image

IBM Certified Data science training with Free Internship & 100% Placement Assistance

About The Course

Data is the fuel of the 21st Century.

This advanced IBM Certified Data Science course in Hyderabad guarantees career transformation. Here’s a one time opportunity to learn with the best Data Science training in Hyderabad. Gain knowledge of data analytics, tools, and operations for data science certification and meet the massive demand for these skills.

Here you will learn to read, analyze, clean, engineer and present data in a way that promotes the growth of your business. In order to drive data and extract significant results, this Data Science course can help you progress in leaps and bounds. This IBM Certified Data Science training will accelerate your career as it covers relevant topics & pushes you to work on real-time scenarios. 

Artificial Intelligence and Machine Learning in Data Science technology are constantly revolutionizing the industry by innovating and solving complex business problems. Innomatics Research Labs is a hub of advanced training in such technologies.

Our principle of holistic development lies on the strong bedrock that believes in the amalgamation of theoretical knowledge along with practical training. This makes us the best Data Science course in Hyderabad. 

    Enroll Here for Course

    IBM Certified Data Science Webinar/Demo/Batch details

    Session Type Date & Time
    New Batch – OFFLINE May 5th at 10 AM
    New Batch – ONLINE May 5th at 8 AM

                   PREREQUISITES: The candidate must be pursuing a Bachelor’s degree.
                                                   Previous coding experience is an added benefit.

    IBM Certified Data Science Course Curriculum (Syllabus)

    Module 1: Python Core and Advanced

    INTRODUCTION

    • What is Python?
    • Why does Data Science require Python?
    • Installation of Anaconda
    • Understanding Jupyter Notebook
    • Basic commands in Jupyter Notebook
    • Understanding Python Syntax

    Data Types and Data Structures

    • Variables and Strings
    • Lists, Sets, Tuples, and Dictionaries

    Control Flow and Conditional Statements

    • Conditional Operators, Arithmetic Operators, and Logical Operators
    • If, Elif and Else Statements
    • While Loops
    • For Loops
    • Nested Loops and List and Dictionary Comprehensions

    Functions

    • What is function and types of functions
    • Code optimization and argument functions
    • Scope
    • Lambda Functions
    • Map, Filter, and Reduce

    File Handling

    • Create, Read, Write files and Operations in File Handling
    • Errors and Exception Handling

    Class and Objects

    • Create a class
    • Create an object
    • The __init__()
    • Modifying Objects
    • Object Methods
    • Self
    • Modify the Object Properties
    • Delete Object
    • Pass Statements

    Module 2: Data Analysis in Python

    Numpy – NUMERICAL PYTHON

    • Introduction to Array
    • Creation and Printing of an array
    • Basic Operations in Numpy
    • Indexing
    • Mathematical Functions of Numpy

    2. Data Manipulation with Pandas

    • Series and DataFrames
    • Data Importing and Exporting through Excel, CSV Files
    • Data Understanding Operations
    • Indexing and slicing and More filtering with Conditional Slicing
    • Group by, Pivot table, and Cross Tab
    • Concatenating and Merging Joining
    • Descriptive Statistics
    • Removing Duplicates
    • String Manipulation
    • Missing Data Handling
    DATA VISUALIZATION

    Data Visualization using Matplotlib and Pandas

      • Introduction to Matplotlib
      • Basic Plotting
      • Properties of plotting
      • About Subplots
      • Line plots
      • Pie chart and Bar Graph
      • Histograms
      • Box and Violin Plots
      • Scatterplot

      Case Study on Exploratory Data Analysis (EDA) and Visualizations

      • What is EDA?
      • Uni – Variate Analysis
      • Bi-Variate Analysis
      • More on Seaborn based Plotting Including Pair Plots, Catplot, Heat Maps, Count plot along with matplotlib plots.
      UNSTRUCTURED DATA PROCESSING

      Regular Expressions

      • Structured Data and Unstructured Data
      • Literals and Meta Characters
      • How to Regular Expressions using Pandas?
      • Inbuilt Methods
      • Pattern Matching

      PROJECT ON WEB SCRAPING: DATA MINING and EXPLORATORY DATA ANALYSIS

      • Data Mining (WEB – SCRAPING)
        This project starts completely from scratch which involves the collection of Raw Data from different sources and converting the unstructured data to a structured format to apply Machine Learning and NLP models. This project covers the main four steps of the Data Science Life Cycle which involves.

