Yuva Sakthi Academy data science Course is a good option if you are looking for a Data Science Course that keeps students up to date on the latest data science trends while also providing practical expertise.The data science course offered here is for individuals serious about pursuing a career in data science.

Enroll in a structured and approved data science course today. Data science professionals will teach students everything they need to know to succeed in the field – from fundamentals to advanced skills – and will receive a professional certificate upon completion.

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About Data Science Training

Data Science Training will help you to become an expert in Probability and Statistics, Excel, SQL, Tableau, R and Python with 10+ Real Life Projects. This Data Science Course will give you expertise on Machine Learning and Big data analysis for Solving Complex Challenges with Hands-on Classes. Gain the Critical Skills Such as Data Visualization, Linear Algebra, Multivariable Calculus, Python libraries and more from this Data Science Training Courses. Develop Your Mathematical & Programming Skills to become Professional Certified Data Scientist with our Data Science Courses.

Available Data Science s

Eligibility for Data Science Course

Eligibility for a Data Science course typically requires candidates to hold a bachelor’s degree in fields like Computer Science, Mathematics, Statistics, Engineering, or related disciplines. Proficiency in programming languages such as Python, R, or SQL is often expected, along with a solid foundation in mathematics and statistics encompassing topics like linear algebra, calculus, and probability. While prior experience in data analysis or machine learning is advantageous, it may not always be mandatory. Strong analytical skills, problem-solving abilities, and familiarity with data manipulation and visualization techniques are also essential. It’s advisable to check specific prerequisites outlined by the institution offering the course to ensure readiness for enrollment in a data science program.

Training Option: The Data Science Training is available in Online Live Instructor-Led Classes and Classroom Training Classes in Our Branches – Saravanampatti

Key Deliverables from Data Science –Yuva Sakthi Academy

Yuva Sakthi Academy is committed to equipping Data Science course participants with the essential skills to achieve their career aspirations. Here are some key highlights from our Data Science training:

  • Comprehensive understanding of Amazon Web Services, Security Pricing, Data Science Concepts, Architecture, and Support.
  • Access to learning resources designed to cultivate advanced Data Science skills through our Masters Program.
  • Hands-on experience in Data Science computing during the course, enhancing practical skills.
  • Ability to optimize, automate, and design efficient Data Science setups.
  • Preparation for lucrative internship and job opportunities facilitated by our industry-aligned curriculum.
  • Becoming a sought-after professional in the global Data Science workforce, leveraging newly acquired skills and training.
  • Effortless deployment of applications across multiple global regions using the Data Science platform.

Why Should You Attend Data Science with yuva sakthi academy?

Here are the reasons why Yuva Sakthi Academy is an ideal choice for a Data Science Masters Program:

  • Mock interviews are conducted to help students prepare for challenging interviews and Data Science exams, offering access to the latest interview questions, answers, and study materials.
  • Students engage in projects that enhance their skills in Data Science architecture, services, and tools like WAF, fostering practical learning.
  • Expert instructors provide 100% practical Data Science Online Training, focusing on various Amazon Web Services concepts.
  • Dedicated batches cater to both freshers and experienced candidates, ensuring personalized learning experiences.
  • The institute stays updated with industry advancements, continuously adapting teaching methods to ensure student success in their careers.
  • We employ top professors and certified specialists in Data Science Masters Program, known for guiding students towards successful career launches.
  • Affordable Data Science Masters Program course fees and flexible course schedules accommodate all students, ensuring no missed lessons.
  • Yuva Sakthi Academy, renowned as the best Data Science Institute, pledges to enhance career prospects through superior classroom and online training.
  • Why Should You Hold Off? Enroll in a 100% Placement Assurance Data Science Course to jumpstart your career.

    Hands-on Data Science

    • Yuva Sakthi Academy offers flexible Data Science training options to suit your learning preferences, including online and classroom sessions led by accredited instructors, ensuring a hands-on learning experience.
    • Free demo classes are available to help you explore the course content and teaching style.
    • For more information, contact us at +91-9597-684-055 or email yuvasakthiacademycbe@gmail.com.

