Data Science Course in Hyderabad

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Master your data analysis skills with Data Science Certification Course

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Data Science Course in Hyderabad
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Course Features

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24/7 Lifetime Support

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Certification Based Curriculum

Flexible Schedule

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One-on-onedoubtclearing

One-on-one doubt clearing

Careerpathguidance

Career path guidance

About Data Science Course

Mindmajix offers a world-class data science course in Hyderabad for professionals who want to start a successful career in data science.  The trainees will get exposure to different aspects of data science like Python, R programming, data manipulation, data analysis, statistics, machine learning, and natural language processing(NLP). Our data science course in Hyderabad also provides an in-depth understanding of scientific computing, deep learning, artificial intelligence, Keras API, big data, and excel. It also makes you apply your data science skills in real-world applications like speech recognition, internet search, and fraud and risk identification. Join our data science course in Hyderabad to advance your career in a rapidly growing field. 

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Course Coverage

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Learn & practice Data Science Course Concepts

Demonstrate your proficiency in use cases & Lab Assignments
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Data Science Course Content

To provide students with the right skillset Mindmajix has specially designed this Data Science course content with certified experts to meet modern industry needs. It includes all the fundamentals of data science from basic to advanced with the best practices. Our Data Science course Syllabus is also in line with the certification syllabus which makes your certification preparation far easier. Go through the below course modules to get a clear understanding of Data Science.Read more
Topic-wise Content Distribution

Data Science Basics

In this module, you will learn the basics of data science and R programming, Importance of Data Science.

Topics covered in this section are: 

  • What is Data Science
  • Significance of Data Science in today’s world.
  • R Programming basics

Learning Outcomes: By the end of this module, you will get a fundamental idea about data science and R programming.
 

Python Fundamentals

This python fundamentals module discusses the python concepts required for a data scientist. 

Topics covered in this section are: 

  • Python Introduction
  • Indentations in Python
  • Python data types and operators
  • Python Functions

Learning Outcomes: By the end of this module, you will get the basic Python programming knowledge.
 

Data Structures and Data Manipulation

This module deals with the basic concepts of data structures and data visualization.

Topics covered in this section are: 

  • Data Structures Overview
  • Identifying the Data Structures
  • Allocating values to the Data Structures
  • Data Manipulation Significance
  • Dplyr Package and performing different data manipulation operations.

Learning Outcomes: Upon completing this module, you will be able to understand the significance of Data structures and Data manipulation in Data science.
 

Data visualization

This module discusses topics like Data visualization, types of graphs, Ggplot2 package, bar plots creation, Univariant, and Multivariant analysis.

Topics covered in this section are: 

  • Introduction to Data Visualisation
  • Various kinds of graphs, Graphics grammar
  • Ggplot2 package
  • Multivariant analysis by using geom_boxplot
  • Univariant analysis by using the histogram, barplot, multivariate distribution, and density plot.
  • Creating the bar plots for the categorical variables through geop_bar() and including the themes through the theme() layer.

Learning Outcomes: At the end of this module, you will be able to visualize the data through different graphs, Ggplot2 package. Also, you will get a real-time experience of bar plot creation, Univariant, and Multivariant analysis. 
 

Statistics

This Data Science online classroom training module deals with statistics concepts like Classification, Probability Types, Covariance, and Correlation. Along with this, you will learn how to analyze the given data set through Data Sampling, Hypothesis Test, and Binary Distribution.

Topics covered in this section are: 

  • Statistics Importance
  • Statistics classification, Statistical terminology.
  • Data types, Probability types, measures of speed, and central tendency.
  • Covariance and Correlation, Binary and Normal distribution
  •  Data Sampling, Confidence, and Significance levels.
  • Hypothesis Test and Parametric testing

Learning Outcomes: By the end of this module, you will gain practical knowledge of different statistical concepts like Probability types, Hypothesis test, Covariance. You will also be able to work with other statistics techniques like Correlation, Data sampling, Normal and Binary Distribution.
 

Introduction to Machine Learning

This Machine learning module discusses machine learning basics like Supervise learning, classification, linear regression, and ensemble learning techniques.

Topics covered in this section are: 

  • Machine Learning Fundamentals
  • Supervised Learning, Classification in Supervised Learning
  • Linear Regression and mathematical concepts related to linear regression
  • Classification Algorithms, Ensemble Learning techniques

Learning Outcomes: Upon completing this module, you will get a basic knowledge of machine learning, and you will be proficient in Supervised learning, Linear regression, and Ensemble learning. 
 