        • Data Collection
        • Data Mining
        • Data Preprocessing
        • Data Visualization
          Ex: Text, CSV, TSV, Excel Files, Matrices, Images

      Module 3: Advanced Statistics

      Data Types and Data Structures

      • Statistics in Data science:
      • What is Statistics?
      • How is Statistics used in Data Science?
      • Population and Sample
      • Parameter and Statistic
      • Variable and its types

      Data Gathering Techniques

      • Data types
      • Data Collection Techniques
      • Sampling Techniques:
      • Convenience Sampling, Simple Random Sampling
      • Stratified Sampling, Systematic Sampling, and Cluster Sampling

      Descriptive Statistics

      • What is Univariate and Bi Variate Analysis?
      • Measures of Central Tendencies
      • Measures of Dispersion
      • Skewness and Kurtosis
      • Box Plots and Outliers detection
      • Covariance and Correlation

      Probability Distribution

      • Probability and Limitations
      • Discrete Probability Distributions
      • Bernoulli, Binomial Distribution, Poisson Distribution
      • Continuous Probability Distributions
      • Normal Distribution, Standard Normal Distribution

      Inferential Statistics

      • Sampling variability and Central Limit Theorem
      • Confidence Intervals
      • Hypothesis Testing
      • Z-test, T-test
      • Chi-Square Test
      • F-Test and ANOVA

      Module 4. SQL for Data Science

      Introduction to Databases

      • Basics of SQL
        • DML, DDL, DCL, and Data Types
        • Common SQL commands using SELECT, FROM, and WHERE
        • Logical Operators in SQL
      • SQL Joins
        • INNER and OUTER joins to combine data from multiple tables
        • RIGHT, LEFT joins to combine data from multiple tables
      • Filtering and Sorting
        • Advanced filtering using IN, OR, and NOT
        • Sorting with GROUP BY and ORDER BY
      • SQL Aggregations
        • Common Aggregations including COUNT, SUM, MIN, and MAX
        • CASE and DATE functions as well as work with NULL values
      • Subqueries and Temp Tables
        • Subqueries to run multiple queries together
        • Temp tables to access a table with more than one query
      • SQL Data Cleaning
        • Perform Data Cleaning using SQL

      Module 5: Machine Learning Supervised Learning

      INTRODUCTION
      • What Is Machine Learning?
      • Supervised Versus Unsupervised Learning
      • Regression Versus Classification Problems Assessing Model Accuracy
      REGRESSION TECHNIQUES

      Linear Regression

      • Simple Linear Regression:
      • Estimating the Coefficients
      • Assessing the Coefficient Estimates
      • R Squared and Adjusted R Squared
      • MSE and RMSE

      Multiple Linear Regression

      • Estimating the Regression Coefficients
      • OLS Assumptions
      • Multicollinearity
      • Feature Selection
      • Gradient Descent

      Evaluating the Metrics of Regression Techniques

      • Homoscedasticity and Heteroscedasticity of error terms
      • Residual Analysis
      • Q-Q Plot
      • Cook’s distance and Shapiro-Wilk Test
      • Identifying the line of best fit
      • Other Considerations in the Regression Model
      • Qualitative Predictors
      • Interaction Terms
      • Non-linear Transformations of the Predictors

      Polynomial Regression

      • Why Polynomial Regression
      • Creating polynomial linear regression
      • Evaluating the metrics

      Regularization Techniques

      • Lasso Regularization
      • Ridge Regularization
      • ElasticNet Regularization
      • Case Study on Linear, Multiple Linear Regression, Polynomial, Regression using Python

      CAPSTONE PROJECT:  A project on a use case will challenge the Data Understanding, EDA, Data Processing, and above Regression Techniques.

