IBM Certified Data Science program
For Mainframe Professionals

Data Science for Mainframe professionals

About Mainframe Technology:

The computers used in large organizations to manage critical applications such as bulk data processing, enterprise resource planning, and transaction processing is called mainframe computers. Mainframe is a technology where the requests received are processed fast and sent to the related processor cards. Mainframe Technology hosts about 80% of data in the world in different domains like Healthcare, Finance, E-commerce, Data Security, logistics, Science and Technology, and now on Data Science. Almost all big companies still have 80-90% of their applications written in the mainframe.

Mainframe professionals are considered as the people with most of the domain knowledge and play a vital role when it comes to estimating the future for a certain technology. Even though the technology is more than 50 years old, it is still irreplaceable, as compared to the other technologies which become obsolete within a couple of years of coming into the market.

Data Science in Mainframes Technology

Considering the advancements and enhancements in the field of Data Science and Artificial Intelligence, it is an ideal match for the mainframe professionals to learn Data Science and enhance their careers using their domain expertise. In fact, our founders themselves are from a mainframe background and around 15% of our daily inquiries come from Mainframe professionals, for a separate course in Data Science.

So, we are excited to launch and welcome you to an exceptional Data Science course for Mainframe Professionals which is curated to deliver the course in-depth for 4 months, on weekends. So that, it would be feasible for the working professionals to upskill themselves. For further queries, please contact to the given number.

    Enroll Here for Course

    Languages & Tools covered in IBM Certified Data Science course

    Professional Data Science Course details

    ProgramDetails
    Program Duration5 Months
    Session TypeWeekends
    Class TypeOffline Classes
    New Batch (Offline)N/A

    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: Productionizing ML

      • Components of productionizing ML systems like data analysis, transformation, tuning, serving, Integration, and workflow
      • Training and serving design
      • Data Ingestion (could be part of bigger data engineering module)
      • Serving ML in batch and real-time
      • Docker and container basics
      • Flask with Python
      • Creating a rest API web app in a flask on local
      • Creating a docker file and container image on the local machine
      • Deploying the image in either one of the cloud options like Azure Container Registry or AWS docker
      • Concepts of code repository and version control
      • Using CodePipeline in AWS
      • CodeBuild using docker image
      • Using Amazon container registry
      • AWS Lambda to trigger the deployment to AWS Sagemaker for endpoint.

      Module 11: 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

      Mode of training at Innomatics Reseach Labs

      We provide Classroom training on Data Science for Mainframe Professionals at Hyderabad for the individuals who believe in hand-held training. We teach as per the Indian Standard Time (IST) with In-depth practical Knowledge on each topic in classroom training, 120 – 150 Hours of Real-time practical training classes. There are different slots available on weekends according to your choices. We are also available over the call or mail or direct interaction with the trainer for active learning.

      For any queries feel free to Call/WhatsApp us on +91-9951666670 or mail at info@innomatics.in

      We provide IBM Certified Data Science for corporates, which helps businesses to strengthen and reap huge benefits. We always stay updated and provide training on real-time use cases, which bridges the gap enabling the organization to capitalize on the potential of the employees.

      For any queries feel free to Call/WhatsApp us on +91-9951666670 or mail at info@innomatics.in

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      Why Data Science at Innomatics Research Labs?

      • 18+ Data Science Industry experts from fortune 500 companies
      •  Dedicated In-house data scientist team accessible round the clock
      •  150+ Hours of intensive practical oriented training
      •  Full-time Internship on our projects & products
      •  Bi-weekly Industry connects from industry experts from various sectors 
      •  Project and use-cases derived from businesses
      •  Opportunity to participate in meet-ups, hackathons, and conferences
      •  Dedicated training programs for NON-IT professionals
      •  100% placement assistance
      Why IBM Certified Data Science at Innomatics Research Labs

      Meet Our Expert Advisors

      A few reviews from our Trainees

      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

      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

      Srinivas Challa

      I have attended a webinar given by IBM’s Senior Expert Mr G Ananthapadmanabhan (Practice leader – Analytics) on Emerging trends in Analytics and Artificial Intelligence. It was a great session and got a basic idea of how AI is being used in analytics nowadays. After the end of the session, I was glad to join the Data Science program. I came to know about AI & Machine Learning in very detailed. The mentorship through industry veterans and student mentors makes the program extremely engaging.

      Data Science Trainee

      Vinay Keerthi

      Innomatics Research Lab is one of the best data science training institute in Hyderabad. I joined here as soon as I completed my bachelor’s to attain a big job. This team suggested me to go with a data science course, for the first two days I heard the demo and just got impressed by their teaching and with no second thought choose data science course. I’m almost about to complete my course and no doubt they are turning me into a very well qualified data scientist.

      Data Science trainee

      Patlolla Laxma Reddy

      This is my first offline course ever taken. Initially, I was confused to take the Data Science Associate Course offline but, accidentally, I came across INNOMATICS Research Labs training institute website after inquiring about the course from many sources. After seeing the course curriculum and way of teaching, it impressed me. Starting from the first day of sessions, I was very happy and impressed with the way they handled the classes. Never did I feel bored during the sessions and the hands-on experience is good. The credit for this experience goes to Innomatics and the educator. I hope to travel with Innomatics training institute for a few more courses (Recently Launched INVESTMENT BANKING) in the days to come. Important Note: They did not make any false commitments/promises that we provide 100% Job guarantee, in this case, so many other institutes give you false commitment (100% job) but they never achieved their commitments. All they have promised me that provide the best training. Thanks for this wonderful experience!