Logistic Regression

In this module, you will learn concepts like logistic regression basics, Bivariate and Multivariate Logistic regression, Poisson Regression. Also, it discusses developing logistic models and logistic regression applications.

Topics covered in this section are: 

  • Logistic Regression Introduction
  • Logistic vs Linear Regression, Poisson Regression
  • Bivariate Logistic Regression, math related to logistic regression
  • Multivariate Logistic Regression, Building Logistic Models
  • False and true positive rate, Real-time applications of Logistic Regression

Learning Outcomes: At the end of this module, you will get practical knowledge of Logistic regression, Linear Regression, Poisson Regression, and Logistic models.
 

Random Forest and Decision Trees

This module discusses topics like classification techniques, implementing random forest, Naive Bayes, Entropy, Information Gain, and Gini Index.

Topics covered in this section are: 

  • Classification Techniques. Decision Tree Induction Algorithm
  • Implementation of Random Forest in R
  • Differences between classification tree and regression tree
  • Naive Bayes, SVM
  • Entropy, Gini Index, Information Gain

Learning Outcomes: Upon completing this module, you will acquire an in-depth understanding of decision tree induction algorithms, implementing the random forest in the R programming. 
 

Unsupervised learning

This module provides a detailed overview of different clustering types, K-means clustering algorithm, K-means clustering concepts, and implementing historical clustering and PCA in R programming.

Topics covered in this section are: 

  • Clustering, K-means clustering, Canopy Clustering, and Hierarchical Clustering
  • Unsupervised learning, Clustering algorithm, K-means clustering algorithm
  • K-means theoretical concepts, k-means process flow, and K-means implementation.
  • Implementing Historical Clustering in R
  • PCA(Principal Component Analysis) Implementation in R

Learning Outcomes: Upon completing this module, you will get a real-time experience of k-means clustering, clustering algorithm, and Principal Component Analysis.
 

Natural Language Processing

This data science online training module will help you master natural language processing,  text mining, and NPL working with text mining.

Topics covered in this section are: 

  • Natural language processing and Text mining basics
  • Significance and use-cases of text mining
  • NPL working with text mining, Language Toolkit(NLTK)
  • Text Mining: pre-processing, text-classification and cleaning

Learning Outcomes: At the end of this module, you will get a working knowledge of Natural Language Processing and Text Mining.
 

Mathematics for Data Science

This module discusses mathematical concepts like Probability basics, Bayes theorem, Numpy Mathematical functions, Conditional probability, and Joint probabilities.

Topics covered in this section are: 

  • Numpy Basics
  • Numpy Mathematical Functions
  • Probability Basics and Notation
  • Correlation and Regression
  • Joint Probabilities
  • Bayes Theorem
  • Conditional Probability, sum rule, and product rule

Learning Outcomes: By the end of this module, you will be able to use probability concepts, Numpy functions,  Bayes theorem, Correlation, and Regression in Data Science.
 

Scientific Computing through Scipy

In this module, you will learn how to perform scientific computing through the Scipy library.

Topics covered in this section are: 

Scipy Introduction and characteristics
Scipy sub-packages like Integrate, Cluster, Signal, Fftpack, and Bayes Theorem

Learning Outcomes: By the end of this module, you will get a real-time scientific computing experience.
 

Python Integration with Spark

In this module, you will learn the basics, importance, installation, advantages, and applications of Pyspark.

Topics covered in this section are: 

  • Pyspark basics
  • Uses and Need of pyspark
  • Pyspark installation
  • Advantages of pyspark over MapReduce
  • Pyspark applications

Learning Outcomes: At the end of this module, you will acquire practical knowledge of Pyspark.

 

Deep Learning and Artificial Intelligence

In this module, you will learn the concepts like Deep learning basics, supervised learning, neural networks basics, deep neural networks, convolutional neural networks, recurrent neural networks, and Deep Learning Graphical Processing Unit(GPU).

Topics covered in this section are: 

  •  Machine Learning effect on Artificial Intelligence
  • Deep Learning Basics, Working of Deep Learning
  • Regression and Classification in the Supervised Learning
  • Association and Clustering in unsupervised learning
  • Basics of Artificial Intelligence and Neural Networks
  • Supervised Learning in Neural Networks, multi-layer network
  • Deep Neural Networks, Convolutional Neural Networks
  • Reinforcement Learning, dnn optimisation algorithms
  • Recurrent Neural Networks, Deep learning graphics processing unit
  • Deep Learning Applications, Time series modeling

Learning Outcomes: By the end of this module, you will be able to master the deep learning and artificial intelligence concepts required for a data scientist.