      CLASSIFICATION TECHNIQUES

      Logistic regression

      • An Overview of Classification
      • Difference Between Regression and classification Models.
      • Why Not Linear Regression?
      • Logistic Regression:
      • The Logistic Model
      • Estimating the Regression Coefficients and Making Predictions
      • Logit and Sigmoid functions
      • Setting the threshold and understanding decision boundary
      • Logistic Regression for >2 Response Classes
      • Evaluation Metrics for Classification Models:
      • Confusion Matrix
      • Accuracy and Error rate
      • TPR and FPR
      • Precision and Recall, F1 Score
      • AUC-ROC
      • Kappa Score

      Naive Bayes

      • Principle of Naive Bayes Classifier
      • Bayes Theorem
      • Terminology in Naive Bayes
      • Posterior probability
      • Prior probability of class
      • Likelihood
      • Types of Naive Bayes Classifier
      • Multinomial Naive Bayes
      • Bernoulli Naive Bayes and Gaussian Naive Bayes
      TREE BASED MODULES

      Decision Trees

      • Decision Trees (Rule-Based Learning):
      • Basic Terminology in Decision Tree
      • Root Node and Terminal Node
      • Regression Trees and Classification Trees
      • Trees Versus Linear Models
      • Advantages and Disadvantages of Trees
      • Gini Index
      • Overfitting and Pruning
      • Stopping Criteria
      • Accuracy Estimation using Decision Trees

      Case Study: A Case Study on Decision Tree using Python

      • Resampling Methods:
      • Cross-Validation
      • The Validation Set Approach Leave-One-Out Cross-Validation
      • K-Fold Cross-Validation
      • Bias-Variance Trade-O for K-Fold Cross-Validation

      Ensemble Methods in Tree-Based Models

      • What is Ensemble Learning?
      • What is Bootstrap Aggregation Classifiers and how does it work?

      Random Forest

      • What is it and how does it work?
      • Variable selection using Random Forest

      Boosting: AdaBoost, Gradient Boosting

      • What is it and how does it work?
      • Hyper parameter and Pro’s and Con’s

      Case Study: Ensemble Methods – Random Forest Techniques using Python

      DISTANCE BASED MODULES

      K Nearest Neighbors

      • K-Nearest Neighbor Algorithm
      • Eager Vs Lazy learners
      • How does the KNN algorithm work?
      • How do you decide the number of neighbors in KNN?
      • Curse of Dimensionality
      • Pros and Cons of KNN
      • How to improve KNN performance

      Case Study: A Case Study on KNN using Python

      Support Vector Machines

      • The Maximal Margin Classifier
      • HyperPlane
      • Support Vector Classifiers and Support Vector Machines
      • Hard and Soft Margin Classification
      • Classification with Non-linear Decision Boundaries
      • Kernel Trick
      • Polynomial and Radial
      • Tuning Hyper parameters for SVM
      • Gamma, Cost, and Epsilon
      • SVMs with More than Two Classes

      Case Study: A Case Study on SVM using Python

      CAPSTONE PROJECT: A project on a use case will challenge the Data Understanding, EDA, Data Processing, and above Classification Techniques.

      Module 6: Machine Learning Unsupervised Learning

      • Why Unsupervised Learning
      • How it Different from Supervised Learning
      • The Challenges of Unsupervised Learning

      Principal Components Analysis

      • Introduction to Dimensionality Reduction and its necessity
      • What Are Principal Components?
      • Demonstration of 2D PCA and 3D PCA
      • Eigen Values, EigenVectors, and Orthogonality
      • Transforming Eigen values into a new data set
      • Proportion of variance explained in PCA

      Case Study: A Case Study on PCA using Python

      K-Means Clustering

      • Centroids and Medoids
      • Deciding the optimal value of ‘K’ using Elbow Method
      • Linkage Methods

      Hierarchical Clustering

      • Divisive and Agglomerative Clustering
      • Dendrograms and their interpretation
      • Applications of Clustering
      • Practical Issues in Clustering

      Case Study: A Case Study on clusterings using Python

      Association Rules

      • Market Basket Analysis

      Apriori

      • Metric Support/Confidence/Lift
      • Improving Supervised Learning algorithms with clustering

      Case Study: A Case Study on association rules using Python

      CAPSTONE PROJECT: A project on a use case will challenge the Data Understanding, EDA, Data Processing, and Unsupervised algorithms.

