Best Data Science Training

Best Data Science Training by Sapalogy offers you the best Data Science training in nagpur for freshers and experience candidate in nagpur, with expert guidance and 100% guaranteed placement assistance. Our Data Science training course covers every technical topics in details and the student is left with real-time hand on experience.

Our practical, Job-oriented training program will not only provide you with a certificate but also with knowledge equivalent to around 2+ years of field experience. We value your time as much as over. Hence we invented to train you in Data Science in just 7 months.

All the topics will be covered in details and also include

Chat gpt
Generative AI
LLM (Large language models)
Computer vision
Neural network
Interview preparation
Case studies
Multiple projects
Industry level projects
With 100% job opportunities guaranted program

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What is Data Science ?

Data science is the field of study that combines domain expertise, programming skills and knowledge of mathematics and statistics to extract meaningful insights from data.
Some of the techniques utilize in data science include machine learning, visualisation, pattern recognition, probability modeling data, data engineering, signal processing etc.
Data science in one of the most in demand jobs of the 21st century. A majority of companies now rely on data science to make informed decisions about their future an create an action plan.

Sapalogy cover all the data science skills to get you hired in 2024.
Sapalogy taining provides data science training in offline and online mode. Starting end user, consulting, implementation, support with real time data science project based training.
IT background, non IT background, freshers, experience can start their career in data science irrespective of their background.
Sapalogy is the best training institute in nagpur with the 100% job opportunities.

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Roadmap to learn Data Science with Sapalogy

1.Introduction

What is AI & ML?
AI & ML course
AI & ML certification
AI & ML jobs in india
AI & ML jobs in nagpur

2.Foundational Skills

Learn programming languages such as Python or R.
Develop a solid understanding of mathematics and statistics.
Acquire proficiency in using essential tools and libraries like Jupyter Notebooks, NumPy, and Pandas.

3. Data Acquisition and Cleaning

Gain expertise in obtaining and collecting data from various sources.
Master techniques for cleaning and preprocessing data to ensure its quality and reliability.
Explore data storage solutions and databases.

4.Exploratory Data Analysis (EDA)

Learn exploratory data analysis techniques to understand the structure and patterns within the data.
Use data visualization tools like Matplotlib and Seaborn to create meaningful plots.
Develop the ability to ask relevant questions about the data and derive insights.

5. Machine Learning Fundamentals

Understand the basics of machine learning algorithms and their applications.
Learn how to train and evaluate models using popular frameworks like scikit-learn.
Explore supervised and unsupervised learning techniques.

6. Advanced Machine Learning and Deep Learning

Dive into more advanced machine learning topics such as ensemble methods and hyperparameter tuning.
Familiarize yourself with deep learning concepts and frameworks like TensorFlow or PyTorch.
Experiment with neural networks and understand their architectures.

7. Model Deployment and Communication

Learn how to deploy machine learning models to production environments.
Develop skills in model interpretation and explainability.
Practice effective communication of data-driven insights to both technical and non-technical stakeholders.

8. Resume preparation

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How to prepare resume
How to prepare resume for freshers.
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Key features of AI & ML Training

165+ Hours course duration

100% Job oriented training

Industry expert faculties

Free demo class available

Completed 100+ batches

Certification guidance

Data Science syllabus

FOUNDATION

Introduction to Python
Python Basics
Python Functions and Packages
Working with Data Structures, Arrays,
Vectors & Data Frames
Google colab notebook
Pandas, NumPy, Matplotlib, Seaborn

Applied Statistics
Descriptive Statistics
Probability & Conditional Probability
Hypothesis Testing
Inferential Statistics
Probability Distributions

EDA and Data Processing
Data Types
Dispersion & Skewness
Uni & Multivariate Analysis
Data Imputation
Identifying and Normalizing Outlier

Supervised learning
Linear Regression
Multiple Variable Linear Regression
Logistic Regression
Naive Bayes Classifiers
k-NN Classification
Support Vector Machines

Unsupervised Learning
K-means Clustering
Hierarchical Clustering
Dimension Reduction-PCA

Decision Trees
Bagging
Random Forests
Boosting

Feature Engineering
Model Selection and Tuning
Model Performance Measures
Regularising Linear Models
MI Pipeline
Bootstrap Sampling
Grid Search Cv
Randomized Search Cv
K Fold Cross-validation

