Mastering in Data Analytics Course in Kurnool

Job Oriented Training

Dive into the world of Data Analytics with 3RI Technologies! Gain valuable insights, make informed decisions, and propel your career with our comprehensive training. Our courses are designed for all levels, ensuring success in this dynamic field. Enroll today to kickstart your journey as a skilled Data Analyst in Kurnool.

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Key Features

Course Duration : 5 Months

Live Projects : 4

Online Live Training

EMI Option Available

Certification & Job Assistance

24 x 7 Lifetime Support

Our Industry Expert Trainer

We are a team of 10+ Years of Industry Experienced Trainers, who conduct the training with real-time scenarios.
The Global Certified Trainers are Excellent in knowledge and highly professionals.
The Trainers follow the Project-Based Learning Method in the Interactive sessions.

Data Analyst Course in Kurnool

Overview

Embark on an Insightful Voyage: 3RI’s Dynamic Data Analytics Program.

 

Join 3RI Technologies on an enriching journey into the realm of Data Analytics with our engaging training program. Designed to equip individuals with essential skills and profound knowledge, our curriculum blends theory with hands-on applications for a comprehensive learning experience.

 

Explore theoretical frameworks alongside practical projects, gaining a robust foundation in Data Analytics methodologies and tools. Through real-world case studies, participants acquire invaluable insights into how Data Analytics fuels business innovation and success, transcending geographical boundaries like Kurnool.

Key Areas of Focus:

Data Collection and Preparation: Acquire skills to gather, clean, and organize raw data for analysis.

Statistical Analysis and Modeling Mastery: Learn advanced techniques such as hypothesis testing, regression, and forecasting.

Visual Communication of Insights: Learn to present findings through impactful charts, graphs, and dashboards.

Exploring Big Data Tools: Gain hands-on experience with top tools like Hadoop, Spark, and Python libraries.

Immerse Yourself in Real-World Data Analytics: Explore how data drives success in diverse industries through hands-on projects and case studies. Gain insights into customer behaviour, refine marketing strategies, forecast trends, and make strategic decisions confidently.

Empower Your Career with Data: Whether you’re new to Data Analysis or a seasoned professional, 3RI Technologies’ Data Analytics training program provides the skills and knowledge needed to excel. Join us and embark on a transformative journey to become a proficient Data Analyst, ready to tackle the challenges of the digital age.

What is Data Analytics?

Data Analytics is the process of examining large datasets to uncover valuable insights that inform better decision-making. At 3RI Technologies, our data analysts employ statistical and computational techniques to transform raw data into meaningful intelligence. This empowers our clients to optimize processes, capitalize on opportunities, mitigate risks, and drive success through data-driven decisions.

Our expertise spans from deriving insights from big data to guiding strategic business choices. We demystify analytics, collaborating with you to clarify objectives and ensure our analysis provides actionable insights tailored to your needs. This allows us to help you fully leverage the power of your data to gain a competitive edge.

Ultimately, our goal is to unlock the potential of your data to create tangible value. We turn numbers into nuanced qualitative insights that lead to quantitative business improvements. With robust analytics capabilities and a focus on practical application, we enable fact-based strategic planning and data-driven change.

Why Seek Data Analytics Training?

In today’s digital landscape, Data is being produced at an unprecedented pace across industries. Savvy organizations recognize the immense value of Data Analytics for gaining a competitive edge, driving efficiency, and enabling innovation.

By pursuing Data Analytics training with 3RI Technologies, individuals can acquire the multifaceted skills needed to harness data’s transformative potential and unlock abundant career opportunities in Kurnool and beyond. Our tailored curriculum provides the core competencies to succeed as a Data Analyst, business intelligence professional, or Data Scientist.

With our meticulously designed program, you will gain expertise in statistical modeling, computational techniques, and translating raw data into actionable insights. This equips you to help organizations leverage Data Analytics to optimize processes, capitalize on emerging opportunities, and gain a competitive advantage.

Whether you aim to become a data-driven decision-maker or lead analytics initiatives, our training will empower you to shape the future of your industry. We provide the essential skills and knowledge to advance your career and drive success in today’s data-centric landscape.

Learning Data Analytics in Pune with 3RI Technologies

At 3RI Technologies in Kurnool, we offer personalized pathways to help you master Data Analytics. Our cutting-edge facilities, experienced instructors, and hands-on training ensure participants receive high-quality education tailored to their needs and goals.