      RECOMMENDATION SYSTEMS

      • What are recommendation engines?
      • How does a recommendation engine work?
      • Data collection
      • Data storage
      • Filtering the data
      • Content-based filtering
      • Collaborative filtering
      • Cold start problem
      • Matrix factorization
      • Building a recommendation engine using matrix factorization
      • Case Study

      Module 7: Deep Learning

      Introduction to Neural Networks
      • Introduction to Perceptron & History of Neural networks
      • Activation functions
        • a)Sigmoid b)Relu c)Softmax d)Leaky Relu e)Tanh
      • Gradient Descent
      • Learning Rate and tuning
      • Optimization functions
      • Introduction to Tensorflow
      • Introduction to Keras
      • Backpropagation and chain rule
      • Fully connected layer
      • Cross entropy
      • Weight Initialization
      • Regularization

      TensorFlow 2.0

      • Introducing Google Colab
      • Tensorflow basic syntax
      • Tensorflow Graphs
      • Tensorboard

      Artificial Neural Network with Tensorflow

      • Neural Network for Regression
      • Neural Network for Classification
      • Evaluating the ANN
      • Improving and tuning the ANN
      • Saving and Restoring Graphs

      Module 8: Computer Vision

      Working with images & CNN Building Blocks
      • Working with Images_Introduction
      • Working with Images – Reshaping understanding, size of image understanding pixels Digitization,
      • Sampling, and Quantization
      • Working with images – Filtering
      • Hands-on Python Demo: Working with images
      • Introduction to Convolutions
      • 2D convolutions for Images
      • Convolution – Backward
      • Transposed Convolution and Fully Connected Layer as a Convolution
      • Pooling: Max Pooling and Other pooling options

      CNN Architectures and Transfer Learning

      • CNN Architectures and LeNet Case Study
      • Case Study: AlexNet
      • Case Study: ZFNet and VGGNet
      • Case Study: GoogleNet
      • Case Study: ResNet
      • GPU vs CPU
      • Transfer Learning Principles and Practice
      • Hands-on Keras Demo: SVHN Transfer learning from MNIST dataset
      • Transfer learning Visualization (run package, occlusion experiment)
      • Hands-on demo T-SNE

      Object Detection

      • CNN’s at Work – Object Detection with region proposals
      • CNN’s at Work – Object Detection with Yolo and SSD
      • Hands-on demo- Bounding box regressor
      • #Need to do a semantic segmentation project

      CNN’s at Work – Semantic Segmentation

      • CNNs at Work – Semantic Segmentation
      • Semantic Segmentation process
      • U-Net Architecture for Semantic Segmentation
      • Hands-on demo – Semantic Segmentation using U-Net
      • Other variants of Convolutions
      • Inception and MobileNet models

      CNN’s at work- Siamese Network for Metric Learning

      • Metric Learning
      • Siamese Network as metric learning
      • How to train a Neural Network in Siamese way
      • Hands-on demo – Siamese Network

      Module 9: Natural Language Processing (NLP)

      Introduction to Statistical NLP Techniques

      • Introduction to NLP
      • Preprocessing, NLP Tokenization, stop words, normalization, Stemming and lemmatization
      • Preprocessing in NLP Bag of words, TF-IDF as features
      • Language model probabilistic models, n-gram model, and channel model
      • Hands-on NLTK

      Word Embedding

      • Word2vec
      • Golve
      • POS Tagger
      • Named Entity Recognition(NER)
      • POS with NLTK
      • TF-IDF with NLTK