    Data Science

    • Data Science Certified Professionals gain a competitive edge with globally recognized course completion certificates, significantly enhancing job prospects.
    • Maximize your skills and knowledge.
    • Become certified, demonstrate your expertise, and apply what you've learned, establishing credibility in your professional environment.

    100% Placements and Job Support

    • Yuva Sakthi Academy's placement support ensures regular interview opportunities with leading MNCs such as TCS, Wipro, CTS, Google, Amazon, and Flipkart.
    • Learn effective interview techniques tailored to align your qualifications with company requirements.

    Data Science and Course Content

    Data Science Data Science Practitioner Essentials
    This updated digital course is for individuals who want to develop a fundamental understanding of the Data Science Masters Program Data Science, independent of any specific technical role. You’ll learn about Data Science Masters Program Data Science concepts, core Data Science services, security, architecture, pricing, and support to build your Data Science Data Science knowledge. This course will also help you prepare for the Data Science Certified Data Science Practitioner exam.
    Data Science Technical Essentials
    In this introductory course, you will learn about Data Science products, services, and common solutions. You will learn the fundamentals of identifying Data Science services so that you can make informed decisions about IT solutions based on your business requirements.
    Architecting and Advanced Architecting on Data Science Masters Program

    Architecting on Data Science

    Through a series of use case scenarios and practical learning, you’ll learn to identify services and features to build resilient, secure, and highly available IT solutions in the Data Science Data Science. Expert Data Science Masters Program Instructors emphasize best practices using the Data Science Well-Architected Framework and guide you through the process of designing optimal IT solutions, based on real-life scenarios. At the end of the course, you’ll practice building a solution and apply what you’ve learned with confidence.

    Advanced Architecting on Data Science

    Through a series of use case scenarios and practical learning, you’ll explore Data Science services that can be applied to solve architectural problems. You’ll find opportunities to combine your new knowledge with critical thinking and problem-solving skills. This course culminates in a final review project to reinforce what you’ve learned throughout the three days.

    Developing and Advanced Developing on Data Science Masters Program

    Developing on Data Science

    In this course, you will learn how to use the Data Science SDK to develop secure and scalable Data Science applications. We will explore how to interact with Data Science Masters Program using code and discuss key concepts, best practices, and troubleshooting tips.

    Advanced Developing on Data Science

    This course covers advanced development topics such as architecting for a Data Science-native environment and deconstructing on-premises legacy applications and repackaging them into Data Science-based, Data Science-native architectures. It also covers how to apply the tenets of the Twelve-Factor Application methodology.

    Systems Operations on Data Science Masters Program
    This course teaches systems operators and anyone performing system operations functions how to install, configure, automate, monitor, secure, maintain, and troubleshoot the services, networks, and systems on Data Science necessary to support business applications. The course also covers specific Data Science Masters Program features, tools, and best practices related to these functions.
    DevOps Engineering on Data Science Masters Program
    DevOps Engineering on Data Science Masters Program teaches you how to use the combination of DevOps cultural philosophies, practices, and tools to increase your organization’s ability to develop, deliver, and maintain applications and services at high velocity on Data Science. This course covers continuous integration (CI), continuous delivery (CD), infrastructure as code, microservices, monitoring and logging, and communication and collaboration. Hands-on labs give you experience building and deploying Data Science Data ScienceFormation templates and CI/CD pipelines that build and deploy applications on Amazon Elastic Compute Data Science (Amazon EC2), serverless applications, and container-based applications. Labs for multi-pipeline workflows and pipelines that deploy to multiple environments are also included.

Upcoming Training Batches

Yuva Sakthi Academy provides flexible timings to all our students. Here is the Data Science Masters Program Training Course 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.