      Data Science Trainee

      Rishabh Garg

      Writing this review after completing close to 3 months at this institute: I am working as a Tech Lead for Oracle Apps at a major Indian Tech firm for their client projects in Asia Pacific Countries. I came from Sydney to Hyderabad for a short project and decided to utilize spare time to learn Data Science course. Before joining Innomatics training institute, I researched a lot of online and classroom training for Data Science course. I even attended a couple of demo sessions of many institutes. Finally, I choose Innomatics training institute because of the depth and clarity of the trainers as well as the professional environment. All main trainers have rich industry experts & they also have an in-house team who r building products on image processing using AI and NLP, they also absorb good performers into this inhouse team. In the last 3 months, able to complete 3 major modules: Stats, Python and Machine Learning – Regressions, which were greek to me until now. Thanks to Arvind, Avinash, Amresh, Raghu. Appreciate the fact that they extend their super tight schedules to help out whenever required. Unlike other places, here some trainers went out of their way and also sacrificed their weekends many times to provide more support for difficult topics whenever required like Hypothesis-testing in Stats, PCA and LogReg in ML, etc… Recommend this institute to anyone looking to start their journey into data science.

      Data Science trainee
      Oracle Apps

      Sai Chaitanya

      I joined Innomatics training institute as a student and an Intern at the end of December 2018. It has been a knowledgeable experience to date. I got to work on some interesting projects and case studies as a Data Science Intern. The mentors at the institute are talented, experienced, yet friendly. They are always ready to help. It has been a great experience and I am sure I will benefit a lot from it in my career.

      Data Scientist

      Vakkalagadda Rajasree

      I am B.Tech fresher When I am looking for data science training institutes. I found innomatics training institute is so practical. The way they teach everything is practical 1st-week stats From 2nd week they will start python programming. Any layman can understand, They cover from basics to advance, they have nearly 220 +uscases. This is the only institute who train on web scraping projects. These people will cover Python, Statistics, Machine learning, Deep learning, SQL, Excel, NLP & AI. Finally, I am done with course. I am doing the coding. If u r from NON-IT blindly joins innomatics training institute. Thanks to innomatics. I made the right choice.

      Data Science Trainee

      Dipti Thakkar

      I found the course at Innomatics Research Labs extremely practical and market-oriented. It was a great and high-quality learning experience in terms of Instructor’s knowledge, delivery, course content of the program, notes, quality of materials and especially the support staff for handling questions & queries right on time. I have completed the Data Science program – it more than met my expectations and I feel I have really benefited from it. It was one of the best decisions that I made in my life. Data Science Program here bridges the gap between theory and practical which is very much required in the industry. I highly recommend this course with my full conviction & sincerity to everyone who wants to start a career in DATA SCIENCE technology.  Thank you very much Innomatics Team, for helping me start my Data Science Career.

      Data Science Trainee

      Durga Bhavani

      I am from Guntur Nalanda college ECE department. I searched almost all the best institutes in HYDERABAD for data science. I attend a workshop in innomatics training institute. They r teaching is completely industry-related. They also have an in-house development team. After the course, these will offer paid internship I am doing the coding. I made a ✔choice of choosing innomatics training institute. Thanks to management. If u r NON-IT background I recommend innomatics training institute to everyone.

      Data Science trainee

      Sai Charan

      I never regret joining this institute, first things first the atmosphere over the institute is pleasing which creates the study environment. Sessions in the classes are quite interesting and made me learn more. Yes, the projects given by the institute is more in number but this boosted me even I’m from the EE background now I can code and apply machine learning & AI skills. Especially I thank the faculty in the institute for helping me out with job opportunities. I highly recommended Innomatics training institute 🙂

      Data Scientist

      Vidyasagar Chakrapani

      Dear friends, initially I struggled a lot to find the best training centre for Data Science in Hyderabad. Finally, I ended up joining Innomatics institute after a lot of research. Initially, I was a little worried about whether I will be able to understand Data Science since I come from a non-programming and non-technical background. But to my surprise here the trainers and the mentors are so cooperative that I never felt that I come from a non-technical background. Here the quality is top-notch and the way treat every student is simply superb. They have assigned Eswar as a mentor to our group right from the beginning and he was always there for us to clarify the doubts and helped us with the projects. And from the past 3 weeks, they have been conducting interview preparation sessions in every afternoon for 3 hours. They conduct the hackathons, quizzes, assignments and mock interviews. Since they are also into development we are also having access to real-time projects. Thanks a lot to Innomatics training institute and I definitely recommend this to everyone.

      Data Science Trainee

      Frequently Asked Questions (FAQs)

      What is the Eligibility criteria to learn Data Science?

      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.

      Are there any Pre-requisites for 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.

      What will I learn in IBM certified Data Science?

      In Data Science technology, you will learn how to find valuable data, analyze and apply mathematical skills on bulk data processing, enterprise resource planning, and transaction processing to use in business for making great decisions, developing a product, forecasting, and building business strategies. 

      What is the average salary of a Data Scientist?

      The salary of a Data Scientists entirely depends on the skillset. As per the recent reports, on average a Data Scientists earn ₹24,00,000 per year.

      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.

      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

      Do you provide any placements if I learn Data Science course at Innomatics?

      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.

      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.

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