      Sequential Models

      • Introduction to sequential models
      • Introduction to RNN
      • Introduction to LSTM
      • LSTM forward pass
      • LSTM backdrop through time
      • Hands-on Keras LSTM

      Applications

      • Sentiment Analysis
      • Sentence generation
      • Machine translation
      • Advanced LSTM structures
      • Keras – machine translation
      • ChatBot

      Module 10: Tableau for Data Science

      Tableau for Data Science
      • Install Tableau for Desktop 10
      • Tableau to Analyze Data
      • Connect Tableau to a variety of dataset
      • Analyze, Blend, Join and Calculate Data
      • Tableau to Visualize Data
      • Visualize Data In the form of Various Charts, Plots, and Maps
      • Data Hierarchies
      • Work with Data Blending in Tableau
      • Work with Parameters
      • Create Calculated Fields
      • Adding Filters and Quick Filters
      • Create Interactive Dashboards
      • Adding Actions to Dashboards

      Languages & Tools covered in IBM Certified Data Science

      image
      image
      image
      image
      image
      image
      image
      image

      OUR USPs

      image

      Why Data Science at Innomatics Research Labs?

      20+ Industry experts from fortune 500 companies
      Dedicated In-house data scientist team accessible round the clock
      200+ Hours of intensive practical oriented training
      Flexible Online and Classroom training sessions
      5+ Parallel Data science batches running currently on both weekdays & weekends
      Backup Classes and Access to the Learning Management System (LMS)
      One-to-One Mentorship and Free Technical Support
      FREE Data science Internship on our projects & products
      Projects and use-cases derived from businesses
      30+ POCs and use cases to work, learn, and experiment
      Bi-weekly Industry connects from industry experts from various sectors
      Opportunity to participate in Meet-ups, Hackathons, and Conferences
      Dedicated training programs for NON-IT professionals
      100% placement assistance
      Globally Recognized Certification from IBM

      Why IBM Certified Data Science at Innomatics Research Labs

      Meet Our Expert Advisors

      What is the scope of Certified Data Scientists in India?

      Data Science is quoted as the Sexiest Job of the 21st Century – Harvard Business Review

      According to the Harvard Business Review, Data Scientist is the sexiest job of the 21st century. Data Science has also topped LinkedIn’s Emerging Jobs’ List for 3 years in a row. 

      During the pandemic in 2021, there were around 82,000 job openings globally that required skills in Data Analysis and India witnessed a 45% increase in the adoption of Artificial Intelligence. 

      Therefore with each passing day, individuals and organizations are embracing the digital increasing the market demand of Data scientists. The average salary of a Data Scientist in Hyderabad alone is at around Rs 6 lakhs. An entry-level Data Scientist can earn anything between Rs 5 -6 lakhs. . If a candidate is willing to constantly learn, upgrade and upskill themselves, the compensation package may go up to Rs 24 lakhs or crores for that matter.

      Job opportunities (Careers) in Data Science Technology

      Data Scientists are needed for businesses in every Industry. Even tech giants such as Google, Amazon, Apple, Facebook, Microsoft are constantly in need of Data Science experts who have in-depth knowledge in data extraction, data mining, visualization, and more. Here are some leading careers in Data Science

      • Business Intelligence Developer: With an average salary of $89,333, they design and develop business strategies for quick decision-making and growth.
      • Data Architect: With an average salary is $137,630, they build data solutions that can be applied on multiple platforms.
      • Applications Architect: With an average salary of around $134,520, they track applications behavior and applied in the business to analyze the way they interact with the user.
      • Industry Architect: With an average salary of $126,353, they analyze the business system and optimize accordingly to support the development of updated technologies and system requirements.
      • Enterprise Architect: With an average salary of $161,323 they work with stakeholders, including management and subject matter experts (SME), to develop a view of an organization’s strategy, information, processes, and IT assets.
      • Data Scientist: With an average salary of $139,480, they explore, analyze, visualize, and organize data for the companies. They analyze the complex data sets and processes to find patterns for decision making and predicting the business and drive strategies.
      • Data Analyst: With an average salary of $83,989, they transform and manipulate large sets of data, which incorporate web analytics tracking and testing.
      • Data Engineer: With an average salary of $151,498, they perform real-time processing on data that is visualized and stored.