 

Keras and TensorFlow API

This module teaches you how to use TensorFlow and Keras APIs to develop and deploy machine learning and deep learning models.

Topics covered in this section are: 

  • Tensorflow Basics and Tensorflow open-source libraries
  • Deep Learning Models and Tensor Processing Unit(TPU)
  • Graph Visualisation, keras
  • Keras neural-network 
  • Define and Composing multi-complex output models through Keras
  • Batch normalization, Functional and Sequential composition
  • Implementing Keras with tensorboard, customizing neural network training process
  • Implementing neural networks through TensorFlow API

Learning Outcomes: Upon completing this module, you will be able to build deep learning models and visualize the data through Keras and TensorFlow API.

 

Restricted Boltzmann Machine and Autoencoders

In this module, you will learn how to use restricted Boltzmann machines and autoencoders in deep learning.

Topics covered in this section are: 

  • Basics of Autoencoders and rbm
  • Implementing RBM for the deep neural networks
  • Autoencoders features and applications

Learning Outcomes: At the end of this module, you will achieve hands-on knowledge of Restricted Boltzmann machines and Autoencoders.

 

Big Data Hadoop and Spark

This module allows you to master the concepts of Hadoop, MapReduce, Hive, Kafka, Scala, Spark, Kafka, Spark Streaming, and Dstreams.

Topics covered in this section are: 

  • Big Data and Hadoop Basics
  • Hadoop Architecture, HDFS
  • MapReduce Framework and Pig
  • Hive and HBase
  • Basics of Scala and Functional Programming
  • Kafka basics, Kafka Architecture, Kafka cluster and Integrating Kafka with Flume
  • Introduction to Spark
  • Spark RDD Operations, writing spark programs.
  • Spark Transformations, Spark streaming introduction
  • Spark streaming Architecture, Spark Streaming Features
  • Structured streaming Architecture, Dstreams, and Spark Graphx

Learning Outcomes: By the end of this module, you will acquire real-time experience of working with HDFS, MapReduce framework, HBase, and Kafka. You will also achieve extensive knowledge of developing Spark programs and performing Spark transformations and Spark RDD operations.

Tableau

This Tableau module deals with Data Visualisation concepts, Tableau Installation, Tableau Architecture, sets creation, Tableau Dashboards, Stories, Graphs, and Charts. Along with this, you will also learn expressions, data blending, and tableau prep.

Topics covered in this section are: 

  • Data Visualisation Basics
  • Data Visualisation Applications
  • Tableau Installation and Interface
  • Tableau Data Types, Data Preparation
  • Tableau Architecture
  • Getting Started with Tableau
  • Creating sets, Metadata and Data Blending.
  • Arranging visual and data analytics
  • Mapping, Expressions, and Calculations
  • Parameters and Tableau prep
  • Stories, Dashboards, and Filters
  • Graphs, charts
  • Integrating Tableau with Hadoop and R

Learning Outcomes: By the end of this module, you will get a real-time experience of Creating sets, graphs, charts, dashboards for analyzing data. You will also acquire hands-on knowledge of tableau architecture, tableau installation, tableau prep, and integrating Tableau with R and Hadoop.

MongoDB

This MongoDB module will help you master the concepts like MongoDB basics, MongoDB installation, CRUD operations, Data Indexing, Data Modeling, and Data Administration. Along with this, you will also learn Data Aggregation Schema and Security concepts.

Topics covered in this section are: 

  • MongoDB and NoSQL Basics
  • MongoDB Installation
  • Significance of NoSQL
  • CRUD Operations
  • Data Modeling and Management
  • Data Indexing and Administration
  • Data Aggregation Schema 
  • MongoDB Security
  • Collaborating with Unstructured Data

Learning Outcomes: At the end of this module, you will get hands-on knowledge of using MongoDB for performing different database operations like creating a database, inserting data into a database, deleting and updating the data. You will also be able to master data modeling, data Indexing, and data administration.

SAS

This module deals with the SAS analytic concepts like functions, operators, data sets creation, procedures, graphs, and macros. You will also learn some advanced concepts of SAS.

Topics covered in this section are: 

  • SAS Basics
  • SAS Enterprise Guide
  • SAS functions and Operators
  • SAS Data Sets compilation and creation
  • SAS Procedures
  • SAS Graphs
  • SAS Macros
  • PROC SQL
  • Advance SAS

Learning Outcomes: By the end of this module, you will be able to carry out advanced data analysis by using SAS concepts.