Introduction to Perceptron & Neural Networks
Activation and Loss functions
Gradient Descent
Batch Normalization
TensorFlow & Keras for Neural Networks
Hyper Parameter Tuning

Introduction to Convolutional
Neural Networks
Introduction to Images
Convolution, Pooling, Padding &
its Mechanisms
Forward Propagation & Backpropagation
for CNNs
CNN architectures like AlexNet, VGGNet,
InceptionNet & ResNet
Transfer Learning
Object Detection
YOLO, R-CNN, SSD
Semantic Segmentation
U-Net
Face Recognition using Siamese Networks
Instance Segmentation

Introduction to NLP
Stop Words.
Tokenization
Stemming and Lemmatization
Bag of Words Model
Word Vectorizer
TF-IDF
POS Tagging
Named Entity Recognition
Introduction to Sequential data
RNNs and its Mechanisms
Vanishing & Exploding gradients in RNNs
LSTMs – Long short-term memory
GRUs – Gated Recurrent Unit
LSTMs Applications
Time Series Analysis
LSTMs with Attention Mechanism
Neural Machine Translation
Advanced Language Models:
Transformers, BERT, XLNet

Introduction To DBMS
ER Diagram
Schema Design
Key Constraints and Basics of Normalization.
Joins
Subqueries Involving Joins and Aggregations
Sorting
Independent Subqueries
Correlated Subqueries
Analytic Functions.
Set Operations
Grouping and Filtering

Introduction of Power BI and its component
Data preparation and transformation using Power Query
Creating and customizing visualization
Building interactive report and dashboard
Power BI DAX(Data Analysis Expression)
Integration of Power BI with database

Overview of ChatGPT and OpenAl
Timeline of NLP and Generative Al
Frameworks for Understanding ChatGPTand Generative Al
Implications for Work, Business, andEducation
Output Modalities and Limitations
Business Roles to Leverage ChatGPT
Prompt Engineering for Fine-TuningOutputs
Practical Demonstration and BonusSection on RLHF
Introduction to Generative Al
Al vs ML vs DL vs GenAl
Supervised vs Unsupervised Learning.
Discriminative vs Generative Al
A Brief Timeline of GenAl
Basics of Generative Models
Large Language Models
Word Vectors
ChatGPT: The Development Stack
Attention Mechanism
Business Applications of ML, DL and GenAl
Hands-on Bing Images and ChatGPT

BASIC PROJECTS ON FOUNDATION

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Skills covered by Sapalogy

Upcoming Batch

Sapalogy provides flexible timings to all our students. Here is the Data Science Training Class Schedule in our branches. If this schedule doesn’t match please let us know. We will try to arrange appropriate timings based on your flexible timings.

Course	New batch	Offline	Online	Enquire now

Data science	Starts every week	Nagpur	India	Enquire now
Data analytics	Starts every week	Nagpur	India	Enquire now
Python	Starts every week	Nagpur	India	Enquire now

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Data Science certification

Sapalogy training certification will serve as proof that the courses were completed by Sapalogy.
The Data science certification offered by Sapalogy will equip you with valuable skills, enhancing your competitiveness in the job market.
Sapalogy provides comprehensive guidance for your Data Science global certification, ensuring a 100% passing guarantee in examinations such as Data science Certification, Data science Platform Development Certification, and various other global exams.

Projects

Our Data Science student’s review

Our course review

Our Students Working At

Our students have secured positions at numerous organizations. Here are a few where they are leading with an impact !

Frequently asked question

Data science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract insights and knowledge from structured and unstructured data.

To become a data scientist, acquire a strong foundation in statistics, programming (e.g., Python or R), and machine learning. Gain practical experience through projects and consider obtaining relevant education or certifications.

Key skills include programming, statistical analysis, machine learning, data visualization, domain knowledge, and communication skills.

Python and R are widely used programming languages in data science.

Machine learning is a broader concept involving algorithms that can learn patterns from data, while deep learning is a subset of machine learning using neural networks with many layers.

Clean and preprocess data by handling missing values, removing duplicates, scaling features, and transforming variables as needed.

CRISP-DM (Cross-Industry Standard Process for Data Mining) is a extensively used version that outlines the steps in the records mining approach, which encompass commercial organization knowledge, records know-how, facts training, modeling, evaluation, and deployment.

Popular machine learning algorithms include linear regression, decision trees, random forests, support vector machines, k-nearest neighbors, and neural networks.