Time Days Batch Type Duration (Per Session)
8:00AM - 12:00PM Mon - Sat Weekdays Batch 4Hr - 5:30Hrs
12:00PM - 5:00PM Mon - Sat Weekdays Batch 4Hr - 5:30Hrs
5:00PM - 9:00PM Mon - Sat Weekdays Batch 4Hr - 5:30Hrs

Data Science Masters Program Syllabus

Data Science with Python

Module 1: Introduction to Data Science

  • What is Data Science?
  • What is Machine Learning?
  • What is Deep Learning?
  • What is AI?
  • Data Analytics & its types

Module 2: Introduction to Python

  • What is Python?
  • Why Python?
  • Installing Python
  • Python IDEs
Hands-on-Exercise:
  • Installing Python idle for windows,Linux and
  • Creating “Hello World” code

Module 3: Python Basics

  • Introduction to Python
  • Basic Concepts
  • Conditional Statements
  • Control Statements
  • List, Tuples, Dictionary, Set
  • String Functions
  • Mathematical Functions
  • User defined Functions
  • Class and Objects
  • Constructor
  • Inheritance
  • Package
  • Exception handling
  • Lambda Functions
  • File Concepts
  • Date and Regex Function
  • Tkinter Entry
  • Tkinter Checkbox
  • Tkinter Dropdown list
  • Tkinter Radio Button
  • Tkinter Label
  • Tkinter Button
  • Tkinter Menu
  • Tkinter Frame
  • Tkinter MessageBox
  • Tkinter Scroll bar
  • MySql and Sqlite 3 Connection
Hands-on-Exercise-Constructing Operators
  • Practice and Quickly learn Python necessary skills by solving simple questions and problems.
  • how Python uses indentation to structure a program, and how to avoid some common indentation errors.
  • You executed to make simple numerical lists, as well as a few operations you can perform on numerical lists, tuple, dictionary and set

Module 4: Python Packages, Database, Operating System & Tools

  • Pandas
  • Numpy
  • Sci-kit Learn
  • Mat-plot library
  • Seaborn
  • Pillow
  • OpenCV
Hands-on-Exercise:
  • Installing IDLE for windows, Linux and Mac
  • Installing numpy, pandas and matplotlib

Database

  • MySQL
  • MongoDB

Operating System

  • Linux

Tools

  • Power BI

Module 5: Importing Data

  • Reading CSV files
  • Saving in Python data
  • Loading Python data objects
  • Writing data to CSV file
Hands-on-Exercise:
  • To generate data sets and create visualizations of that data. You learned to create simple plots with matplotlib, and you saw how to use a scatter plot to explore random
  • You learned to create a histogram with Pygal and how to use a histogram to explore the results of rolling dice of different
  • Generating your own data sets with code is an interesting and powerful way to model and explore a wide variety of real-world
  • As you continue to work through the data visualization projects that follow, keep an eye out for situations you might be able to model with

Module 6: Manipulating Data

  • Selecting rows/observations
  • Rounding Number
  • Selecting columns/fields
  • Merging data
  • Data aggregation
  • Data munging techniques
Hands-on-Exercise:
  • As you gain experience with CSV and JSON files, you’ll be able to process almost any data you want to analyze.
  • Most online data sets can be downloaded in either or both of these From working with these formats, you’ll be able to learn other data formats as well.

Module 7: Statistics Basics

  • Central Tendency
    • Mean
    • Median
    • Mode
    • Skewness
    • Normal Distribution
  • Probability Basics
    • What does it mean by probability?
    • Types of Probability
    • ODDS Ratio?
  • Standard Deviation
    • Data deviation & distribution
    • Variance
  • Bias variance Tradeoff
    • Underfitting
    • Overfitting
  • Distance metrics
    • Euclidean Distance
    • Manhattan Distance
  • Outlier analysis
    • What is an Outlier?
    • Inter Quartile Range
    • Box & whisker plot
    • Upper Whisker
    • Lower Whisker
    • Scatter plot
    • Cook’s Distance
  • Missing Value treatment
    • What is NA?
    • Central Imputation
    • KNN imputation
    • Dummification
  • Correlation
    • Pearson correlation
    • positive & Negative correlation
Hands-on-Exercise:
  • Compute probability in a situation where there are equally-likely outcomes
  • Apply concepts to cards and dice
  • Compute the probability of two independent events both occurring
  • Compute the probability of either of two independent events occurring
  • Do problems that involve conditional probabilities
  • Calculate the probability of two independent events occurring
  • List all permutations and combinations
  • Apply formulas for permutations and combinations