MS Excel

This data science online classroom training module deals with excel concepts like conditional formatting, data filtering, pivot tables, logical functions, and creating charts.  Along with this, you will also learn how to use VBA concepts for data analysis. 

Topics covered in this section are: 

  • Entering Data
  • Logical Functions
  • Conditional Formatting
  • Validation, Excel formulas
  • Data sorting, Data Filtering, Pivot Tables
  • Creating charts, Charting techniques
  • File and Data security in excel
  • VBA macros, VBA IF condition, and VBA loops
  • VBA IF condition, For loop
  • VBA Debugging and Messaging

Learning Outcomes: At the end of this module, you will acquire a working knowledge of excel and VBA.

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Add-ons

Most of the Data Science Jobs in the industry expect the following add-on skills. Hence, we offer these skills-set as FREE Courses (Basics) to ease your learning process and help you stay ahead of the competition.

Agile & Scrum

Projects

Our Data Science Course course aims to deliver quality training that covers solid fundamental knowledge on core concepts with a practical approach. Such exposure to the current industry use-cases and scenarios will help learners scale up their skills and perform real-time projects with the best practices.

  1. Project 1: Taking Insurance Policy

  2. Project 2: Deciding Traffic on the Road

  3. Project 3: Analyzing Customers buying decision ‘Yes’ or ‘No’

  4. Project 4: Developing a mobile application

  5. Project 5: Setting up a new plant, factory, company, shopping mall or an institute

logoTraining Options

Choose your own comfortable learning experience.

Best Value

On-Demand Training

30 hrs of Self-Paced Videos

  • 30 hours of Data Science Course videos
  • Curated and delivered by industry experts
  • 100% practical-oriented classes
  • Includes resources/materials
  • Latest version curriculum with covered
  • Get lifetime access to the LMS
  • Learn technology at your own pace
  • 24x7 learner assistance
  • Certification guidance provided
  • Post sales support by our community
self-paced

Get Pricing

Preferred

Live Online (Instructor-Led)

30 hrs of Remote Classes in Zoom/Google meet

2025 Batches

Start - End

Time

Weekend

Mar 29 - Apr 13

07:00 PM

Weekdays

Apr 01 - Apr 16

07:00 PM

Weekend

Apr 05 - Apr 20

09:00 AM

Weekdays

Apr 08 - Apr 23

09:00 AM

Customize your schedule here

+ Includes Self-Paced
  • Live demonstration of the industry-ready skills.
  • Virtual instructor-led training (VILT) classes.
  • Real-time projects and certification guidance.
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For Corporates

Empower your team with new skills to Enhance their performance and productivity.

Corporate Training

  • Customized course curriculum as per your team's specific needs
  • Training delivery through self-Paced videos, live Instructor-led training through online, on-premise at Mindmajix or your office facility
  • Resources such as slides, demos, exercises, and answer keys included
  • Complete guidance on obtaining certification
  • Complete practical demonstration and discussions on industry use cases
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Online Work Support for your on-job roles.

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Our work-support plans provide precise options as per your project tasks. Whether you are a newbie or an experienced professional seeking assistance in completing project tasks, we are here with the following plans to meet your custom needs:

  • Pay Per Hour
  • Pay Per Week
  • Monthly
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Data Science Course Instructor

Learn from the certified and real time working professionals.

instructor

Training by

Abhishek , having 7+ yrs of experience

Specialized in:Data Science, AI & Machine Learning, Python

Passion towards teaching made Abhishek share the industrial experience he has got for further generations. He has got a total of three years into the real-time industrial background and has trained over 520+ students.

One Access for Multiple Courses

Choose from our Self-Paced learning library based on trending job Roles or Career Paths at a Discounted price.

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Data Science Course FAQs

What are the core objectives of the Data Science certification course in Hyderabad?

This Data Science Certification course In Hyderabad has been designed to make you expert in all fundamental concepts of Data Science. Besides training, you will also be working with real-time projects to gain hands-on expertise, and also our expert trainers provide you with the real-time assistance of attending interviews and techniques to qualify the certifications.

What are the concepts covered as a part of this training?

In this Data Science course hyderabad, you will learn the following concepts: 

 

  • Data analysis, project life cycle, and role of Data Science in this data-driven world
  • project deployment,  experimentation, and techniques of evaluation.
  • Integration of Hadoop with the R 
  • process to install Impala
  • Analysis segmentation using techniques of clustering, and prediction. 
  • Introduction to responsibilities and tasks handled by data scientist
  • Machine learning algorithms
  • R Programming  and other concepts in R
  • Analytics, Association rules, recommendation engines, and Data Science projects.