Whether you’re an established professional looking to upskill or a recent graduate eager to start a new career, our flexible schedules and customized guidance support your unique learning journey. We cater to diverse learners with varied backgrounds who want to gain expertise in Data Analytics.

By joining our transformative programs, you’ll have access to indispensable resources and unwavering support to achieve data mastery. We are committed to helping you gain proficiency in statistical modeling, computational techniques, and translating raw data into actionable insights.

Our student-centered approach focuses on providing real-world skills that equip you for success in the high-demand field of Data Analytics. Let us help you embark on a rewarding learning experience designed around your specific aspirations in this rapidly growing industry.

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Data Analytics Course Syllabus

Decade Years Legacy of Excellence | Multiple Cities | Manifold Campuses | Global Career Offers

  1. Fundamentals of Data Science and Machine Learning
  • Introduction to Data Science
  • Need of Data Science
  • BigData and Data Science’
  • Data Science and machine learning
  • Data Science Life Cycle
  • Data Science Platform
  • Data Science Use Cases
  • Skill Required for Data Science
  1. Mathematics For Data Science
  • Linear Algebra
    • Vectors
    • Matrices
  • Optimization
    • Theory Of optimization
    • Gradients Descent
  1. Introduction to Statistics
  • Descriptive vs. Inferential Statistics
  • Types of data
  • Measures of central tendency and dispersion
  • Hypothesis & inferences
  • Hypothesis Testing
  • Confidence Interval
  • Central Limit Theorem
  1. Probability and Probability Distributions
  • Probability Theory
  • Conditional Probability
  • Data Distribution
  • Distribution Functions
    • Normal Distribution
    • Binomial Distribution
  1. Using Spreadsheet
  • What is Excel?
  • Why Use Excel?
  • Excel Overview
  • Excel Ranges,Selection of Ranges
  • Excel Fill,Fill Copies,Fill Sequences,Sequence of Dates
  • Excel add,move,delete cells
  • Excel Formulas
  • Relative and Absolute References
  1. Functions
  • SUM
  • AVERAGE
  • COUNT
  • MAX & MIN
  • RANDBETWEEN
  • TRIM
  • LEN
  • CONCATENATE
  • TODAY & NOW
  1. Advanced Functions
  • Excel IF Function
  • Excel If Function with Calculations
  • How to use COUNT, COUNTIF, and COUNTIFS Function?
  • Excel Advanced If Functions
  1. Data Visualization
  • Excel Data Analysis – Data Visualization
  • Visualizing Data with Charts
  • Chart Elements and Chart Styles
  • Data Labels
  • Quick Layout

 

  • An Introduction to RDBMS & SQL
  • Data Retrieval with SQL
  • Pattern matching with wildcards
  • Basics of sorting
  • Order by clause
  • Aggregate functions
  • Group by clause
  • Having clause
  • Nested queries
  • Inner join
  • Multi join
  • Outer join
  • Adding and Deleting columns
  • Changing column name and Data Type
  • Creating Table from existing Table
  • Changing Constraints Foreign key.
  1. An Introduction to Python
  • Why Python , its Unique Feature and where to use it?
  • Python environment Setup/shell
  • Installing Anaconda
  • Understanding the Jupyter notebook
  • Python Identifiers, Keywords
  • Discussion about installed module s and packages
  1. Conditional Statement ,Loops and File Handling
  • Python Data Types and Variable
  • Condition and Loops in Python
  • Decorators
  • Python Modules & Packages
  • Python Files and Directories manipulations
  • Use various files and directory functions for OS operations
  1. Python Core Objects and Functions
  • Built in modules (Library Functions)
  • Numeric and Math’s Module
  • String/List/Dictionaries/Tuple
  • Complex Data structures in Python
  • Python built in function
  • Python user defined functions

4. Introduction to NumPy

  • Array Operations
  • Arrays Functions
  • Array Mathematics
  • Array Manipulation
  • Array I/O
  • Importing Files with Numpy

5. Data Manipulation with Pandas

  • Data Frames
  • I/O
  • Selection in DFs
  • Retrieving in DFs
  • Applying Functions
  • Reshaping the DFs – Pivot
  • Combining DFs
    Merge
    Join
  • Data Alignment 