      A few reviews from our students

      Abhishek Murthy

      INNOMATICS RESEARCH LABS is really a wonderful destination for students really interested in Data science .The curriculum the Internship & they’ve some of the great professionals who help you absorb deeply into Data Science which really helps one individual to transform from nothing to something with shear confidence. I would highly recommend Innomatics for all the interested students who want learn want & deep dive into Data Science as it has really helped me learn & grow

      Data Science Intern

      Pooja Roy Choudhary

      Hello Everyone, It’s not just a feedback which I am giving here but it’s the respect what I feel for the Innomatics Team specially our mentor Senior Data Scientist Kanav Bansal sir who have supported me very much throughout internship journey. I must say he is the best mentor I have ever got in my Data Science journey. I learned data science already from other institute. But i have joined Innomatics as an intern in October 2021, when I joined Innomatics, I really had a best experience with Innomatics Institute. The way they look after and treat their students and interns is amazing. The teaching here is very professional, even the workshops which are conducted by them are amazing and full of learning experiences and knowledge, I can say it is one of the best Data Science Institute in India and I congratulate each and every team member of Innomatics for giving such a wonderful support system to all students and the people who are connected with them.

      Data Science Trainee

      Syed Saba

      Innomatics has been true to its words, they really did transform me from a noob coder to a good one. The daily practice and the well curated assignments help me become better day by day. Though I was part of Innomatics for a short time (data science internship) they left a huge impact. From the cleverly curated content, assignments and the interactive sessions to the every helpful team of Innomatics . Every session of Kanav sir is power-packed and you learn something new. It’s been an amazing ride. Kudos to great work!

      Data Science Intern

      Ameer Abdul

      Innomatics stands true to its name. It really does transform careers, because I came from a mechanical engineering background and today I am a Data Scientist. The productive environment and laboratory setup that stresses on practical training is one of the best features. From placement services, mentorship, LMS Access, Discord to providing a student with expert and world-class training, Innomatics has everything in its kitty. I recommend this institute for everyone who aspires to be a Data Scientist. Here you will build your confidence and learn a lot.

      Data Scientist

      Ramyareddy Munnangi

      After completion of my M.Sc., I have decided to join the Data Science course so in the process of searching Institute’s , I found that @Innomatics Research Labs is the best one. And yes, my decision is right…. Our trainer(senior Data Scientist) he is the amazing person and the way he teaches was really good. The mentors clarified each and every doubts….very friendly to students. @Innomatics….provide real time projects and also hands on practice  Provide placement assistance. Recently, I completed Data science Course at Innomatics Research Labs and now I was placed in some company, Thank you Innomatics Research Labs .

      Data Scientist

      Vinod Kumar

      It’s an excellent platform for everyone; IT or non-IT , fresher or experienced who wants to learn Data science. The trainers, mentors and HR all are very supportive. They provide LMS access also , including recorded sessions and assignments which can be accessed even after the course completion . Because of their constant support and encouragement i got placed at a good company with a good package as soon as my course ended.

      Data Science trainee

      Maheshwarareddy Manthu

      Innomatics family has helped me learn Data Science and build a successful career.. Today i am a more confident individual with world class training and bright future in hand. I thank this institution for helping me build my confidence. If you are looking for a Data Science course in Hyderabad, Innomatics is the perfect destination. Thanks to all the mentors, trainers and the entire Innomatics team

      Data Science Trainee

      Vaishnavi Mandhani

      Innomatics Research labs is the only platform to learn Data Science course with real-time working experience, they just don’t teach, let you work, and gain experience. There are so many institutions charging Lakhs of rupees but not teaching complete data science course. But at Innomatics training institute, I learned real data science and practical application of it. After joining here only I got to know what real Data science is. 3 months back I don’t know about Data Science. Today I can claim that I am an expert on it. As part of my internship, I worked on the Docnizer project. It gave me an enormous amount of knowledge in Data Science. Thank you Innomatics – Hyderabad Branch

      Data Scientist

      Moin Mansuri

      Innomatics has helped me in every situation, be it training, completing assignments, doing the tasks or preparing for interviews. I have received more than what I had expected from this place. Thank you for teaching from scratch. Despite being from a Sales background, I never felt like an outsider in the class. All concepts were taught from the basic level till the advanced level. I am privileged & happy to carry the Innominion tag.

