways from the course

Stages of Analytics - Descriptive, Predictive, Prescriptive, etc.

Cross-Industry Standard Process for Data Mining

The raw Data collected from different sources may have different formats, values, shapes, or characteristics. Cleansing, or Data Preparation, Data Munging, Data Wrapping, etc., are the next steps in the Data handling stage. The objective of this stage is to transform the Data into an easily consumable format for the next stages of development.

Feature Engineering on Numeric / Non-numeric Data

Feature Extraction

Feature Selection

What is Power BI?

Introduction to Power BI

Overview of Power BI

Architecture of PowerBI

PowerBI and Plans

Installation and introduction to PowerBI

Transforming Data using Power BI Desktop

Importing data

Changing Database

Data Types in PowerBI

Basic Transformations

Managing Query Groups

Splitting Columns

Changing Data Types

Working with Dates

Removing and Reordering Columns

Conditional Columns

Custom columns

Connecting to Files in a Folder

Merge Queries

Query Dependency View

Transforming Less Structured Data

Query Parameters

Column profiling

Query Performance Analytics

M-Language

Learn the preliminaries of the Mathematical / Statistical concepts which are the foundation of techniques used for churning the Data. You will revise the primary academic concepts of foundational mathematics and Linear Algebra basics. In this module, you will understand the importance of Data Optimization concepts in Machine Learning development. Check out the Mathematical Foundations here.

Data Optimization

Derivatives

Linear Algebra

Matrix Operations

Data mining unsupervised techniques are used as EDA techniques to derive insights from the business data. In this first module of unsupervised learning, get introduced to clustering algorithms. Learn about different approaches for data segregation to create homogeneous groups of data. In hierarchical clustering, K means clustering is the most used clustering algorithm. Understand the different mathematical approaches to perform data segregation. Also, learn about variations in K-means clustering like K-medoids, and K-mode techniques, and learn to handle large data sets using the CLARA technique.

Clustering 101

Distance Metrics

Hierarchical Clustering

Non-Hierarchical Clustering

DBSCAN

Clustering Evaluation metrics

Dimension Reduction (PCA and SVD) / Factor Analysis Description: Learn to handle high dimensional data. The performance will be hit when the data has a high number of dimensions and machine learning techniques training becomes very complex, as part of this module you will learn to apply data reduction techniques without any variable deletion. Learn the advantages of dimensional reduction techniques. Also, learn about yet another technique called Factor Analysis.

Prinicipal Component Analysis (PCA)

Singular Value Decomposition (SVD)

Learn to measure the relationship between entities. Bundle offers are defined based on this measure of dependency between products. Understand the metrics Support, Confidence, and Lift used to define the rules with the help of the Apriori algorithm. Learn the pros and cons of each of the metrics used in Association rules.

The study of a network with quantifiable values is known as network analytics. The vertex and edge are the nodes and connection of a network, learn about the statistics used to calculate the value of each node in the network. You will also learn about the google page ranking algorithm as part of this module.

Entities of a Network

Properties of the Components of a Network

Measure the value of a Network

Community Detection Algorithms

Machine Learning primer

Difference between Regression and Classification

Evaluation Strategies

Hyper Parameters

Metrics

Overfitting and Underfitting

k Nearest Neighbor algorithm is a distance-based machine learning algorithm. Learn to classify the dependent variable using the appropriate k value. The KNN Classifier also known as a lazy learner is a very popular algorithm and one of the easiest for application.

Learn about overfitting and underfitting conditions for prediction models developed. We need to strike the right balance between overfitting and underfitting, learn about regularization techniques L1 norm and L2 norm used to reduce these abnormal conditions. The regression techniques of Lasso and Ridge techniques are discussed in this module.

Extension to logistic regression We have multinomial and Ordinal Logistic regression techniques used to predict multiple categorical outcomes. Understand the concept of multi-logit equations, baseline, and making classifications using probability outcomes. Learn about handling multiple categories in output variables including nominal as well as ordinal data.

Kaplan Meier method and life tables are used to estimate the time before the event occurs. Survival analysis is about analyzing the duration of time before the event. Real-time applications of survival analysis in customer churn, medical sciences, and other sectors are discussed as part of this module. Learn how survival analysis techniques can be used to understand the effect of the features on the event using the Kaplan-Meier survival plot.

The Perceptron Algorithm is defined based on a biological brain model. You will talk about the parameters used in the perceptron algorithm which is the foundation of developing much complex neural network models for AI applications. Understand the application of perceptron algorithms to classify binary data in a linearly separable scenario.

Neurons of a Biological Brain

Artificial Neuron

Perceptron

Perceptron Algorithm

Use case to classify a linearly separable data

Multilayer Perceptron to handle non-linear data

Neural Network is a black box technique used for deep learning models. Learn the logic of training and weights calculations using various parameters and their tuning. Understand the activation function and integration functions used in developing a Artificial Neural Network.

Integration functions

Activation functions

Weights

Bias

Learning Rate (eta) - Shrinking Learning Rate, Decay Parameters

Error functions - Entropy, Binary Cross Entropy, Categorical Cross Entropy, KL Divergence, etc.

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Data Science Course in Nagpur - Fees, Modules, Certification

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In this data-driven environment, 360DigiTMG's certification prepares you for the surging demand for Big Data skills and technology in all the leading industries. There is a considerable career prospect available in data science, and our certification program is one of the most comprehensive in the industry today.