6. SciPy

  • Matrices Operations
  • Create matrices
    Inverse, Transpose, Trace,   Norms , Rank etc
  • Matrices Decomposition
  • Eigen Values & vectors
  • SVDs

7.Visualization with Seaborn

    • Seaborn Installation
    • Introduction to Seaborn
    • Basics of Plotting
    • Plots Generation
    • Visualizing the Distribution of a Dataset
    • Selection color palettes  

8. Visualization with Matplotlib

  • Matplotlib Installation
  • Matplotlib Basic Plots & it’s Containers
  • Matplotlib components and properties
  • Pylab & Pyplot
  • Scatter plots
  • 2D Plots-
  • Histograms
  • Bar Graphs
  • Pie Charts
  • Box Plots
  • Customization
  • Store Plots

9. SciKit Learn

  • Basics
  • Data Loading
  • Train/Test Data generation
  • Preprocessing
  • Generate Model
  • Evaluate Models

10. Descriptive Statistics

  • Data understanding
  • Observations, variables, and data matrices
  • Types of variables
  • Measures of Central Tendency
  • Arithmetic Mean / Average
    • Merits & Demerits of Arithmetic Mean and Mode
    • Merits & Demerits of Mode and Median
    • Merits & Demerits of Median Variance

11. Probability Basics

  • Notation and Terminology
  • Unions and Intersections
  • Conditional Probability and Independence

12. Probability Distributions

  • Random Variable
  • Probability Distributions
  • Probability Mass Function
  • Parameters vs. Statistics
  • Binomial Distribution
  • Poisson Distribution
  • Normal Distribution
  • Standard Normal Distribution
  • Central Limit Theorem
  • Cumulative Distribution function

13.  Tests of Hypothesis

  • Large Sample Test
  • Small Sample Test
  • One Sample: Testing Population Mean
  • Hypothesis in One Sample z-test
  • Two Sample: Testing Population Mean
  • One Sample t-test – Two Sample t-test
  • Paired t-test
  • Hypothesis in Paired Samples t-test
  • Chi-Square test

14. Data Analysis

  • Case study- Netflix
  • Deep analysis on Netflix data
  1. Exploratory Data Analysis
  • Data Exploration
  • Missing Value handling
  • Outliers Handling
  • Feature Engineering
  1. Feature Selection
  • Importance of Feature Selection in Machine Learning
  • Filter Methods
  • Wrapper Methods
  • Embedded Methods
  1. Machine Learning: Supervised Algorithms Classification
  • Introduction to Machine Learning
  • Logistic Regression
  • Naïve Bays Algorithm
  • K-Nearest Neighbor Algorithm
  • Decision Tress
    1. SingleTree
    2. Random Forest
  • Support Vector Machines
  • Model Ensemble
  • Model Evaluation and performance
    • K-Fold Cross Validation
    • ROC, AUC etc…
  • Hyper parameter tuning
    • Regression
    • classification
  1. Machine Learning: Regression
  • Simple Linear Regression
  • Multiple Linear Regression
  • Decision Tree and Random Forest Regression
  1. Machine Learning: Unsupervised Learning Algorithms
  • Similarity Measures
  • Cluster Analysis and Similarity Measures
  1. Ensemble algorithms
  • Bagging
  • Boosting
  • Voting
  • Stacking
  • K-means Clustering
  • Hierarchical Clustering
  • Principal Components Analysis
  • Association Rules Mining & Market Basket Analysis