      Data Science Trainee

      Soumya Prusty

      When I first joined Innomatics Data Science online course, I was a little anxious about how I will complete this course. Especially because I was doing my course from Orissa. But as soon as the course started my worries were put to rest. The Innomatics Online Data Science course has comprehensive curriculum with many many practical applications, efficient mentorship model, world class trainers and more… I would recommend this institute to everyone who is looking to study Data Science. Don’t worry about online or offline. If you are willing to work hard and looking for an institute to provide you with knowledge and right advice, Innomatics is for you. Also, no fake promises & only 100% placement assistance 🙂

      Data Science Trainee

      Bindiya Roy

      I came across Innomatics Research Labs as a data science Intern. It was unpaid but the way Kanav Sir trained us, the Innomatics team supported us it was just amazing and I’ve learned a lot.Worked on few projects also, actually everything boosted up my confidence which was really needed and made my training super enjoyable. From having just interest in Data Science, it’s become my goal. If their internship training is like this, then I can guess how the couse would be! They all are so supportive, sweet, cool & friendly. They just don’t say “We just don’t train, We transform the careers” … They actually mean it !! 😊 A big Thanks to the Innomatics Team !! 💐💐

      Data Science Intern

      PV Meenakshi

      One of the best learning institute in Hyderabad I say! In every aspect, Innomatics research labs training institute has proved to be the best for me. The topics and projects covered in Machine Learning & Artificial Intelligence were real-time and it helped me understand the basics along with high-level stuff, equally well. Thank you Innomatics for turning me into a Young data scientist!

      Data Scientist

      Frequently Asked Questions (FAQs)

      1) What will I learn in IBM certified Data Science?

      In Data Science, you will learn how to find valuable data, analyze and apply mathematical skills to it to use in business for making great decisions, developing a product, forecasting, and building business strategies. 

      2) What is the average salary of a Data Scientist?

      Salary of a Data Scientists entirely depends on the skillset. As per the recent reports, on average a Data Scientists earn ₹14,00,000 per year.

      3) Are there any prerequisites to learn the Data Science course?

      One need not have any major knowledge in Data Science. A basic understanding of technology is all enough to get started. It is better to possess knowledge of mathematical and communication skills, Python, R, and SAS tools.

      4) What are my takeaways after completion of the Data Science course?

      Based on the program you choose, you will get a course completion certificate from Innomatics. Mastery level certification from IBM.

      5) What are the career opportunities in Data Science Technology?

      As data has become the never-ending part of this world, businesses need people to work with data for effective business processing. Organizations are ready to recruit and pay top dollars to the right dollars, which can leverage the business.

      Here are some of the roles you can find in Data Science

      • Research Analyst
      • Data Scientist
      • Data Analyst
      • Big Data Analytics Specialist
      • Business Analyst Consultant / Manager
      • Data analyst

      6) If I study Data Science course in Hyderabad, is placement guaranteed?

      Apart from the training, we do provide placement and career assistance with capstone projects and hands-on training after completing the course successfully. We do offer internship programs, mockup interviews, hackathons to gain more knowledge and explore a wide range of job opportunities.

      7) Will I get any career support after the Data Science training?

      All our trainees will have access to the Learning Management System (LMS), where they can get the backup classes and stay updates, 1-1 interviews, continuous updates on placements, and hackathons.

      8) What is the eligibility criteria to learn Data Science course?

      Anyone who has a bachelor’s degree, a passion for data science, and little knowledge of it are eligibility criteria for the Data Science Course.

      Join us!! We'll transform your career. Call Now