Mastering in Data Analytics Course in Kakinada

Job Oriented Training

Explore the realm of Data Analytics with 3RI Technologies! Unlock valuable insights, drive informed decisions, and advance your career through our extensive Data Analytics training. Our courses cater to all levels, ensuring success in this ever-evolving field. Take the first step towards your journey as a proficient Data Analyst in Kakinada by enrolling today.

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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 Kakinada

Overview

Explore the Depths of Data with 3RI’s Dynamic Analytics Program

Join 3RI Technologies for an advanced Data Analytics training program meticulously crafted to empower individuals with the essential skills and deep understanding needed to thrive in the fast-paced realm of Data Analytics.

 

Our meticulously designed curriculum seamlessly integrates theoretical principles with practical applications, delivering participants a strong foundation in Data Analytics methods and tools. Through immersive projects and real-world case studies, gain valuable insights into how Data Analytics drives business success and innovation, even in Kakinada.

 

Key Learning Areas:

 

Data Collection and Cleansing: Master techniques for gathering, preparing, and structuring raw data.

Statistical Analysis and Modeling: Learn hypothesis testing, regression analysis, and forecasting.

Data Visualization: Communicate insights effectively through compelling charts and dashboards.

Discover Big Data Technologies: Delve into tools like Hadoop, Spark, and Python libraries.

Through engaging projects and real-world case studies, you’ll acquire hands-on experience and understand how Data Analytics fuels business success across industries. Analyze customer behavior, optimize marketing strategies, predict market trends, and guide strategic decisions with confidence.

 Empower your career and organization with insights fueled by data.. Our comprehensive training, suitable for beginners and experienced professionals alike, prepares you to excel in the high-demand field of data analytics. Join us and embark on a transformative journey to become a skilled Data Analyst, prepared for the challenges of the digital age.

What is Data Analytics?

Data Analytics is a sophisticated process that involves meticulously analyzing large datasets to uncover intricate patterns and insights crucial for strategic decision-making. At 3RI Technologies, our Data Analysts skillfully use statistical and computational techniques to convert raw data into actionable intelligence. This empowers organizations to enhance processes, discover opportunities, and manage risks more effectively. 

Unraveling the Potential of Data Analysis: Transitioning from Big Data to Actionable Insights.

Data Analytics entails extracting hidden patterns and actionable insights from extensive datasets. Through the adept use of statistical and computational techniques, Data Analysts at 3RI Technologies unlock the full potential of raw data, transforming it into valuable intelligence that guides informed decision-making. Join us to master the art and science of Data Analytics and empower yourself to optimize processes, identify opportunities, mitigate risks, and drive data-driven decisions within your organization.

Why Learn Data Analytics Training?

In the fast-paced digital age, Data is generated at an unprecedented rate across various industries. Forward-thinking organizations recognize the pivotal role of Data Analytics in gaining a competitive edge, enhancing operational efficiency, and fostering innovation.

 

At 3RI Technologies, our Data Analytics training equips individuals with the diverse skills and nuanced expertise necessary to harness the transformative potential of data, even in Guntur. Whether aspiring to become proficient Data Analysts, Business Intelligence professionals, or visionary Data Scientists, our meticulously curated program lays the groundwork for success in these pivotal roles and beyond.

 

Shape Your Future, Shape Your Industry: Gain a Competitive Edge with Data Expertise

 

In today’s data-driven landscape, organizations worldwide value Data Analytics for its profound impact. By enrolling in our training program, you’ll acquire the skills to leverage data effectively, positioning yourself for success in this thriving field. Whether pursuing a career as a Data Analyst, Business Intelligence expert, or Data Scientist, our program provides the essential foundation for excellence and advancement.

 

Learning Data Analytics in Kakinada with 3RI Technologies

Experience the journey to Data Mastery with us at 3RI Technologies in Kakinada. Our advanced facilities, expert instructors, and hands-on approach guarantee top-tier education and training. Dive into the depths of Data Analytics with us for an enlightening learning adventure.

 

At 3RI Technologies in Kakinada, we offer highly flexible scheduling and personalized guidance to meet the diverse needs of learners. Whether you’re a seasoned professional or a recent graduate, we provide the resources and support necessary to excel in Data Analytics. Whether you seek to enhance your skills, acquire new ones, or pivot into a new career path, we’re dedicated to supporting you in achieving your goals with precision and expertise.

 

Join 3RI Technologies and embark on a tailored learning journey designed to meet your individual needs and aspirations. Our dedication to excellence shines through in our cutting-edge facilities, knowledgeable instructors, and practical learning methods, guaranteeing you receive outstanding education and training. We understand the importance of flexibility and personalization, offering adaptable schedules and customized guidance to accommodate a wide range of learners, from experienced professionals to fresh graduates. Whether you’re aiming to enhance your skills, pivot your career, or explore new opportunities in Data Analytics, 3RI Technologies equips you with the resources and assistance necessary to accomplish your objectives.

 

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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.

25-May-24 | SAT-SUN 8:00 AM to 10:00 AM

10-June-24 | MON-FRI 8:00 AM to 10:00 AM

29-June-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 career paths can I pursue after completing a Data Analytics course?

After  completing a Data Analytics course, you can explore career paths such as Data Analyst, business analyst, or data scientist. These roles are in demand across industries such as finance, healthcare, and marketing, offering competitive salaries and ample growth prospects.

2. Are there any specific software tools or programming languages used in the Data Analytics classes?

Yes, Data Analytics classes often use software tools like Excel, Tableau, and SQL for Data Analysis and visualization, along with programming languages like Python and R for statistical analysis and data manipulation.

3. Are there Data Analytics courses available for individuals with no experience in programming or statistics?

 You can learn Data Analytics without a background in programming or statistics. These courses are designed to start with the basics, making them suitable for beginners with diverse backgrounds. With dedication and the appropriate resources, you can establish a strong foundation in Data Analytics.

 

4. Is there a certification provided after finishing the Data Analytics course in Kakinada?

Yes, you will receive a certificate. After completing the Data Analytics course at 3RI Technologies in Kakinada, which will be valuable for your career advancement and job opportunities.

5. Is it possible to take a part-time Data Analytics Training course while working or studying?

Yes, you can take a Data Analytics Training part-time while working or studying at 3RI Technologies in Kakinada. They offer flexible schedules tailored to accommodate your other commitments.

6. Is there any post-course career support or assistance with job placement offered?

At 3RI Technologies, we provide comprehensive career support and job placement assistance to help you secure opportunities in the field of Data Analytics. Our training programs are designed to include resume building, interview preparation, and access to job postings, ensuring you are well-equipped for a successful career in Data Analytics.

7. May I attend a demo or trial class prior to enrolling in the course?

Yes, We offer demo or trial classes to prospective students, allowing you to experience the quality of our instruction and course content before enrolling. It’s a great opportunity to get a feel for the learning environment and see how our courses can benefit you.

8. Can I take a Data Analytics course online, or is it only available offline?

Yes, you can take a Data Analytics course online through 3RI Technologies in Kakinada. Our online courses offer the flexibility to learn at your own place from any location. You’ll have access to live instructors and interactive learning resources to enhance your learning experience.

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