Module 8: Error Metrics

  • Classification
    • Confusion Matrix
    • Precision
    • Recall
    • Specificity
    • F1 Score
  • Regression
    • MSE
    • RMSE
    • MAPE
Hands-on-Exercise:
  • State why the z’ transformation is necessary
  • Compute the standard error of z
  • Compute a confidence interval on ρ The computation of a confidence interval
  • Estimate the population proportion from sample proportions
  • Apply the correction for continuity

Machine Learning

Supervised Learning

  • Linear Regression
    • Linear Equation
    • Slope
    • Intercept
    • R square value
  • Logistic regression
    • ODDS ratio
    • Probability of success
    • Probability of failure Bias Variance Tradeoff
    • ROC curve
    • Bias Variance Tradeoff
Hands-on-Exercise:
  • we’ve reviewed the main ways to approach the problem of modeling data using simple and definite

Unsupervised Learning

  • K-Means
  • K-Means ++
  • Hierarchical Clustering

SVM

  • Support Vectors
  • Hyperplanes
  • 2-D Case
  • Linear Hyperplane

SVM Kernal

  • Linear
  • Radial
  • polynomial

Other Machine Learning algorithms

  • K – Nearest Neighbour
  • Naïve Bayes Classifier
  • Decision Tree – CART
  • Decision Tree – C50
  • Random Forest
Hands-on-Exercise:
  • We have covered the simplest but still very practical machine learning models in an eminently practical way to get us started on the complexity
  • where we will cover several regression techniques, it will be time to go and solve a new type of problem that we have not worked on, even if it’s possible to solve the problem with clustering methods (regression), using new mathematical tools for approximating unknown values.
  • In it, we will model past data using mathematical functions, and try to model new output based on those modeling

Artificial Intelligence

Module 1: AI Introduction

  • Perceptron
  • Multi-Layer perceptron
  • Markov Decision Process
  • Logical Agent & First Order Logic
  • AL Applications

Deep Learning

Module 1: Deep Learning Algorithms

  • CNN – Convolutional Neural Network
  • RNN – Recurrent Neural Network
  • ANN – Artificial Neural Network
Hands-on-Exercise:
  • We took a very important step towards solving complex problems together by means of implementing our first neural
  • Now, the following architectures will have familiar elements, and we will be able to extrapolate the knowledge acquired on this chapter, to novel

Introduction to NLP

  • Text Pre-processing
  • Noise Removal
  • Lexicon Normalization
  • Lemmatization
  • Stemming
  • Object Standardization

Text to Features (Feature Engineering)

  • Syntactical Parsing
  • Dependency Grammar
  • Part of Speech Tagging
  • Entity Parsing
  • Named Entity Recognition
  • Topic Modelling
  • N-Grams
  • TF – IDF
  • Frequency / Density Features
  • Word Embedding’s

Tasks of NLP

  • Text Classification
  • Text Matching
  • Levenshtein Distance
  • Phonetic Matching
  • Flexible String Matching
Hands-on-Exercise:
  • provided, you will even be able to create new customized
  • As our models won’t be enough to solve very complex problems, in the following chapter, our scope will expand even more, adding the important dimension of time to the set of elements included in our generalization.

Project Works

Project 1: Board Game Review Prediction

  • To perform a Linear regression
  • Analysis by predicting the average reviews in a board game

Project 2 :Credit Card Fraud Detection

  • TO focus on Anomaly Detection by using probability densities to detect credit card fraud

Project 3: Stock Market Clustering

  • Learn how to use the K-means clustering
  • To find related companies by finding correlations among stock market movements over a given time span

Project 4: Getting Started with Natural Language Processing

  • will focus on Natural Language Processing (NLP) methodology, such as tokenizing words
  • and sentences, part of speech identification and tagging, and phrase

Project 5: Obtaining Near State-of-the-Art Performance on Object Recognition

  • Using Deep Learning – In this project, will use the CIFAR-10 object recognition dataset as a
  • benchmark to implement a recently published deep neural

Project 6: Image Super Resolution with the SRCNN – Learn how to implement & use

  • Tensorflow version of the Super Resolution Convolutional Neural Network (SRCNN) for
  • improving image