7. Recommendation Systems

  • collaborative filtering model
  • content-based filtering model.
  • Hybrid collaborative system.
  1. Artificial Intelligence
    • An Introduction to Artificial Intelligence
    • History of Artificial Intelligence
    • Future and Market Trends in Artificial Intelligence
    • Intelligent Agents – Perceive-Reason-Act Loop
    • Search and Symbolic Search
    • Constraint-based Reasoning
    • Simple Adversarial Search (Game-Playing)
    • Neural Networks and Perceptions
    • Understanding Feedforward Networks
    • Boltzmann Machines and Autoencoders
    • Exploring Backpropagation
  2. Deep Networks and Structured Knowledge
    • Understanding Sensor Processing
    • Natural Language Processing
    • Studying Neural Elements
    • Convolutional Networks
    • Recurrent Networks
    • Long Short-Term Memory (LSTM) Networks
  3. Natural Language Processing
    • Natural Language Processing
    • Natural Language Processing in Python
    • Studying Deep Learning
    • Artificial Neural Networks
    • ANN Intuition
    • Plan of Attack
    • Studying the Neuron
    • The Activation Function
    • Working of Neural Networks
    • Exploring Gradient Descent
    • Stochastic Gradient Descent
    • Exploring Backpropagation
  4. Artificial and Conventional Neural Network
    • Understanding Artificial Neural Network
    • Building an ANN
    • Building Problem Description
    • Evaluation the ANN
    • Improving the ANN
    • Tuning the ANN
  5. Image Processing / Machine Vision
  • Image basics
  • Loading and saving images
  • Thresholding
  • Bluring
  • Masking
  • Image Augmentation
  1. Conventional Neural Networks
  • CNN Intuition
  • Convolution Operation
  • ReLU Layer
  • Pooling and Flattening
  • Full Connection
  • Softmax and Cross-Entropy
  • Building a CNN
  • Evaluating the CNN
  • Improving the CNN
  • Tuning the CNN
  1. Recurrent Neural Network
  • Recurrent Neural Network
  • RNN Intuition
  • The Vanishing Gradient Problem
  • LSTMs and LSTM Variations
  • Practical Intuition
  • Building an RNN
  • Evaluating the RNN
  • Improving the RNN
  • Tuning the RNN
  1. Time Series Data
  • Introduction to Time series data
  • Data cleaning in time series
  • Pre-Processing Time series Data
  • Predictions in Time Series using ARIMA, Facebook Prophet models.
  1. Introduction to Git& Distributed Version Control
  2. Life Cycle
  3. Create clone & commit Operations
  4. Push & Update Operations
  5. Stash, Move, Rename & Delete Operations.

Machine Learning Features and Services

  • Using python in Cloud
  • How to access Machine Learning Services
  • Lab on accessing Machine learning services
  • Uploading Data
  • Preparation of Data
  • Applying Machine Learning Model
  • Deployment by Publishing Models using AWS or other cloud computing

1.Introduction  to Data Visualization and the Power of Tableau

  • Architecture of Tableau
  • Product Components
  • Working with Metadata and Data Blending
  • Data Connectors
  • Data Model
  • File Types
  • Dimensions & Measures
  • Data Source Filters
  • Creation of Sets

2.Scatter Plot

  •  Gantt Chart
  • Funnel Chart
  • Waterfall Chart
  • Working with Filters
  • Organizing Data and Visual Analytics
  • Working with Mapping
  • Working with Calculations and Expressions
  • Working with Parameters
  • Charts and Graphs
  • Dashboards and Stories
  • Machine Learning end to end Project blueprint
  • Case study on real data after each model.
  • Regression predictive modeling – E-commerce
  • Classification predictive modeling – Binary Classification
  • Case study on Binary Classification – Bank Marketing
  • Case study on Sales Forecasting and market analysis
  • Widespread coverage for each Topic
  • Various Approaches to Solve Data Science Problem
  • Pros and Cons of Various Algorithms and approaches
  • Amazon-Recommender
  • Image Classification
  • Sentiment Analysis

Project Domains: Finance

  • Insurance company wants to decide on the premium using various parameters of the client.
  • It’s an important problem to keep the clients and attract new ones.

By completing this project you will learn:

  • How to collect data?, how to justify right features? , Which ML / DL model is best in this situation? How much data is enough?
  • How to have CI/CD in the project?
  • How to do Deployment of Project to cloud?

Image Processing in Health care

  • A hospital wants to automate Detection of pneumonia in X-rays using image processing.

By doing this project you will understand

  • How to handle image data? How to preprocess and augment image data? How to choose right model for image process?
  • How to apply transfer learning in image processing?
  • How to do incremental learning & CI/CD in the project?
  • How to do Deployment of Project to cloud?

Natural Language Processing

  • One of the companies wants to automate applicant’s level in English communication.
  • Create a ML/DL model for this task.

By completing this project you will learn

  • How to do convert text to right representation? How to preprocess text data? How to select right ML/DL model for text data ?
  • How to do transfer learning in Text Analytics?
  • How to do CI/CD in text analytics project?
  • How to do Deployment of Project to cloud?