Who should take up this Online Data Science course in Hyderabad?

Below mentioned professionals can enhance their skill set by taking up this Data Science course in Hyderabad. 

  • Business intelligence professionals
  • Big data Experts
  • Predictive analyst Experts 
  • Big data Statisticians 
  • Predictive analyst Experts
  • Machine learning Professionals 
  • Candidates who wish to build their career in Data Science.

How learning Data Science Course helps in accelerating my career growth?

If you are about to start your career in Data Science or already working in Data Science field then congratulations you have chosen the right path where opportunities are limitless. Increased demand for data processing has laid roots for the huge demand for Data Science professionals. This surge is constantly growing over the years.  Companies are leveraging the benefits of data to accelerate their business growth. To handle the data and analytics process companies are looking for certified data scientists and offering huge packages.

Do you need any special qualification to enrol in this Data Science training in Hyderabad?

No, as such there are no special requirements to attend this Data Science online course. All you need to have is a willingness to learn. If you are good at maths then learning Data Science becomes a bit easier for you.

Are there any Job opportunities in Hyderabad for data scientists?

Hyderabad is becoming home for world-class companies with excellent infrastructure facilities. Hyderabad is an IT hub with thousands of companies ranging from large scale to small scale. All these things have contributed to the availability of more opportunities in Hyderabad compared to other cities.

How can you get assistance for preparing a Data Science interview?

Once you enrol into Mindmajix Data Science training in Hyderabad, we are not only committed to providing you with the best Data Science training but also make you ready for cracking Data Science interviews by conducting mock interviews. Moreover, our specialized teams will also help you with the resume preparation process.

I want to complete the training in a few days or a week? Is it possible?

Yes, we got custom training programs to complete the course as you need.

What are the system requirements I need to attend online training?

You need good internet connectivity with a mobile/tab/laptop/system installed with Zoom/Meet.

How can I access recorded videos of my training sessions?

You can access the recorded videos through our LMS after every session.

Do I get any discount on the course?

Yes, you get two kinds of discounts. They are group discounts and referral discounts.

  • A group discount is offered when you join as a group of three or more.
  • When you are referred by someone already enrolled for training, you receive a referral discount.

How do I get a course completion certificate?

You will get a verifiable course completion certificate once you attend all the sessions and successfully submit the assessments.

How do you help me with certification?

We provide you with all the necessary resources and guidance to get certified with the relevant software/technology vendor on your own.

Can you help me with resume preparation?

Yes, our experts help you draft the perfect resume that matches your desired job roles.

Do you offer placements after the training program?

Yes, we help you with placement assistance through mock interviews, resume building, and by forwarding your profile to our corporate clients seeking trained resources.

How does your mock interviews process work?

A mock interview is a unique program we offer to help you experience real-time interviews.
Our expert connects with you, conducts an interview, and provides you feedback and quick tips to improve your skills as needed.

Can I avail EMI option to pay the fee?

Yes, we have associated partners who allow payments through three/six easy installments.

Can I customize the course curriculum?

Yes, you can customize the course curriculum as per your requirements.

Can I rejoin the subsequent batch if I cannot continue in the current schedule?

Yes, we provide feasibility to attend the next batch for the missing sessions; however, we cannot guarantee that the same trainer and the same kind of schedule would be available.

Will you continuously update your course content as per the latest software version/release available?

We keep our course curriculum aligned with the current stable Technology / Software version releases.

We want to hire resources you have trained. Is it possible?

Yes, we can offer resources depending upon the availability.

Why is there a difference between live online and self-paced videos duration?

In the case of live online training, we consider additional interaction time between the trainer and the learners.

Can we extend the access for the pre-recorded sessions?

Yes, you can renew the access after one year with a minimal fee.

Is it possible to customize the live training (scheduling and curriculum)?

Yes, we can customize the course curriculum and schedule the sessions as per your project requirements.

Do you conduct assessments at the end of the program?

Yes, we do conduct assessments.

Can we extend the lab access beyond training completion?

Yes. On special request, we can extend lab practice sessions for the learners at an extra cost.

We need a few consulting sessions with the training after completion. Is it possible?

Yes, we can arrange consulting sessions with the trainer at an extra cost.

User Testimonials
Everyone from start-ups to large enterprises prefer Mindmajix
Naresh Kumar

I have learned a lot from this training which I missed in my previous training institute. You helped me understand the data science concepts really well. Thank you MindMajix for your efforts.

Naresh Kumar

Stamford, Connecticut, USA

Rating: 5

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