Project 7: Natural Language Processing: Text Classification

  • an advanced approach to Natural Language Processing by solving a text classification task
  • using multiple classification

Project 8: K-Means Clustering For Image Analysis

  • use K-Means clustering in an unsupervised learning method to analyze and classify 28 x 28 pixel images from the MNIST

Project 9:Data Compression & Visualization Using Principal Component Analysis

  • This project will show you how to compress our Iris dataset into a 2D feature set and how to visualize it through a normal x-y plot using k-means clustering

Tableau

Module 1: Tableau Course Material

  • Start Page
  • Show Me
  • Connecting to Excel Files
  • Connecting to Text Files
  • Connect to Microsoft SQL Server
  • Connecting to Microsoft Analysis Services
  • Creating and Removing Hierarchies
  • Bins
  • Joining Tables
  • Data Blending

Module 2: Learn Tableau Basic Reports

  • arameters
  • Grouping Example 1
  • Grouping Example 2
  • Edit Groups
  • Set
  • Combined Sets
  • Creating a First Report
  • Data Labels
  • Create Folders
  • Sorting Data
  • Add Totals, Subtotals and Grand Totals to Report

Hands-on-Exercise:

  • Install Tableau Desktop
  • Connect Tableau to various Datasets: Excel and CSV files

Module 3: Learn Tableau Charts

  • Area Chart
  • Bar Chart
  • Box Plot
  • Bubble Chart
  • Bump Chart
  • Bullet Graph
  • Circle Views
  • Dual Combination Chart
  • Dual Lines Chart
  • Funnel Chart
  • Traditional Funnel Charts
  • Gantt Chart
  • Grouped Bar or Side by Side Bars Chart
  • Heatmap
  • Highlight Table
  • Histogram
  • Cumulative Histogram
  • Line Chart
  • Lollipop Chart
  • Pareto Chart
  • Pie Chart
  • Scatter Plot
  • Stacked Bar Chart
  • Text Label
  • Tree Map
  • Word Cloud
  • Waterfall Chart
Hands-on-Exercise:
  • Create and use Static Sets
  • Create and use Dynamic Sets
  • Combine Sets into more Sets
  • Use Sets as filters
  • Create Sets via Formulas
  • Control Sets with Parameters
  • Control Reference Lines with Parameters

Module 4: Learn Tableau Advanced Reports

  • Dual Axis Reports
  • Blended Axis
  • Individual Axis
  • Add Reference Lines
  • Reference Bands
  • Reference Distributions
  • Basic Maps
  • Symbol Map
  • Use Google Maps
  • Mapbox Maps as a Background Map
  • WMS Server Map as a Background Map
Hands-on-Exercise:
  • Create Barcharts
  • Create Area Charts
  • Create Maps
  • Create Interactive Dashboards
  • Create Storylines
  • Understand Types of Joins and how they work
  • Work with Data Blending in Tableau
  • Create Table Calculations
  • Work with Parameters
  • Create Dual Axis Charts
  • Create Calculated Fields

Module 5: Learn Tableau Calculations & Filters

  • Calculated Fields
  • Basic Approach to Calculate Rank
  • Advanced Approach to Calculate Ra
  • Calculating Running Total
  • Filters Introduction
  • Quick Filters
  • Filters on Dimensions
  • Conditional Filters
  • Top and Bottom Filters
  • Filters on Measures
  • Context Filters
  • Slicing Fliters
  • Data Source Filters
  • Extract Filters
Hands-on-Exercise:
  • Creating Data Extracts in Tableau
  • Understand Aggregation, Granularity, and Level of Detail
  • Adding Filters and Quick Filters

Module 6: Learn Tableau Dashboards

  • Create a Dashboard
  • Format Dashboard Layout
  • Create a Device Preview of a Dashboard
  • Create Filters on Dashboard
  • Dashboard Objects
  • Create a Story

Module 7: Server

  • Tableau online.
  • Overview of Tableau
  • Publishing Tableau objects and scheduling/subscription.
Hands-on-Exercise:
  • Create Data Hierarchies
  • Adding Actions to Dashboards (filters & highlighting)
  • Assigning Geographical Roles to Data Elements
  • Advanced Data Preparation

Trainer Profile of data science Course

Our Trainers provide complete freedom to the students, to explore the subject and learn based on real-time examples. Our trainers help the candidates in completing their projects and even prepare them for interview questions and answers. Candidates are free to ask any questions at any time.