Mechanical

  • A mechanical company wants to perform predictive maintenance of engine parts.
  • This enables company to efficiently change parts before machine fails.

By performing this task you will learn,

  • How to handle time series data?
  • How to preprocess time series data?
  • How to create ML/DL model for Time series Data?
  • How to do CI/CD in text analytics project?
  • How to do Deployment of Project to cloud?

Sales / Demand Forecasting

  • Predict the sales / demand of a product of a company.
  • Sales / Demand forecasting of the product will help company efficiently manage the resources.
  • Create a ML/DL model for this problem.

By performing this project you will learn,

  • How to handle time series data?
  • How to preprocess time series data?
  • How to create ML/DL model for Time series Data?
  • How to do CI/CD in text analytics project?
  • How to do Deployment of Project to cloud?

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Industry Projects

Learn through real-life industry projects sponsored by top companies across industries

Dedicated Industry Experts Mentors

Receive 1:1 career counselling sessions & mock interviews with hiring managers. Further your career with our 300+ hiring partners.

Batch Schedule

Schedule Your Batch at your convenient time.

31-Mar-24 | SAT-SUN 8:00 AM to 10:00 AM

15-Apr-24 | MON-FRI 8:00 AM to 10:00 AM

28-Apr-24 | SAT-SUN 8:00 AM to 10:00 AM

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Will I Get Certified?

Upon successfully completing this program, you’ll earn a certificate.

The 3RI certification is accepted and respected by every significant multinational company across the nation. Fresh graduates and corporate trainees are eligible for the assistance. We offer certificate once the academic and practical courses have been finished. The certification that we offer here at 3RI is recognized across the country. The value of your resume will grow as a result. With the assistance of this qualification, you will be able to obtain prominent employment posts in the most successful multinational corporations in the country. The completion of our course as well as the projects that are based on practical application, are prerequisites for receiving the certificate.

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Frequently Asked Questions

1. What specific skills will you learn through the Data Analytics course?

Through the Data Analytics course, you will  learn skills such as data wrangling, statistical analysis, data visualization, and machine learning. These skills are essential for extracting insights and making data-driven decisions in the field.

2. What specific skills and knowledge will I gain from the Data Analytics training?

From the Data Analytics training, you will gain essential skills such as data collection, statistical analysis, and data visualization. Additionally, you’ll develop proficiency in tools like Hadoop and Spark, enabling you to excel in data-driven roles and tackle real-world challenges with confidence.

3. What should I do if I come across a query after completing this course?

After completing the course at 3RI Technologies, you can rely on our dedicated support team for any queries. We offer post-training assistance, ensuring you have the resources and guidance needed to address your questions and continue your learning journey successfully.

4. How useful are the course materials and resources provided by 3RI Technologies in Kurnool?

The course materials and resources provided by 3RI Technologies in Kurnool are exceptionally useful. They are comprehensive, well-structured, and support effective learning in Data analytics, enhancing the overall learning experience for students.

5 .Are there flexible learning options available, such as part-time or online classes?

Yes, we offer flexible learning options including part-time and online classes. This allows you to balance your studies with other commitments and learn at your own pace, making it convenient and accessible.

6. Is Data Analytics a challenging course to understand?

Data Analytics  can be challenging to understand due to its complexity, which includes programming, statistics, and data analysis. However, with proper guidance and dedication, individuals can grasp the concepts and excel in the field, making it a rewarding and fulfilling career path.

7. Is the Data Analytics course suitable for beginners?

Yes, The Data Analytics course is suitable for beginners as it covers fundamental concepts and gradually progresses to more advanced topics, providing a strong foundation for newcomers to the field.

8. What happens if I miss a session of this course?

Don’t worry if you miss a session, you can either attend the next class or use the recorded sessions to catch up on anything you missed.

9.How will my progress be assessed throughout the course?

Your progress will be assessed through a combination of assignments, quizzes, projects, and exams. Regular feedback from instructors will guide you, ensuring a thorough understanding of concepts and practical application of skills essential for success in Data Analytics.

10.Can I get a certification After completion of the course, and how will it benefit my career?

After completing the course, you will receive a certification recognized in the industry. This certification validates your expertise and enhances your resume, making you more competitive in the job market and opening up new career opportunities in Data Analytics.

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