  • Trained more than 2000+ students in a year.
  • Strong Theoretical & Practical Knowledge.
  • Certified Professionals with High Grade.
  • Expert level Subject Knowledge and fully up-to-date on real-world industry applications.
  • Trainers have Experienced on multiple real-time projects in their Industries.
  • Our Trainers are working in multinational companies such as CTS, TCS, HCL Technologies, ZOHO, Birlasoft, IBM, Microsoft, HP, Scope, Philips Technologies etc

data science Exams

Yuva Sakthi Academy is Accredited by all major Global Companies around the world. We provide after completion of the theoretical and practical sessions to fresher’s as well as corporate trainees.

Our at Yuva Sakthi Academy is accredited worldwide. It increases the value of your resume and you can attain leading job posts with the help of this in leading MNC’s of the world. The is only provided after successful completion of our training and practical based projects.

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Frequently Asked Questions

What role does Python play in Data Science?

Python is widely used in Data Science due to its simplicity, versatility, and extensive libraries. It serves as a powerful tool for data analysis, visualization, machine learning, and statistical modeling, making it essential for professionals in the field of Data Science.

Why should I learn Python for Data Science?

Learning Python for Data Science offers numerous advantages. Python's simplicity and readability make it accessible for beginners and experts alike. It provides powerful libraries like NumPy, Pandas, and Matplotlib, essential for data manipulation, analysis, and visualization.

What Python libraries are commonly used in Data Science?

Popular Python libraries in Data Science include NumPy for numerical computing, Pandas for data manipulation, Matplotlib and Seaborn for data visualization, Scikit-learn for machine learning, and TensorFlow or PyTorch for deep learning tasks.

Is Python suitable for both beginners and experienced professionals in Data Science?

Yes, Python is suitable for all levels of expertise in Data Science. Beginners find Python easy to learn due to its simple syntax and readability. Experienced professionals benefit from its scalability, extensive libraries, and community support for solving complex data science challenges.

How can Python be used for data analysis and manipulation in Data Science?

Python enables data analysis and manipulation through libraries like Pandas, which offer powerful data structures and tools for cleaning, transforming, and analyzing data. With Python, data scientists can perform exploratory data analysis (EDA) and derive meaningful insights from large datasets.

Does Python support machine learning and artificial intelligence (AI) in Data Science?

Yes, Python is extensively used for machine learning and AI applications in Data Science. Libraries such as Scikit-learn, TensorFlow, and PyTorch provide robust frameworks for developing and deploying machine learning models, neural networks, and deep learning algorithms.

What career opportunities are available for Python experts in Data Science?

Python experts in Data Science can pursue careers as Data Scientists, Machine Learning Engineers, AI Specialists, Data Analysts, and Research Scientists. Industries such as finance, healthcare, e-commerce, and technology offer lucrative job opportunities for professionals skilled in Python and Data Science.

How can Python enhance data visualization in Data Science?

Python libraries such as Matplotlib, Seaborn, and Plotly enable data scientists to create insightful visualizations from complex datasets. These libraries offer customizable charts, graphs, and interactive plots that help communicate data-driven insights effectively to stakeholders and decision-makers.

What are the advantages of learning Python for Data Science online?

Learning Python for Data Science online offers flexibility, allowing you to learn at your own pace from anywhere. Online courses provide access to expert instructors, real-world projects, and community forums for collaboration and networking with fellow learners and professionals.

How can I get started with learning Python for Data Science?

To get started with learning Python for Data Science, enroll in an online course or bootcamp that covers Python fundamentals, data analysis, and machine learning. Practice coding regularly, explore Python libraries, and work on projects to apply your skills and build a strong portfolio.

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Saravanampatti, Coimbatore – 641 035
Tamil Nadu, India.

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